# Optimal Adaptive Filtering Technique for Tamil Speech Enhancement

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3 u( ( n 1) (. e( (3) u( 2 Steps in RLS algorithm 2 Where ( / u( (4) The NLMS algorithm works same as the standard LMS algorithm except that it uses time-varying step size μ (. The advantage of this varying step size will improve the convergence rate but the strength of the signal is still maintained. In contrast to LMS algorithm, the error signal is comparatively smaller in NLMS [11]. Also it is observed that the convergence rate of the NLMS algorithm is greater than that of the standard LMS algorithm because of multiplication operations. 3.3 Recursive Least Squares (RLS) algorithm The RLS filter is a recursive implementation of the Wiener filter which is used to find the difference between the desired and actual signal. RLS algorithm has the potential to automatically adjust the coefficients of a filter, even though the statistics measures of the input signals are not presented. This algorithm performs at each instant an exact minimization of the sum of the squares of the desired signal estimation errors [6]. Since it utilizes all the information contained in the input data, the estimation is updated recursively when the arrival of new sample. The design of RLS algorithm is given in Figure 3. Input vector u( Transversal Filter ŵ (n-1) Adaptive Weight-control Mechanism Figure 3: Design of RLS algorithm ŵ H (n-1)u( Error ξ ( + The steps involved in RLS algorithm are given bellow. Σ - Desired response Out Compared to the LMS algorithm, the RLS approach offers faster convergence and smaller error with respect to the unknown system, at the expense of requiring more computations. 4. EXPERIMENTAL RESULTS LMS and NLMS algorithms have a step size that determines the amount of correction applied as the filter adapts from one iteration to the next. Choosing the appropriate step size plays a major role in adaptive filter design. A step size that is too small increases the time for the filter to converge on a set of coefficients. This becomes an issue of speed and accuracy. A step size that is too large may cause the adapting filter to diverge, never reaching convergence. In this case the resulting filter might not be stable. Based on the above criteria, it was found from the experiments that the results highly depend on the step size value. Various step values are tested with the different datasets and it is clear from the experiments that the smaller step sizes improve the accuracy of the convergence of the filter to match the characteristics of the unknown to adapt. It is also observed that a larger step size gives a faster response, but if it is too large, then the result is not satisfactory [10]. In this paper, 0.2 was found to be the optimal step size for the LMS. For experiments, the separate noise corpus from NOIZEUS were collected and added to the continuous Tamil Speech signals. The performances of these algorithms are investigated for speech enhancement in different noise conditions. Totally 15 datasets were generated for this research work. Three types of noises were implemented namely white noise, pink noise and babble noise at 5, 10,-5 and -10 db SNR. The following figure 4 shows the speech signal noise cancellation for white noise at 10dB using LMS, NLMS and RLS algorithms respectively. 25

4 MSE yi y n p i (5) 5.2 Peak Signal to Ratio (PSNR) The Peak Signal-to- Ratio (PSNR) is the ratio between a signal's maximum power and the power of the signal's noise. It is calculated by the formula (6) 2 R PSNR 10 log 10 MSE (6) 5.3 Signal-to- Ratio (SNR) SNR is defined as the ratio of power between the signal and the unwanted noise. One of the most important goals of any speech enhancement technique is to achieve the highest possible SNR. The higher the ratio the better the performance in noise cancellation or reduction. SNR is calculated using the formula (7) S signal (7) N n n noise Figure 4: Results of noise cancellation for white noise at 10dB SNR using LMS, NLMS and RLS algorithms 5. PERFORMANCE EVALUATION The primary objective of the adaptive filter is to minimize the error signal e(k). The success of this minimization will clearly depend on the nature of the input signals and the adaptive algorithm used. The quality of speech signals is a subjective measure which reflects the way the signal is perceived by listeners. At 0 db the two signals are of equal strength and negative values are associated with loss of intelligibility due to masking whereas positive values are usually associated with better intelligibility. All the three algorithms obtained a positive and higher SNR values. The performances of these algorithms are measured based on the metrics namely PSNR, MSE, SNR and SNR Loss which are explained below. 5.1 Mean Squared Error (MSE) The Mean Squared Error (MSE) of an estimator is used to quantify the difference between values implied and the true values being estimated. It is calculated using the formula Where signal. nsignalthe original is signal and n noise is the noisy 5.4 SNR loss: SNR loss is the increased signal-to-noise ratio required by an individual to understand speech in noise, as compared to normal performance. It is a new objective measure for predicting the intelligibility of noise-suppressed speech [5]. It provides an efficient way to determine the ability of a person to hear speech in background noise. The following table 1 illustrates the range of SNR Loss score and its remarks. SNR loss Table 1. Range of SNR Loss Score and its Remarks Degree SNR loss 0-3 db Normal/near normal of Expected improvement with directional microphones May hear better than normal hear in noise 3-7 db Mild SNR loss May hear almost as well as normal hear in noise 7-15 db Moderate SNR loss Moderate SNR loss Directional microphones help. Consider array microphones 26

