Acoustic Beamforming for Hearing Aids Using Multi Microphone Array by Designing Graphical User Interface

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1 MEE Acoustic Beamforming for Hearing Aids Using Multi Microphone Array by Designing Graphical User Interface Master s Thesis S S V SUMANTH KOTTA BULLI KOTESWARARAO KOMMINENI This thesis is presented as a part of Degree of Master of Science in Electrical Engineering with Emphasis on Signal Processing Blekinge Institute of Technology January-2012 Blekinge Institute of Technology School of Engineering Department of Electrical Engineering Supervisor : Dr. Benny Sällberg Examiner : Dr. Nedelko Grbic Blekinge Tekniska Högskola SE Karlskrona.

2 Contact Information: Author 1: S S V Sumanth Kotta ( ) ssko10@student.bth.se Author 2: Bulli Koteswararao Kommineni ( ) buko10@student.bth.se Supervisor: Dr. Benny Sällberg Department of Electrical Engineering School of Engineering, BTH Blekinge Institute of Technology, Sweden bsa@bth.se Examiner: Dr. Nedelko Grbic Department of Electrical Engineering School of Engineering, BTH Blekinge Institute of Technology, Sweden nedelko.grbic@bth.se ii

3 ABSTRACT Hearing impaired persons lose their ability to distinguish speech signal in ambient noise. Human hearing system is sensitive to interfering noise. Interfering noise decreases the quality and intelligibility of the speech signal which in turn makes speech communication default. To make the speech signal effective and useful for hearing impaired, they need to be enhanced from noisy speech signal. Speech enhancement is one of the most emerging and useful branch in signal processing, to reduce the noise and improves the perceptual quality and intelligibility of the speech signal. Several signal processing techniques has been widely used in hearing aids to enhance the speech signal from the noisy environment. Microphone array is one of the signal processing technique implemented in hearing aids to provide a better solution to the problem encountered by the hearing impaired person when listening to speech in the presence of background noise. Generalized Sidelobe Canceller (GSC) is a powerful technique to enhance the signal of interest which suppressing the interference signal and noise at the output of the array microphones. The main focus of the thesis is to implement a GSC using microphone array, the blocking matrix in the GSC is replaced with Elko s algorithm. Elko s algorithm is used to track and attenuate interference or background noise located in the back half plane of the array of microphones. The proposed system is implemented successfully and validated effectively. Clean speech signal is corrupted by various background noises respectively multi-talker babble noise, wind noise, car interior noise, destroyer engine room noise, tank noise, interference male and female voices at five different Signal-to-Noise Ratio (SNR) levels 0db, 5db, 10db, 15db and 20db. Different types of objective tests, such as SNR, Signal-to-Noise Ratio Improvement (SNRI), Perceptual Evaluation of Speech Quality (PESQ), Speech Distortion (SD) and Noise Distortion (ND) are performed on the test set. The platform is made in Matlab Graphical User Interface (GUI) and all the results have been shown by plots produced from Matlab code. iii

4 To Our Parents iv

5 Acknowledgment We owe our deepest gratitude to our supervisor, Benny Sällberg, for his encouragement and guidance. He provided us with all the advice and support for completing the thesis. His deep knowledge in the field allowed us to learn many things which are helpful to us during the thesis work. We would like to express our utmost gratitude to our Examiner Dr. Nedelko Grbić for providing us this opportunity to pursue Master Thesis. We would like to thank Dr. Gary W. Elko for giving his suggestions during thesis work. We would like to thank our parents and family for their support and encouragement for the completion of thesis. They helped us throughout our educational carrier and motivated us. They helped us both morally and financially. We would like to thank all of our friends who supported us during the thesis work. Lastly, we offer our regards to all of those who supported us in any respect during the completion of thesis. S S V Sumanth Kotta, Bulli Koteswararao Kommineni, Karlskrona, January, 2012, Sweden. v

6 Table of Contents ABSTRACT... III ACKNOWLEDGMENTS... V LIST OF FIGURES... VIII LIST OF TABLES... XI LIST OF ABBREVIATIONS... XI CHAPTER INTRODUCTION Objective of the Thesis Problem Statement Aim of the Thesis Work Overview of the Proposed System Outline of the Thesis:... 4 CHAPTER BACKGROUND Microphone Array in Hearing Aids Beamforming in Hearing Aids Noise Reduction in Hearing Aids Feedback Cancellation in Hearing Aids... 7 CHAPTER MICROPHONE ARRAY Basics of Microphone Microphone Array Microphone array structure and connections Physical Preliminaries Trigonometric Solution CHAPTER ELKO ALGORITHM Introduction Aim of Elko Algorithm Derivation of the Adaptive First-Order Array LMS Version of Back-to-Back Cardioid Microphone vi

7 CHAPTER BEAMFORMING AND GUI Basics structure of Beamforming Types of Beamformers Fixed Beamforming Adaptive Beamforming Acoustic Beamforming Generalized Sidelobe Canceller Elko Based Generalized Sidelobe Canceller Graphical User Interface CHAPTER EVALUATION Test Data Clean Speech Data Noise Data Objective Measures Signal to Noise ratio SNR Improvement Perceptual Evaluation of Speech Quality Measurement of Speech Distortion Measure of Noise Distortion Test Results with Various Noise Signals Evaluation of Babble Noise Evaluation of Car Interior Noise Evaluation of Tank Noise Evaluation of wind Noise Evaluation of Man voice as interference Noise Evaluation of Destroy Engine Noise Evaluation of Female voice as interference Noise CHAPTER SUMMARY AND FUTURE WORK Conclusion Future work BIBLIOGRAPHY vii

8 List of Figures Fig Basic overview of microphone array for recording the signals 01 Fig Basic overview of the speech enhancement system 02 Fig Block diagram of the proposed system 03 Fig Head simulator with the three element microphone array 06 Fig Electronic symbol of microphone 08 Fig Microphone constellation in an array 09 Fig Physical set up of the microphones 10 Fig Diagram showing the possible situation of microphone and source 12 Fig First-order sensor composed of two zero-orders and a delay 15 Fig Directional response of the array for 16 Fig Schematic implementation of an adaptive first-order differential microphone using the combination of forward and backward facing cardioids 17 Fig Directional response of the array for 18 Fig Directional response of the back-to-back cardioid microphone 19 Fig Directional response of the adaptive array for 20 Fig Block diagram of beamforming 22 Fig Signal model for microphone array and beamforming 22 Fig An Adaptive beamforming system 25 Fig Block diagram of generalized sidelobe canceller 27 Fig Detailed structure of generalized sidelobe canceller 28 Fig Structure of Proposed Elko Based Generalized Sidelobe Canceller Model 30 Fig GUI layout used for the design of the proposed model 33 Fig Power spectrum of babble noise 36 Fig Power spectrum of car interior noise 37 Fig Power spectrum of destroy engine noise 37 Fig Power spectrum of tank noise 38 Fig Power spectrum of wind noise 38 Fig Structure of perceptual evaluation of speech quality 40 Fig Graph represents the position of microphones, source and noise signal 42 Fig Graph represents a clean speech signal 42 viii

9 Fig Graph represents babble corrupted speech signal at 10dB, enhanced signal 43 Fig Graph represents the SNR value for babble noise 44 Fig Graph represents the PESQ value for babble noise 44 Fig Graph represents the SD value for babble noise at 10dB 45 Fig Graph represents the ND value for babble noise at 10dB 45 Fig GUI Layout with babble as input noise at 10dB of input SNR 46 Fig Graph represents car noise corrupted speech signal at 0dB, enhanced signal 47 Fig Graph represents the SNR value for car noise 47 Fig Graph shows the PESQ value for car noise 48 Fig Graph represents the SD value for car noise at 0dB 48 Fig Graph represents the ND value for car noise at 0dB 49 Fig GUI Layout with car noise as input noise at 0dB of input SNR 50 Fig Graph represents tank noise corrupted speech signal at 5dB, enhanced signal 50 Fig Graph represents the SNR value for tank noise 51 Fig Graphs represents the PESQ value for tank noise 51 Fig Graph represents the SD value for tank noise at 5dB 52 Fig Graph represents the ND value for tank noise at 5dB 52 Fig GUI Layout with car noise as input noise at 5dB of input SNR 53 Fig Graph represents wind noise corrupted speech signal at 0dB, enhanced signal 54 Fig Graph represents the SNR value for wind noise 54 Fig Graph shows the PESQ value for wind noise 55 Fig Graph represents the SD value for wind noise at 0dB 55 Fig Graph represents the ND value for wind noise at 0dB 56 Fig GUI Layout with wind noise as input noise at 0dB of input SNR 57 Fig Graph represents man noise corrupted speech signal at 5dB, enhanced signal 57 Fig Graph shows the SNR value for man voice as interference noise 58 Fig Graph shows the PESQ value for man voice as interference noise 58 Fig Graph shows the SD value for man voice as interference noise at 5dB 59 Fig Graph shows the ND value for man voice as interference noise at 5dB 59 Fig GUI Layout with man noise as input noise at 5dB of input SNR 60 Fig Graph represents engine noise corrupted speech signal at 0dB, enhanced signal 61 Fig Graph shows the SNR value for destroy engine noise 61 Fig Graph shows the PESQ value for destroy engine noise 62 ix

10 Fig Graph shows the SD value for destroy engine noise at 0dB 62 Fig Graph shows the ND value for destroy engine noise at 0dB 63 Fig GUI Layout with destroy engine noise as input noise at 0dB of input SNR 64 Fig Graph represents female noise corrupted signal at 10dB, enhanced signal 64 Fig Graph shows the SNR value for female voice as interference noise 65 Fig Graph shows the PESQ value for female voice as interference noise 65 Fig Graph shows the SD value for female voice as interference noise at 10dB 66 Fig Graph shows the ND value for female voice as interference noise at 10dB 66 Fig GUI Layout with female noise as input noise at 10dB of input SNR 67 x

11 List of Tables Table 5.1 Basic components used in GUI 34 Table 6.1 Type of male and female sentences used for evaluation 35 Table 6.2 Represents the SNRI speech and noise distortion for babble noise 46 Table 6.3 Represents the SNRI speech and noise distortion for car interior noise 49 Table 6.4 Represents the SNRI speech and noise distortion for tank noise 53 Table 6.5 Represents the SNRI speech and noise distortion for wind noise 56 Table 6.6 Represents the SNRI speech and noise distortion for man interference noise 60 Table 6.7 Represents the SNRI speech and noise distortion for destroy engine noise 63 Table 6.8 Represents the SNRI speech and noise distortion for female interference noise 67 List of Abbreviations GSC SNR SNRI PESQ ANC ADMA NLMS GUI VoIP DAT SD ND db Generalized Sidelobe Canceller Signal-to-Noise Ratio Signal-to-Noise Ratio Improvement Perceptual Evaluation of Speech Quality Adaptive Noise Canceller Adaptive Differential Microphone Array Normalized Least Mean Square Graphical User Interface Voice over Internet Protocol Digital Audio Tape Speech Distortion Noise Distortion Decibels xi

12 xii

13 Chapter-1 Chapter 1 Introduction Hearing impairments affect 10% of the world population. Surveys in Sweden, has estimated that about 1.2 million people aged 18 years and older have mild hearing loss, 495,000 have moderate hearing loss, 120,000 have sever hearing loss [1]. About 367,000 Swedes, with hearing damage uses hearing aids. For the people suffering with hearing impairment, hearing aids are used to amplify the acoustic signal that enables an individual with hearing loss to understand the acoustic signal in an efficient manner. Most of the hearing impaired people with hearing aids do not satisfy with the hearing aids because of background noise. The poor performance of the conventional hearing aids in background noise motivated the use of microphone array to create directional sensitive hearing aids that amplify the signal arriving in a particular direction. Microphone array is used to improve desired speech signal when the interference arises from different directions. The microphone array is considered as a preprocessor, followed by conventional hearing aid processing [2]. It is used to improve the SNR value and speech intelligibility. Microphone array is used in various applications such as audio, teleconference, voice recognition applications [3]. Fig represents the basic overview of microphone array for recording signals. Fig Basic overview of microphone array for recording the signals 1

14 Chapter-1 The speech signal recorded by the microphone array is of poor quality, because of various interfering noises recorded and the distance between the speaker and microphones. Further the output of the microphone array should be processed to enhance the pure speech signal. Speech enhancement is one of the key technology, used to enhance the speech signal and to suppress the unwanted noise while maintaining the quality of the speech signal. Fig represents the basic overview of speech enhancement system. Fig Basic overview of the speech enhancement system 1.1 Objective of the Thesis The objective of the thesis is to improve the perceptual aspects such as quality and intelligibility of the degraded signal in hearing aids by the use of microphone array. This project will analyze the achievable performance of speech enhancement, for a microphone with 1 cm aperture. The speech enhancement paradigm that will be used exclusively throughout the project is GSC and Elko algorithm. 1.2 Problem Statement Microphone array as a preprocessor to hearing aids, the problem is to design a system to enhance desired speech signal form the interfering noise signals. The interfering noise signal may be of random, wind, background sounds form offices, car or babble noise. The noise signal may affect the original signal in an additive, multiplicative or convolution manner. This thesis concern with Firstly how to design and implement a microphone array that suits the system and to determine the angle of arrival of the speech and noise signal to the microphones. Secondly how to suppress the noise by implementing a new way of speech enhancement method which uses GSC in which blocking matrix is replaced with the Elko algorithm and to develop a GUI layout which suits the proposed method. 2

15 Chapter Aim of the Thesis Work The main aim of developing this thesis is to overcome the problem of interfering noise signals in hearing aids, to improve the quality and intelligibility of the speech signal by using microphone array as a preprocessor to the hearing aids. This work is divided into four major parts Design of the microphone array that suits the system and determining the angle of arrival of the speech and noise signal to the microphones. Implementing the Elko algorithm and GSC. The blocking matrix in the GSC is replaced with Elko algorithm. The final objective is to analyze the performance of the proposed system with different interfering noises and to perform the objective tests on the system. Implementing the proposed system in Matlab GUI. 1.4 Overview of the Proposed System The block diagram of the proposed system used in our thesis is as shown in Fig Fig Block diagram of the proposed system The proposed system consists of a speech signal and an interfering noise signal. It consists of an array of microphones placed in an arc position as shown in Fig

16 Chapter-1 Speech and noise signals are individually passed through an array of microphones, the output is the delayed version of the original signals. Elko algorithm is applied to the signals obtained from the microphone array. Elko algorithm is a linear system which is used to improve the SNR [4]. Both the speech and noise signal obtained from the Elko algorithm is added together and is applied to the adaptive beamformer such as GSC [5]. GSC is most popular adaptive beamformer which is used to enhance the speech signal. For the effective performance of the system the blocking matrix in GSC is replaced with Elko algorithm. The modified GSC is used to suppress the noise signal form the noise contaminated speech signal. The output from the modified GSC is a speech signal that is presented to the listener. The detailed description of the Elko algorithm and GSC is explained in the further chapters. This system is implemented in the Matlab GUI. To validate the system it undergoes various objective measures such as measure of SNR, SNRI, PESQ, SD and ND. 1.5 Outline of the Thesis: This document is a report on thesis presented as a part of Degree of Master of Science in Electrical Engineering with Emphasis on Signal Processing. It is made up of six chapters and is organized as follows Chapter 1 introduces the subject that are handled in this thesis. It comprises of sub-chapters that deals with objective of the thesis, problem statement, aim of the thesis, overview of the proposed system. Chapter 2 provides the background information of hearing aids and various enhancement technologies used in hearing aids. Chapter 3 provides the information about the microphones, arrangement of microphones and the angle of arrival of the speech signal to the microphones. Chapter 4 provides the information of the elko algorithm. It comprises of sub-chapters that deal with first order differential microphones, derivation of the adaptive first order and second order arrays, LMS version of the differential microphone. Chapter 5 provides the information about the beamforming. It comprises of sub-chapters that deals with different types of beamforming, generalized sidelobe canceller, the model proposed in the thesis and information about GUI. Chapter 6 provides the information about the testing and presentation of the results. It gives the description about the SNR, SNRI, PESQ, SD and ND values. Chapter 7 gives the information about final conclusion and recommendation for future work. Some useful references used in thesis. 4