5 >15 db Severe SNR loss Maximum SNR improvement is needed. Consider FM system The following figures 5, 6, 7 and 8 shows the average performance evaluation for the 15 datasets using LMS, NLMS and RLS algorithms for 10dB white noise based on MSE, PSNR, SNR and SNR loss respectively. Figure 7: Performance Evaluation of LMS, NMLS and RLS based on SNR Figure 5: Performance Evaluation of LMS, NMLS and RLS based on MSE Figure 8: Performance Evaluation of LMS, NMLS and RLS based on SNR Loss Figure 6: Performance Evaluation of LMS, NMLS and RLS based on PSNR It is clear from the above figures that the RLS algorithm offers least MSE and SNR loss values and it also achieves the highest SNR and PSNR values for all types of noise at different SNR level. All the above algorithms obtained the SNR loss score of the range between 0-3 db generally and 0-1dB for this particular experiment. The following table 2 shows the comparison of the adopted algorithms based on the average value of the 15 datasets. 27

6 Table 2. Comparison of LMS, NLMS and RLS algorithms for White, Pink and Babble based on MSE, PSNR, SNR and SNR Loss Values Type of SNR (db) LMS NLMS RLS MSE PSNR SNR SNR Loss MSE PSNR SNR SNR Loss MSE PSNR SNR SNR Loss 10dB White 5dB dB dB dB Pink 5dB dB dB Babble 10dB dB dB dB CONCLUSION The performance of speech communication system is degraded when the input signal contains a significant level of noise. As a result, speech quality, speech intelligibility, or recognition rate requirements cannot be met. Improvements are obtained when the speech processing system is combined with a speech enhancement preprocessor. In this paper, the three widely used adaptive filters such as LMS, NLMS and RLS algorithms have accomplished for Tamil speech noise cancellation. Among these, LMS algorithm is a very simple and effective method to implement though it is a slower one. Even though, with increased step size, the rate of convergence obtained in NLMS is not up to the satisfactory level. The experimental results show that the RLS algorithm makes the converging speed and also provides better noise reduction and improved speech quality and intelligibility when compared to the other algorithms. As a result, with these appropriate settings of the adaptive filter parameters, this optimal signal can be employed for the speech recognition system as a front end. 7. REFERENCES [1] Georgi Iliev and Nikola Kasabov, Adaptive Filtering with Averaging in Cancellation for Voice and Speech Recognition, Department of Information Science, University of Otago. [2] V.JaganNaveen, T.prabakar, J.Venkata Suman, P.Devi Pradeep, suppression in speech signals using adaptive algorithms, International Journal of Signal Processing, Image Processing and Pattern Recognition,Vol. 3, No. 3, September, [3] Jan Vanus and Vitezslav Stkskala, Application of optimal settings of the LMS adaptive filter for speech signal prosessing, Procedings of the International multiconference on Computer Science and Information Technology pp , ISBN ,ISSN [4] Jan Vanus, The use of the adaptive noise cancellation for voice Communication with the control system, International Journal of Computer Science and Applications,Technomathematics Research Foundation,Vol. 8, No. 1, pp , [5] Jianfen Maa b, Philipos C. Loizou b, SNR loss: A new objective measure for predicting the intelligibility of noise-suppressed speech, J. Ma, P.C. Loizou / Speech Communication 53 (2011) [6] Komal R. Borisagar and Dr. G.R.Kulkarni, Simulation and Comparative Analysis of LMS and RLS Algorithms Using Real Time Speech Input Signal, Global Journal of Researches in Engineering, Page 44,Vol.10,Issue,5 (Ver1.0)October2010. [7] D.Prabhakara Rao,Sri M.Koteswararao, Dr.I.Santhi Prabha, Combinational Adaptive Filter Technique for Speech Enhancement, IJECT Vol. 2, SP-1, Dec. 2011, ISSN : (Online) ISSN : (Print). 28

7 [8] Sayed. A. Hadei, Student Member IEEE and M. lotfizad, A Family of Adaptive Filter Algorithms in Cancellation for Speech Enhancement, International Journal of Computer and Electrical Engineering, Vol. 2, No. 2, April 2010, [9] Simon Haykin,"Adaptive Filter Theory", prentice hall of india,4 th Edition. [10] Siva Prasad Nandyala and T. Kishore Kumar, Speech Enhancement Using Kernel Adaptive Filtering Method, Microwaves, Communications, Antennas and Electronics Systems (COMCAS), 2011 IEEE International Conference on, /COMCAS , ISBN: [11] A. Srinivasan, Adaptive Echo Elimination for Speech Enhancement of Tamil letter Zha, International Journal of Engineering and Technology Vol.1 (3), 2009, 91-97, ISSN: [12] L. Stasionis, A. Serackis, Selection of an Optimal Adaptive Filter for Speech Signal Cancellation using C6455 DSP, Electronics And Electrical Engineering, ISSN , No. 9(115). 29

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