17 Chapter-2 Chapter 2 Background It is difficult for the normal hearing person to understand the speech signal with background noise. The problem is very severe for the person suffering from hearing impairment. To enhance the speech signal form the background noise many algorithms have developed. To increase the quality and intelligibility of the speech signal in the hearing aids several signal processing techniques. Signal processing has wide range of applications in hearing aids. Since more than 25 years onwards research is going on hearing aids as an application in signal processing. In the past decades, the development of hearing aids was increased with the development of sophisticated signal processing algorithms such as beamforming, noise reduction techniques and feedback cancellation. Other signal processing technologies such as adaptive filtering, echo cancellation, array processing has been widely used in hearing aids. 2.1 Microphone Array in Hearing Aids Microphone array consists of a multiple microphones arranged in spatial domain. Microphone array hearing aids provides a better solution for the hearing impaired person when listening to speech in the presence of background noise. The aim of the microphone array hearing aids is to increase the speech to interference ratio when the interference is arrived from different directions rather than the desired speech signal. Functionally, a microphone array hearing aid consists of three components: the microphone array, processing unit and receiver all these units are interconnected. Microphone array acts as a preprocessor to the system followed by the speech enhancement system [2]. A microphone array is capable of maintaining high signal to noise ratio in a noisy environment. The advantage of microphone array is their ability to exploit, reduction of noise based on the knowledge of the position of speech signal. Fig represents an example of a head simulator using a hearing aid with element microphone array [13]. 5

18 Chapter-2 Fig Head simulator with the three element microphone array 2.2 Beamforming in Hearing Aids Beamforming is a signal processing technique used for signal transmission or reception. Beamforming technology is used to create a constructive interference in a particular direction and destructive interference in other directions. Hearing impaired person facing problem with different directions of noise source. Beamforming is used to create a null in the direction of the noise source and allows only signal coming from a particular direction. Beamforming is performed in hearing aids to enhance the SNR and to increase the speech intelligibility in hearing aids [6]. Several beamforming techniques have been developed for hearing aids to enhance the desired speech signal from various types of noises. Fixed beamforming is used to obtain the beam in a particular direction and don t change its direction as that the incident source direction changes. To steer the directional pattern to the location of the desired source and to maximize the attenuation of noise source an adaptive beamforming is used in hearing aids. 2.3 Noise Reduction in Hearing Aids Noise is an unwanted signal, plays a major role in many applications. Noise exists in several forms and creates problems to various devices such as telecommunications, radar, sonar, medical applications and so on. Hearing impaired people faces a problem to understand the speech signal in presence of noise because SNR is an important factor for hearing impairment. A person with normal hearing can understand the speech signal with SNR as low as -5db. Hearing impaired person needs at least +5db SNR to understand the speech signal. To enhance the speech signal in hearing aids several noise reduction techniques have been developed. A reference signal is available and is used to reduce the noise. 6

19 Chapter-2 Reliable and intelligent signal detection plays an important role for the success of noise reduction. Hearing aids are sensitive to the presence of noise. Amplitude modulation is the key technology used to separate speech from noise signal. Amplitude modulation works on the principle that desired speech signal has a harmonic structure and the amplitude of this harmonic component will change over the time and produces amplitude modulation. The amplitude signal of the speech and noise may vary. Speech signal has higher amplitude signal compared to that of stationary and pseudo stationary noises. Pseudo noise has very low amplitude modulation. The amplitude modulation of the environmental noises such as babble, traffic noise has higher amplitude than the stationary noise and lower amplitude compare to the speech signal. Amplitude modulation alone does not provide the reliable signal detection because the signal with higher modulation need not be the desired signal. With reliable signal detection, it is not possible to enhance the speech signal while attenuating the noise. Intelligent signal detection and noise reduction have been improved by using temporal and timing information about the signal and noise in combination with amplitude modulation [7]. 2.4 Feedback Cancellation in Hearing Aids Feed back cancellation is used to suppress the feedback signal for which the hearing aid gain is larger than the feedback part which is the attenuation between the hearing aid output and its microphone input. Feedback compensation approach consists of a linear adaptive filter subtracts the feedback signal [6]. The adaption control of the adaptive filter is the challenging for the feedback cancellation. The typical correlation between the input signal and feedback signal causes the signal distortion at the hearing aid output. 7

20 Chapter-3 Chapter 3 Microphone Array 3.1 Basics of Microphone Microphone is a device used to convert one form of energy to another form. Microphone is a transducer, which converts a non electrical signal into electrical signal. The input to the microphone is sound information exists as a pattern of air pressure. Sound information is converted into patterns of electric current by the microphone. Microphones are used in various applications such as hearing aids, telephones, tape recorders, radio, television and non acoustic purpose such as ultrasonic checking. The electronic symbol of microphone is as shown in Fig Pattern of Air Pressure Pattern of electric current Fig Electronic symbol for microphone 3.2 Microphone Array Microphone array consists of multiple microphones arranged in space with a single directional input device whose outputs are processed individually and added to produce the desired output. Microphone array improves the performance of picking up distance sound compared to that of directional microphone. In applications where a speech signal is monitored by the microphone, a better performance can be achieved by using an array of microphones. The microphone array processing technique can be effectively used for the reduction of noise, it can be used to improve the signal-to-noise ratio of acquired sound pick up the desired speech with a flat spectrum response at arbitrary speaker position, and detect the speech period in noisy speech signal [8]. The outputs from the microphone array are further processed in order to achieve the speech enhancement. Microphone array has been used in wide different fields such as speech acquisition in hand-free communication, audio, teleconference and hearing aid applications [9]. The microphone array processing is well tested and well understood to enhance distance noisy 8

21 Chapter-3 target signal. The main aim of microphone array is to improve the quality of the input signal, to reduce the effect of typical recording problem. 3.3 Microphone array structure and connections Fig Microphone constellation in an array Fig shows 8-element semi circle shaped microphone array with the sound source in located in the far field. The microphone array consists of 8-elements and the microphones are placed in a semi circle shape with a distance between the microphones. The distance between the source and microphone is greater than that of the distance between the microphones, indicates that the source is located in the far field. The sound signals coming from the source are assumed to be parallel to each other. The sound signals from the source arrives the microphone at different time instants because the distance traveled by the source to the microphones may vary. Each of the microphones will receive the input signal with some delay due to the distance between the microphone and the source signal. Let us consider that the distance between the microphones as. The distance travelled by the source signal to the microphone array is considered as where is the angle of arrival of the source signal to the microphone. The time delay to the microphone is considered as and is given as 9

22 Chapter-3 where is the speed of the sound. The input to the microphone is given as The phase shift of the incoming signal is given as From equation 2.3 substituting the value of in equation 2.3 then By considering equation 3.4 can be written as In the similar manner a noise signal is passed through the microphone array [10]. The total signal received by the microphone is the combination of the source signal and the noise signal given as, The output from the microphone array is given to the Elko algorithm further to beamforming to enhance the speech signal from the noisy speech signal. 3.4 Physical Preliminaries To determine the angle of arrival of the source signal to the microphones let us consider two microphones placed as shown in the Fig Fig Physical set of the microphones 10

23 Chapter-3 Fig represents the physical set of two microphones and. The source signal is represented by S and is located in the front of the microphones. To determine the angle of arrival of the source signal to microphone, it is needed to fix the origin for the microphones. The midpoint between the microphones is considered as the origin. Considering the orthogonal line to the microphone axis at the origin (OX). The angle is defined as the separation between the line OX and OS. The angle determines the angle of arrival of the source signal to the microphone array [11]. From the Fig it is observed that the source signal is closer to the microphone compared to that of the microphone. The sound travelling from the source signal reaches to the microphone and then to. The time delay between the two microphones is denoted as. The source signal received by the microphone is represented as and the source signal received by the microphone is represented as. 3.5 Trigonometric Solution To determine the angle of arrival between the microphones and the source signal consider a point S with coordinates x and y these are assumed to be the variables. The coordinates of the microphones and are considered as and respectively. The distance between the microphones is considered as cm. The midpoint between the microphones and is taken as origin. The target is to determine the angle of angle of arrival of sound signal from the source signal to microphone. A signal coming from the source reaches the microphone in time t. In the same moment, the signal travels from the source to the microphone [11]. Let be the number of samples between the two signals and is expressed a 11

24 Chapter-3 Fig Diagram showing the possible situation of microphone and source Fig shows the possible arrangement of the microphone and source signal. Let be the midpoint between the microphones and is expressed as expressed as The slope of the line joining the midpoint of the microphones and the source signal is The angle made by the line joining the midpoint of the microphones and the source signal is obtained by taking arctangent of the slope of the line joining the point and is expressed as 12

25 Chapter-3 The angle is the angle made by the line joining the line between the midpoint of the microphone and the source signal to the X-axis. The angle is the angle made by the Y-axis and the line joining the point and is given as follows If then and if then In this way we can determine the angle of arrival of the source signal to the microphone. This procedure is applied to all the microphones to determine the angle of the microphone. Similar this procedure is applied to the noise signal as the input to the microphones. 13

26 Chapter-4 Chapter 4 Elko Algorithm 4.1 Introduction Communication devices are widely used in many environments, the acoustic pick up of the electro acoustic transducer requires a combination of transducer and signal processing unit. During communication, the transmitted signal is effected by the background noise due to this the quality of the signal is degraded. The presence of background noise causes acoustic signal transmission to ubiquitous problems. To overcome the problem of background noise, convectional microphones are used to pick up the signal in a particular direction, such that the background noise can be eliminated. Utilization of the conventional directional microphones limits the solution to this problem because the noise doesn t have particular direction of arrival. A better solution can be obtained by taking the advantage of ANC capabilities of the differential microphone array in combination of digital signal processing [12]. An adaptive microphone system is to be designed such that it adjusts its directive pattern to maximize the SNR. ADMA is used to suppress the background noise and to maximize the SNR value. ADMAs are able to adaptively track and attenuate possibly moving noise sources that are located in the back half plane of the differential array. 4.2 Aim of Elko Algorithm Elko has proposed a solution for an adaptive directional microphone. Elko algorithm covers the design and implementation of a novel adaptive first order differential microphone that minimizes the microphone output power. By attenuating sound from one direction it can improve the SNR in acoustic field. An adaptive differential microphone has been implemented by combining two omni directional elements to from back-to-back cardioid directional microphone. The microphone signals and a delayed version of the microphones signals are combined such that a null is placed in one direction, any first order array can be realized. The adaption process works under the constrain so that a single null is placed in the rear half plane. 14

27 Chapter Derivation of the Adaptive First-Order Array When a plane-wave with spectrum and wave vector incident on a twoelement microphone array as shown in Fig the sound waves reaches one microphone before the other. The time difference depends on the distance between the microphone and the angle of incident sound wave, where is the speed of the sound. The output can be obtained by taking the difference between the delayed microphone signal and signal from the other microphone, by changing the time delay it is possible to steer the null. The output signal can be written as T Fig First-order sensor composed of two zero-orders and a delay Transforming the equation into frequency domain we get, The magnitude plot of the equation (4.13) is as shown in Fig The plots represent the directional response of the array for three different values of. The time delay is changed between 0 to, so that the null is steered between and. 15

28 Chapter (a) (b) (c) Fig Directional response of the array for The magnitude of the frequency and angular dependent response of the first-order differential microphone from a single point source located in the far field is given as If we assume a small element spacing and inner element delay the above equation can be written as The first-order differential array has a monopole term and first order dipole term. It is observer that the first-order array has first-order differentiator frequency dependency which can be compensated by a first-order low pass filter [14]. The term in the brackets of the above equation has a directional response. The adaptive algorithm minimizes the array output with the appropriate combination of omnidirectional and dipole sensors such that the mean square output would be minimized. The dipole directivity pattern can be realized by subtracting two closely-spaced omnidirectional microphones. A low-pass filter is implemented in the dipole path, the filter is used for inter channel phase shift. Due to this the adaptive algorithm can steer a null in noise source direction. 16

29 Chapter-4 By setting the sampling period equal to and use a fixed delay of one sample, we get a cardioid directional pattern. A directional microphone array with two microphones generates forward and backward cardioid signals. An adaption factor is applied to the backward cardioid and signal obtained is subtracted from the forward cardioid signal to generate output signal [4]. The output signal is applied to the low pass filter which is used to compensate the differential response of the differential microphone. T T Fig Schematic implementation of an adaptive first-order differential microphone using the combination of forward and backward facing cardioids The output of back-to-back cardioid microphone is obtained by setting the sampling period equal to. With sampling period, the expression for the forward facing cardioid and backward facing cardioid is as given The output can be given as 17

30 Chapter-4 Transforming the above equations into frequency domain we get, Normalizing the output signal by the input spectrum results in The time delay T is fixed instead the value of β is changed between 0 and 1. The magnitude plot of the Equation (4.11) is as shown Fig the direction pattern is obtained for different values of β. By changing the value of β between 0 and 1 it is possible to steer between and (a) (b) (c) Fig Directional response of the array for. 5. The direction response of the back-to-back cardioid microphone is as shown in Fig

31 Chapter Forward Cardioid Backward Cardioid Fig Directional response of the back-to-back cardioid microphone 4.4 LMS Version of Back-to-Back Cardioid Microphone Least mean square algorithm is an adaptive algorithm, which uses a gradient method of steepest decent. LMS incorporates an iterative procedure that makes the successive correction to the weight vector in the direction of negative gradient vector which leads to minimum mean square error. LMS algorithm is commonly used algorithm for its simplicity and does not require correction function calculation. LMS algorithm is implemented to backto-back cardioid adaptive first-order differential array [15]. The output of the back-to-back cardioid microphone is given as Squaring the above equation on both sides we get, The minimum error is determined by using steepest descent algorithm by stepping in the direction opposite to gradient of the surface with respect to the weight parameter. The steepest descent update equation is given as, 19

32 Chapter-4 where, is the update step-size and the derivative gives the gradient of error surface with respect to. LMS algorithm minimizes the mean of i.e. instantaneous estimate of the gradient but not the expectation value that is [15]. Taking the derivative of we get, LMS update equation is given as The LMS algorithm is modified by normalizing the update size. Therefore the LMS version with normalized is given as The bracket indicates the time average. The directional pattern for the adaptive array for is shown in the Fig Fig Directional response of the adaptive array for 20

33 Chapter-5 Chapter 5 Beamforming and GUI In speech communication system such as hearing aids, wireless communication, radar s and sonar s the recorded speech is corrupted by various background noises. The reason behind this is that the recording microphone array is located at a certain distance which causes the microphone to record the background noise. The background noise arises from audio equipment and other speakers present. The impact of these background noises on the speech quality depends on the acoustic environment. The intelligibility of the recorded speech signal is degraded by the background noise. The signal recorded by the microphone should be enhanced to improve the quality and the intelligibility of the speech signal. Beamforming is one the simplest method used for distinguishing signals based on the physical location, it is used with the combination of an array of sensors to provide versatile form of spatial filtering. The sensors are used to collect the spatial samples of propagating wave, which are further processed by the beamforming. The term beamforming is derived from the spatial filters, which are used to design the beam in order to receive a signal from the direction of interest and attenuate the signal from other directions. The objective of the beamforming is to estimate the signal arriving from the desired direction in the presence of noise and interference signal. The desired signal and interference signal are placed at different spatial directions [27]. A beamformer performs spatial filtering to separate signals that have overlapping frequency content but originate from different spatial locations. Beamforming is applicable to either radiation or reception of energy. Beamforming is used to extract the signal contaminated by interference signal based on directivity. The signal extraction is performed by processing the signals obtained from multiple sensors such as microphones, antenna and sonar located at different positions in space. Beamforming can be used in both transmitter and receiver side. During the transmission, the beamformer controls the phase and amplitude of the signal at each transmitter, in order to obtain the pattern of the constructive and destructive interference [16]. In the receiving side, information from different sensors are combined together to obtain a desired radiation pattern. Beamforming is used in microphone array for speech enhancement. 21

34 Chapter-5 Beamforming can be considered as multidimensional signal processing in space and time. The general block diagram of beamforming in which sensors are placed at different locations is as shown in Fig Fig Block diagram of beamformer The signals picked by the sensors at particular instant of time are considered as a snapshot. The beamforming combines the signals arriving the sensors in a particular are amplified, while signals from the signals from other direction are attenuated. 5.1 Basics structure of Beamforming Let us assume microphone array and beamformer. Consider the desired signal is received by the Omni-directional microphone at a time instant as shown in Fig Microphone Array Fig Signal model for microphone array and beamforming 22

35 Chapter-5 Let us consider the source signal as, noise signal as and time delay between the source and microphones as. Let as assume that the microphone output as is the attenuated and delayed version of the source and noise given by where the source signal and noise signal are considered as statistically independent. The frequency domain representation of the microphone output is given as The vector representation of arrayed microphone is give by The data vector is given as: and The and is given as represents array steering vector and depends on microphone and source location where is the gain scaling of microphone and is given as and time delay is given as where represents the distance between the microphone and reference microphone respectively and represents the speed of sound. The source signal is 23

36 Chapter-5 retrieved by processing with frequency domain filter weights. The weight vector is given as: The output of the beamformer us the sum of weighted microphone outputs and is given as where (.) H represents hermitian transpose and is represented in vector form as In this a microphone array is used in combination with beamformer to enhance the speech signal from a noise contaminated speech signal [17]. 5.2 Types of Beamformers Beamforming technique is further divided into two types I. Fixed beamforming II. Adaptive beamforming Fixed Beamforming Fixed beamforming uses a set of weights and time delays to combine the signals from the sensors in an array. This type of beamforming optimizes the microphone in a particular direction and does not change the direction as the incident source signal changes. The beam is optimized for the direction of desired source while suppressing the sound from other directions as much as possible. Thus the direction response of the array is fixed to particular angle of elevation. If the target source is non-stationary, the signal enhancement performance is reduced as the source moved away from the steering direction Adaptive Beamforming A beamforming which adaptively forms its directive patterns is called an adaptive beamforming. Adaptive beamforming is a powerful technique to enhance a signal of interest while suppressing the interference signal and noise at the output of the array sensor. Adaptive beamforming alters the direction pattern in according to the changes in the acoustic environment, thus provides a better performance than fixed beamforming. Adaptive 24

37 Chapter-5 beamforming is more sensitive than fixed beamforming to errors such as sensors mismatch, mis-steering and to correlated reflections [18]. Let us consider microphones the general adaptive beamforming is as shown in Fig Fig An adaptive beamforming system Adaptive beamforming is used to create multiple beams towards the signal of interest and suppress the interfering signals from all the other directions. The input signal received by the microphones is multiplied with a coefficient weight vector to adjust the phase and amplitude of the incoming signal. The multiplied signals are summed up to produce a resulting output array. An adaptive algorithm is applied to minimize the error between the desired signal and the output array. The output of the beamformer at an instant of time n is given by the equation where and. The weights are used to adjust the amplitude so that when added together produce a desired beam of interest [19]. Adaptive beamformers has higher capability of unknown directional noise reduction compared to that of fixed beamforming and potentially provides better performance that fixed beamformers. Adaptive beamformers are sensitive to steering errors and might suffer from 25

38 Chapter-5 signal leakage and degradation of the desired signal. Due to this the conventional adaptive beamforming has not gained a wide spread of acceptance for speech applications. Robust modifications to avoid signal leakage and cancellation have been an important matter of interest in microphone applications. GSC is an adaptive beamforming solution that has been proposed for microphone array processing Acoustic Beamforming Acoustic beamforming is a technique where the microphone array is placed in the far field. As a rule of thumb, the far field is defined as being further away from the source than the array dimensions or diameter. The area between near field and far field remains a grey zone. In the near field, sound waves behave like circular or spherical waves whereas, in the far field, they become planar waves. Acoustic beamforming modifies the propagation of sound by introducing spatially dependent delay into a wave front. This focuses incoming sound from a single source or direction into a small volume of space so that it can be detected by a single transducer. Acoustic beamforming can efficiently enhance the speech of interest while suppressing interference, background noise. It allows people to move freely around without wearing or holding a microphone. Acoustic beamforming provides the option to enhance the signal from the specific individual and allows background noise (other speech, motors, movement. etc) to take place. Acoustic beamforming is sometimes called sum and delay since it considers the relative delay of sound wave reaching different microphone positions. Acoustic beamforming requires that all data is measured simultaneously [28]. The main advantage of Acoustic beamforming is good spatial resolution and main disadvantage is it does not perform well in the low frequency range. To rectify this disadvantage we choose high frequency range that is higher than 8000 Hz [28]. 5.3 Generalized Sidelobe Canceller Generalized sidelobe canceller is a most common and successful approach used widely in microphone array applications. GSC is used to reduce the interference noise from non target location in array beamforming [5]. It can be used as adaptive noise canceller in array processing. The structure is used with arrays which have been time delay steered such that the desired signal of interest appears in phase at the steered output. GSC is very susceptible to the burst of interference noise. The Block diagram of GSC is as shown in Fig

39 Chapter-5 Non Adaptive Filter Blocking Matrix Adaptive Filter Fig Block diagram of Generalized Sidelobe Canceller The structure of GSC consists of an adaptive filter and a non adaptive filter. The non adaptive filter is steered in the direction of the input signal. The non adaptive part of the GSC consists of a fixed beamformer such as delay-and-sum beamformer. The adaptive part of the GSC is the cascade combination of the blocking matrix and an adaptive filter. The adaptive part is used to estimate the non-desired components through the blocking matrix that blocks the input signal and allows all the other signals to pass through it. The adaptive filter is used to match the interference in the adaptive branch to as close as possible to interference in the non adaptive branch. The reduction of the noise is performed by a simple unconstrained NLMS algorithm [20]. Fig depicts one simple realization of the GSC A signal flow diagram of the GSC is as shown in Figure The input signal is applied to as array of microphones that are used to steer towards the desired focal point with some time delay. The upper part of the GSC is a delay-and-sum beamformer. A delaysum-beamformer is used to delay the signal received at each microphone and sum them together. The lower part of the GSC consists of a blocking matrix used for processing the signals from the microphone array in order to estimate the noise reference signal from the array of the microphones. A delay of samples is applied to the delay-and-sum beamformer to make the signal processing delay encountered by the adaptive filtering in the lower part of the GSC. Let us assume that the system consists of microphones. The output of the delayand-sum-beamformer is given as 27

40 Chapter-5 NLMS Blocking Matrix NLMS NLMS NLMS Fig Detailed structure of Generalized Sidelobe Canceller In this case the blocking stage is achieved by simple subtracting pair of sensors. Then the output of the blocking matrix is where is a blocking matrix. The output of the adaptive path can be written in terms of and adaptive filter as The total output of the GSC beamforming is given as 28

41 Chapter-5 where the vector of the adaptive filter is weights for each blocking matrix and is the blocking matrix output. The filter weights of the NLMS algorithm are updated using where is given by The value of is given as for the stability of the system the value of the should be very small. The adaptive path of the GSC is used to reduce the coherent noise and it has a poor performance in terms of non-coherent noise. For this reason GSC is used for the rejection of unknown directional interference [21]. In real world applications maladjustment in the microphone position, assumed source position and characteristics of different microphones causes signal leakage in the blocking matrix output which results in target signal cancellation and further reduces the SNR of the system. To decrease the signal leakage in the blocking matrix the GSC blocking matrix is replaced with an Elko algorithm. The structured of the modified GSC is as shown in Fig Elko Based Generalized Sidelobe Canceller Fig represents the proposed Elko based GSC. The Proposed system is a combination of a microphone array, Elko algorithm and an adaptive part of the GSC. The microphones are placed in an arc shape. In our proposed model we are using 8 microphones. A sound wave is passed through the microphone array. The input sound signal reaches the microphones with some time delay because the source signal is placed at a distance from the microphones which indicates that the microphones are located in the far field. The angle of arrival of the speech signal to the microphones is calculated as explained in chapter-3. The output signals from the microphone are given to the elko algorithm. The elko algorithm is applied by considering the output signals from the pair of microphones. The eight microphone used are considered as five pairs of microphones as shown in Fig and elko algorithm is applied on the pairs of microphones. The description of the elko algorithm is explained in chapter-4, the elko algorithm used here is as shown in Fig A noise signal is applied in the same procedure as that of that of the speech signal. The output speech and 29

42 Chapter-5 noise signals from the pairs of microphones are added together. The output signals after adding both the noise and speech signal are named as,,, and. The microphones output which are straight forward to the source signal are and, the output from these pair of microphone is and is considered as the output of fixed path in the GSC or as a main lobe. All the other elko outputs i.e., and are considered as the output from the blocking matrix of the GSC or as the sidelobes and are applied to the adaptive path of the GSC. The adaptive part of the GSC consists of an unconstrained NLMS algorithm. Fig Structure of Proposed Elko Based Generalized Sidelobe Canceller Model Let us assume that the signal arriving the and as and and the angle of arrival of the speech signal is considered as and. The speech signal from the is multiplied with forward cardioid and the signal from the is multiplied with backward cardioid is given as 30

43 Chapter-5 where is the distance between the microphones and is given as which is equivalent to 1cm distance,, and is the speed of sound. Elko algorithm is applied on the signals which are obtained by multiplying with forward and backward cardioid. In the elko algorithm, initially a unit delay is applied to the signals the delayed signals is considered as and. From these delayed signals the forward and backward cardioids are obtained as The output from the elko algorithm for speech as the input signal is given as where is a constant. Similarly noise signal is passed through the microphones and the output of the elko algorithm with noise as input is given as. The output of the elko algorithm for and with speech and noise as input is given as In the similar manner elko algorithm is applied to all the other microphone pairs and the outputs are named as and. To enhance the speech signal the output of the elko algorithm is applied to the adaptive part of the GSC which consist of an NLMS algorithm. For the NLMS algorithm is considered as the reference signal. Let us consider that all the other elko are kept in a vector form as 31

44 Chapter-5 The output vector of the elko algorithm is further applied to adaptive filter. The output of the adaptive algorithm is given as where is the number of microphones and represents the vector of the output elko vector. The total output of the proposed system is given as where is the weight vector of the adaptive filter for the elko vector. These filter weights are updated by the NLMS algorithm. Weight update equation for the NLMS algorithm is given as where is given by The value of is given as for the stability of the system the value of the should be very small. In this way an elko based GSC is implemented to enhance the speech signal from the noisy speech signal. 32

45 Chapter Graphical User Interface MATLAB code is performed by command-line-operation which is a bit difficult to understand the program during the execution. Most of the people interested to perform the task simply by hiding the unnecessary clutters and technicality that lies in the program. A user friendly interface is need for simplification of entry point of the program and encapsulation of its functional behavior. A graphical front-end such as GUI is used in MATLAB to perform the task simply by hiding unnecessary clutters. GUI is used for the pictorial representation of the program. GUI uses graphics and text input to make a familiar environment to the user for the execution of the program. GUI based programs must be prepared for mouse clicks. Each control in the GUI has user-written routine know as call back, used to call back MATLAB to ask it to do things. The execution of the call back is triggered by the user action such as clicking a mouse button, selecting a menu item or pressing the screen button etc. GUI then responds to these events and this type of programming is called as event-driven programming. In event-driven programming call back execution is asynchronous, because it is triggered by the event external to the software [22]. GUI enables the user, to analyze the performance of the system using SNR values, graphical representation of input signal, output signal, speech and noise distortion. The layout of the GUI designed for evaluation of the proposed system is as shown in the Fig Fig GUI layout used for the design of the proposed model 33

46 Chapter-5 Fig represents the layout of the GUI designed for our system. Various components used for the design of the GUI are push button, edit box, popup menu and axes. The brief description of the components used in the GUI is explained in Table TABLE 5.1 BASIC COMPONENTS USED IN THE GUI Elements Push button Edit box Popup menu Axes Description It is created by uicontrol call back. It triggers a call back when with clicked with mouse. It created by uicontrol call back. It is used to display a string and allows the user to modify the information. It triggers a call back when the user press the enter key. It is created by uicontrol. It is used to display a series of text strings in response to a mouse click. Creates a new set of axes which is used to display the data on. Nerves triggers a call back Fig represents the GUI layout designed for our proposed model. In the layout the buttons start, clear and close buttons are push buttons. Input noise signal, output signal and input SNR are made up of popup menu which are used to select one value from a list of values. Input signal, output signal, SD and ND are made of axes component which displays the information. Elko SNR, output SNR, SD and ND are created by edit box in which the information is displayed. To run the GUI designed for your model, select the type of the input noise given to the system, output signal from the system, input SNR value, enter the value of order and step size. By triggering the start button the call back will execute the corresponding call back program and corresponding results are displayed. To clear the previous execution results, clear button should be triggered. To close the GUI layout, clear button should be triggered. 34

47 Chapter-6 Chapter 6 Evaluation This chapter deals with the performance evaluation of the speech enhancement system in hearing aid which is proposed in previous chapters. Enhancement of speech depends on the quality of the processed speech determines whether the effort is worthwhile. An evaluation of speech enhancement requires a series of objective measures to be conducted on the proposed system, these measures determines the quality of the output signal. The objective methods include the measure of SNR, SNRI, the measure of PESQ value under the ITU-TP.862 is used to measure the quality of the speech signal, SD and ND [23]. Objective measures are widely used in speech enhancement. The advantages of the objective measure are that the results can be easily viewed for verification and a large number of test data can be evaluated using a computer. Though there maybe overall noise reduction in the signal, there may be very little amount of noise remains in the processed signal. This chapter describes the test employed and the test data used. 6.1 Test Data Clean Speech Data The speech signals used for the test are sampled at 16 khz. The signal is of short speech sample of 3 seconds. Two male voices and one female voice are used as the test date. The speech file used throughout the test is as Table Sentence.wav is used as a main speech signal and the other two voice signals are used as interference signals. TABLE TYPE OF MALE AND FEMALE SENTENCES USED FOR EVALUATION File Name Type of Voice Sentence Setntence.wav Male Voice She sells seashells by the seashore Man.wav Male Voice Someone walking on the side walk with the rainbow Woman.wav Female Voice A good birthday has canoes with cap cakes cargoes in rainbow color 35

48 Power in [db] Chapter Noise Data Various noise signals are used for the evaluation of the proposed method. All the noise signals are taken from Noisex-92 database [24]. All the noise signals are recorded at these signals are resampled to as that of the sampling frequency of the speech signal. These noise signals and interference male and female voice are added to speech signal at different SNR values. The input SNR value are scaled to different levels such as using the formula where is the variance of speech signal and is the variance of the noise signal. The value of in the equation may be. The brief description of various noise signals used for the evaluation are given as follows Babble Noise The most challenging interference noise for the speech system is babble noise. This type of noise is highly non-stationary and is obtained by recording the voice of people speaking in a canteen. This is obtained by recording samples from B&K condenser microphone onto DAT. The room radius is over two meters therefore, individual voices are slightly audible. The sound level during the recording process was. It is the most difficult noise for speech enhancement. The power spectrum of the babble noise is as show in the Fig Power spectrum of Babble Noise Frequency in [Hz] Fig Power Spectrum of Babble Noise 36

49 Power in [db] Power in [db] Chapter Car Interior Noise This recording was made in Volvo car at, in the gear, on an asphalt road, in rainy conditions. Many speech enhancement systems perform well with car interior noise due to low pass nature of this noise filter. The power spectrum of the car interior noise is as shown in Fig Power spectrum of Car Interior Noise Frequency in [Hz] Destroy Engine Noise Fig Power Spectrum of Car Interior Noise This type of noise is obtained by recording samples from microphone on DAT. Sound level during the recording process is of noise is as shown in Fig The power spectrum of this type 50 Power spectrum of Destroy Engine Noise Frequency in [Hz] Fig Power Spectrum of Destroy Engine Noise 37

50 Power in [db] Power in [db] Chapter Tank Noise This type of noise is recorded from tank by using B&K condenser microphone onto DAT. The tank is moving at a speed of. The sound level during the recording process was in Fig The power spectrum of the tank noise is as shown 60 Power spectrum of Tank Noise Wind Noise Fig Power Spectrum of Tank Noise Wind noise is the noise caused by the turbulent airflow over and around an object. The wind is an invisible force. When wind strikes the surface of the microphone it produces an effect called as wind noise. The power spectrum of the wind noise is as shown in Fig Frequency in [Hz] 60 Power spectrum of Tank Noise Frequency in [Hz] Fig Power Spectrum of Wind Noise 38

51 Chapter Objective Measures Various objective tests used to measure the performance of the proposed system are described below Signal to Noise ratio Signal to noise ratio (SNR) is used to measure to compare the level of the desired signal to level of the background noise. The conventional method to measure the SNR is to compute the amount of speech energy over the noise energy after the enhancement and is given as where is the variance of the speech signal and is the variance of the noise signal SNR Improvement SNR improvement is measured by subtracting the input SNR value from that of the output SNR value and is expressed as follows where is the variance of the output speech signal, is the variance of the output noise signal, is the variance of the input speech signal, is the variance of the input noise signal Perceptual Evaluation of Speech Quality Perceptual Evaluation of Speech Quality (PESQ) is the international standard for objective speech quality measurement and is well known as intrusive objective speech quality assessment method. The PESQ is an Objective measure but it based on cognitive models of the human hearing organ to form pseudo subjective scores and it has high correlation with real subjective tests. It is standardized as ITU-T P.862 PESQ. PESQ operates on a transmitted (input) signal and received (output) speech signal to compute the perceptual quality of the received signal. PESQ is used in Voice over Internet Protocol (VoIP), mobile 39

52 Chapter-6 transmission, in fixed networks in order to measure the quality of the speech signal. The evaluation of system using PESQ measure is as shown in Fig s(n) v(n) x(n) System under Test y(n) PESQ P.862 PESQ Score Fig Structure of Perceptual Evaluation of Speech Quality model A number of objective measures examined in previous study for predicting the intelligibility of speech in noisy conditions. The mostly used one is PESQ. Among all objective measures considered, the PESQ measure is the most complex to compute and is one recommended by a standardized agency i.e. International Telecommunication union (ITU-T 2000) for speech quality assessment of 3.2 KHz (narrow band) handset telephony and narrow-band speech codec [25, 29]. The PESQ measure is computed as follows: The original (clean) and degraded signals are first level equalized to a standard listening level and filtered by a filter with response similar to that of standard telephone headset. The signals are time aligned to correct for time delays, and the processed through an auditory transform to obtain the loudness spectra. The difference in loudness between the original and degraded signals is computed and averaged over time and frequency to produce the prediction of subjective quality rating. Finally the output of the system determines the PESQ value of the signal. The PESQ delivers an output value which lies in the range between -0.5 to 4.5. PESQ values in the range -0.5 indicates the poor quality of the voice signal. The PESQ value in the range 4.5 indicates excellent quality of the voice signal [26]. 40

53 Chapter Measurement of Speech Distortion SD is defined as the spectral deviation in the power of the input clean speech signal and the power of the processed speech signal at the output. A reference power level of the enhanced output signal is obtained by normalizing the target speech signal. The normalizing factor given as SD is given by where is the power of input speech is signal and is the power of output speech signal Measure of Noise Distortion ND is defined as the spectral deviation in the power of the input noise signal and the power of the processed noise signal at the output. A reference power level of the enhanced output signal is obtained by normalizing the target noise signal. The normalizing factor given as ND is given by where is the power of input noise is signal and is the power of output noise signal. 41

54 Chapter Test Results with Various Noise Signals In this thesis, we considered a source signal, noise signal and eight microphones placed in an arc position, the distance between the microphones is considered to be 1cm and is as shown in Fig , red dot indicates the source signal position, black dots indicates the position of microphones and blue dot indicates the noise signal position. 0.1 Position of Source Signal, Microphones and Noise Signal Fig Graph represents the position of source, noise signal and microphones position we use a clean male speech signal as a test signal sampled at 16 khz frequency which is used for the effective validation of the system. This speech signal is corrupted with various noise signals such as babble noise, car interior noise, tank noise, wind noise, male voice as interference noise, destroy engine noise and female voice as interference noise signal at 0 db, 5 db, 10 db, 15 db and 20 db for testing the system. The graphical representation of clean speech signal she sells seashells by the seashore is as shown in Fig Fig Graphs represent a clean speech signal 42

55 Chapter-6 Fig (a), (a), (a), (a), (a), (a), (a), represents the corrupted speech signal with babble noise, car interior noise, tank noise, wind noise, man voice as interference noise, destroy engine noise, woman noise respectively. Fig (b), (b), (b), (b), (b), (b), (b) represents the enhanced speech signal from various noise signals. Fig , , , , , , , represents the graph of SNR values measured. Fig , , , , , , , represents the graphs of input and output signal PESQ score. Fig , , , , , , , represents the graph of SD between pure clean speech signal and enhanced speech signal from various noises. Fig , , , , , , , represents the graph of ND between the input noise signal and output noise signal. Fig , , , , , , , represents the GUI layout designed for the proposed system with various noise signal and different input SNR values. Table 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8 represents the SNRI, SD and ND values for different input SNR values Evaluation of Babble Noise (a) (b) Fig Graphs represent (a) Corrupted speech with babble noise at 10 db (b) Enhanced speech signal. The enhanced speech signal produced by the proposed method is clean with enhanced quality without audible noise signal. 43

56 PESQ Value Elko, Output SNR Chapter Graph Representing the SNR Value of Babble Noise Input SNR Elko SNR Output SNR Input SNR in db Fig Graph represents the SNR value for babble noise PESQ of Babble Noise input output SNR in db Fig Graph shows the PESQ value for babble noise 44

57 Chapter Speech Distortion Graph of Babble Noise Input Speech Output Speech Fig Graph shows the SD of babble noise at 10 db Noise Distortion Graph of Babble Noise Input Noise Output Noise Fig Graph shows the ND of babble noise at 10 db 45

58 Chapter-6 TABLE 6.2 REPRESENTS THE SNRI, SPEECH AND NOISE DISTORTION FOR BABBLE NOISE Input SNR SNRI Speech Distortion Noise Distortion 0 db db db db db Fig GUI Layout with babble as input noise at 10dB of input SNR The proposed system produces a good performance of results with babble noise. The proposed system produces approximately 17 db of SNRI with babble noise. The PESQ value represents that the quality of the output speech signal is good compared to that of the input signal. 46

59 Elko, Output SNR Chapter Evaluation of Car Interior Noise (a) (b) Fig Graphs represent (a) Corrupted speech with car noise at 0db (b) Enhanced speech signal Graph Representing the SNR Value of Car Noise Input SNR Elko SNR Output SNR Input SNR in db Fig Graph represents the SNR value for car noise 47

60 PESQ Value Chapter PESQ of Car Noise input output SNR in db Fig Graph shows the PESQ value for car noise 0-10 Speech Distortion Graph of Car Interior Noise Input Speech Output Speech Fig Graph shows the SD of car interior noise at 0 db 48

61 Chapter Noise Distortion Graph of Car Interior Noise Input Noise Output Noise Fig Graph shows the ND of car interior noise at 0 db TABLE 6.3 REPRESENTS THE SNRI, SPEECH AND NOISE DISTORTION FOR CAR INTERIOR NOISE Input SNR SNRI Speech Distortion Noise Distortion 0 db db db db db The proposed method has a high performance with car interior noise as input noise. The proposed system produces approximately 22 db of SNRI. By listening the output speech signal it is free from noise and a clean speech is audible at the output. The GUI layout of the babble noise at 10db is as shown in Fig

62 Chapter-6 Fig GUI Layout with car interior noise as input noise at 0dB of input SNR Evaluation of Tank Noise (a) (b) Fig Graphs represent (a) Corrupted speech with tank noise at 5db (b) Enhanced speech signal. 50

63 PESQ Value Elko, Output SNR Chapter Graph Representing the SNR Value of Tank Noise Input SNR Elko SNR Output SNR Input SNR in db Fig Graph represents the SNR value for tank noise PESQ of Tank Noise input output SNR in db Fig Graph shows the PESQ value for tank noise 51

64 Chapter Speech Distortion Graph of Tank Noise Input Speech Output Speech Fig Graph shows the SD of tank noise at 5 db Noise Distortion Graph of Tank Noise Input Noise Output Noise Fig Graph shows the ND of tank noise at 5 db 52

65 Chapter-6 TABLE 6.4 REPRESENTS THE SNRI, SPEECH AND NOISE DISTORTION FOR TANK NOISE Input SNR SNRI Speech Distortion Noise Distortion 0 db db db db db Fig GUI Layout with tank noise as input noise at 5dB of input SNR The proposed system produces a good performance of results with tank noise. The proposed system produces approximately 18 db of SNRI with tank noise. The PESQ value represents that the quality of the output speech signal is good compared to that of the input signal. By listening the output speech signal it is free from noise and a clean speech is audible at the output. 53

66 Elko, Output SNR Chapter Evaluation of wind Noise (a) (b) Fig Graphs represent (a) Corrupted speech with wind noise at 0db (b) Enhanced speech signal Graph Representing the SNR Value of Wind Noise Input SNR Elko SNR Output SNR Input SNR in db Fig Graph represents the SNR value for wind noise 54

67 PESQ Value Chapter PESQ of Wind Noise input output SNR in db Fig Graph shows the PESQ value for wind noise 0-10 Speech Distortion Graph of Wind Noise Input Speech Output Speech Fig Graph shows the SD of wind noise at 0 db 55

68 Chapter Noise Distortion Graph of Wind Noise Input Noise Output Noise Fig Graph shows the ND of wind noise at 0 db TABLE 6.5 REPRESENTS THE SNRI, SPEECH AND NOISE DISTORTION FOR WIND Input SNR SNRI Speech Distortion Noise Distortion 0 db db db db db The proposed system produces a good performance of results with tank noise. The proposed system produces approximately 19 db of SNRI with wind noise. The PESQ value represents that the quality of the output speech signal is good compared to that of the input signal. By listening the output speech signal it is free from noise and a clean speech is audible at the output. 56

69 Chapter-6 Fig GUI Layout with wind noise as input noise at 0db of input SNR Evaluation of Man voice as interference Noise (a) (b) Fig Graphs represent (a) Corrupted with man interference noise at 5db (b) Enhanced speech signal. 57

70 PESQ Value Elko, Output SNR Chapter Graph Representing the SNR Value of Male Voice as Interference Noise Input SNR Elko SNR Output SNR Input SNR in db Fig Graph represents the SNR value for man voice as interference noise PESQ of Man Voice as Interference Noise input output SNR in db Fig Graph shows the PESQ value for man voice as interference noise 58

71 Chapter Speech Distortion Graph of Man Voice as Interference Noise Input Speech Output Speech Fig Graph shows the SD of man voice as interference noise at 5 db Noise Distortion Graph of Man Voice as Interference Noise Input Noise Output Noise Fig Graph shows the ND of man voice as interference noise at 5 db 59

72 Chapter-6 TABLE 6.6 REPRESENTS THE SNRI, SPEECH AND NOISE DISTORTION FOR MAN VOICE AS INTERFERENCE NOISE Input SNR SNRI Speech Distortion Noise Distortion 0 db db db db db Fig GUI Layout with man voice as interference noise at 5db of input SNR The proposed system produces a good performance of results with man voice as interference noise. The proposed system produces approximately 15 db of SNRI with man voice as interference noise. The PESQ value represents that the quality of the output speech signal is good compared to that of the input signal. By listening the output speech signal it is free from noise and a clean speech is audible at the output. 60

73 Elko, Output SNR Chapter Evaluation of Destroy Engine Noise (a) (b) Fig Graphs represent (a) Corrupted speech with destroy engine noise at 0dB (c) Enhanced speech signal Graph Representing the SNR Value of Destroy Engine Noise Input SNR Elko SNR Output SNR Input SNR in db Fig Graph represents the SNR value for Destroy engine noise 61

74 PESQ Value Chapter PESQ of Destroy Engine Noise input output SNR in db Fig Graph shows the PESQ value for destroy engine noise 0-10 Speech Distortion Graph of Destroy Engine Noise Input Speech Output Speech Fig Graph shows the SD of destroy engine noise at 0 db 62

75 Chapter Noise Distortion Graph of Destroy Engine Noise Input Noise Output Noise Fig Graph shows the ND of destroy engine noise at 0 db TABLE 6.7 REPRESENTS THE SNRI, SPEECH AND NOISE DISTORTION FOR DESTROY ENGINE NOISE Input SNR SNRI Speech Distortion Noise Distortion 0 db db db db db The proposed system produces a good performance of results with destroys engine noise. The proposed system produces approximately 13 db of SNRI with wind noise. The PESQ value represents that the quality of the output speech signal is good compared to that of the input signal. By listening the output speech signal it is free from noise and a clean speech is audible at the output. 63

76 Chapter-6 Fig GUI Layout with destroy engine noise at 0db of input SNR Evaluation of Female voice as interference Noise (a) (b) Fig Graphs represent (a) Corrupted with female interference noise at 10dB (b) Enhanced speech signal. 64

77 PESQ Value Elko, Output SNR Chapter-6 Graph Representing the SNR Value of Female Voice as Interference Noise Input SNR Elko SNR Output SNR Input SNR in db Fig Graph represents the SNR value for female voice as interference noise PESQ of Female Voice as Interference Noise input output SNR in db Fig Graph shows the PESQ value for female voice as interference noise 65

78 Chapter Speech Distortion Graph of Woman Voice as Interference Noise Input Speech Output Speech Fig Graph shows the SD of female voice as interference noise at 10 db Noise Distortion Graph of Woman Voice as Interference Noise Input Noise Output Noise Fig Graph shows the ND of female voice as interference noise at 10 db 66

79 Chapter-6 TABLE 6.8 REPRESENTS THE SNRI, SPEECH AND NOISE DISTORTION FOR FEMALE VOICE AS INTERFERENCE NOISE Input SNR SNRI Speech Distortion Noise Distortion 0 db db db db db Fig GUI Layout with female voice as interference noise at 10db of input SNR The proposed system produces a good performance of results with female voice as interference noise. The proposed system produces approximately 15 db of SNRI with man voice as interference noise. The PESQ value represents that the quality of the output speech signal is good compared to that of the input signal. By listening the output speech signal it is free from noise and a clean speech is audible at the output. 67

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