An Audio Fingerprint Algorithm Based on Statistical Characteristics of db4 Wavelet
|
|
- Fay Horn
- 6 years ago
- Views:
Transcription
1 Journal of Information & Computational Science 8: 14 (2011) Available at An Audio Fingerprint Algorithm Based on Statistical Characteristics of db4 Wavelet Jianguo JIANG a,, Kaige MA a, Mingxing WEN a, Yongqing LIU a, Shuangji WANG a,b a School of Computer Science and Technology, Xidian Univ., Xi an , China b No Troops of PLA, Zhanjiang , China Abstract Aiming at providing a solution to the problems that the feeblish robustness of general algorithms in dealing with the linear speed change attacks and their overlarge fingerprint memory space, an audio fingerprint algorithm based on db4 wavelet transformation combined with statistical characteristics of wavelet domain is proposed. At first, decompose the audio signal in 5-layer wavelet. Then calculate the plus-minus change of low-frequency sub-band s wavelet coefficient, the energy distribution center, the energy of sub-band in wavelet domain, and the variance of wavelet coefficient. Finally, by using the results calculated as parameters of audio fingerprints, the 8-bit fingerprint block per frame was generated. Simulation results suggest that this algorithm shows excellent robustness in dealing with the attacks toward ordinary stick signal content and additive white Gaussian noise, and linear speed change attacks. Moreover, the memory space taken up by fingerprints is less. Keywords: Audio Fingerprints; Wavelet Transform; Linear Attack; Additive White Gaussian Noise; Robustness 1 Introduction In order to solve the difficulties in searching the needed songs among mass audio information, a digital audio fingerprinting technology with automatic music recognition came into being. Audio fingerprint is a compact digital signature based on content, which can represent the important acoustic characteristics of a piece of music. Its main purpose is to establish an effective mechanism to compare the two audio data in human auditory perception [1,2]. Wavelet transform is a local transformation on a signal in Time and Frequency domains, which can effectively extract information from the signal, and do multi-scale detailed analysis on a function or signal by functions such as scaling and translation, thereby can solve many difficult issues which can not be solved by the Fourier transform. CSLu and others proposed a method, which is, supported by the Fundamental Research Funds for the Defense of China (NO. D ). Corresponding author. address: jjg3306@126.com (Jianguo JIANG) / Copyright 2011 Binary Information Press December 2011
2 3028 J. JIANG et al. / Journal of Information & Computational Science 8: 14 (2011) by adopting one-dimensional continuous wavelet transformation to extract audio characteristics, based on this method the audio fingerprint generation method for identification and authentication respectively was constructed. AL.Ghouti and others used balanced multiwavelets (Balanced Multiwavelets, BMW) extraction coefficient feature to propose an audio hashing algorithm [3]. In some documents, the author proposed audio fingerprint algorithm by combining computer vision. Y. Ke and others made audio signal spectrum as a two-dimensional images to handle [4], S. Bahja and others applied computer vision technology into data stream processing, and generated audio fingerprints by the Haar wavelet transform and Min Hash technology, and used Locality Sensitive Hashing(LSH) technique [5,6] in audio fingerprint retrieval. In terms of audio fingerprint algorithm in time and frequency domains, this paper considered how to improve the robustness in dealing with the linear speed change attacks and reduce its memory space for fingerprints, and proposed an audio fingerprint algorithm based on db4 wavelet transform, which combined wavelet transform with audio fingerprint algorithm. During the process of the algorithm, the audio signal was decomposed into 5-layer wavelet, and then calculated the plus-minus change of low-frequency sub-band s wavelet coefficient, the energy distribution center, the energy of sub-band in wavelet domain, and the variance of wavelet coefficient. Finally, by using the results calculated as the parameters of the audio fingerprints, the 8-bit fingerprint block per frame was generated. Comparison of simulation results suggest that this algorithm shows excellent robustness and identification, and the fingerprint is smaller in size. Using the index relationship established between the audio fingerprint algorithm and audio information, it can realize audio information real-time searching, which greatly improve the efficiency of audio searching [7,8]. 2 Algorithm Process The main steps of the algorithm are as follows: (1)Pretreatment, converts the input audio signal to mono signal whose down-sampling frequency is 5KHz. (2)Framing, windowing and overlapping, the length of the frame is 0.37s, using Hanning window, the overlap factor is P=28/32. The formula of Hanning window is as follows: w(n) = { 0.5 [1 cos(2πn/(n 1))], 0 n N 1 0, else (3)Using the wavelet based on db4 to decompose each frame of audio signal in 5-layer wavelet. A total of six components are achieved which include one approximation component ca5 and five details component cd1,, cd5. (4)Calculate the variance of the wavelet coefficients, the zero-crossing rate of wavelet coefficients, the centroid of wavelet domain and the energy of sub-band in wavelet domain of each component. (5)Extract hash bite value from each set of parameters in order to get a set of audio fingerprints of 8bits for per frame. The principle framework of the algorithm is shown in Fig. 1.
3 J. JIANG et al. / Journal of Information & Computational Science 8: 14 (2011) Generation of Fingerprint Fig. 1: Principle framework of the algorithm 3.1 The variance of the wavelet coefficients The formula of the variance of the wavelet coefficients [9] is σ(i, j) = 1 N (cd j cd) 2 N j=1 Where, cd = N cdj, σ (i, j) represents the variance of the j-th wavelet coefficient in the i-th j=1 frame, and N represents the total number of wavelet coefficients (The following definitions are the same with the definitions above). 3.2 The zero-crossing rate of the wavelet domain The zero-crossing rate of the wavelet domain reflects the plus-minus change of low-frequency sub-band s wavelet coefficients [9] when audio signal has been dealt with wavelet transform. The formula of it is as follows: zcr m = 1 sign[x(n)] sign[x(n 1)] w(n m) 2 m Where, x(n) is the n-th value of the wavelet coefficients in the m-th frame, which separately correspond to ca 5 and cd 5 ; W (n) is the window function, the length of which is N. if x(n) 0, then sign [x(n)] = 1; otherwise sign [x(n)] = The centroid of the wavelet domain The centroid of the wavelet domain is expressed as the center of energy distribution. In wavelet domain, the centroid of the audio signal changes with time, so it can be the characteristics of reflecting the non-stationarity of audio signal.
4 3030 J. JIANG et al. / Journal of Information & Computational Science 8: 14 (2011) The computational formula of the centroid [9] is: N i x(i) 2 centroid = N x(i) 2 Where, x(i) is the i-th wavelet coefficient. 3.4 The energy of sub-band in wavelet domain The change in amplitude of the audio signal is an important dynamic characteristic of the audio signal, and the change in amplitude can reflect the change of energy. We can use the wavelet coefficients to measure the energy characteristics of audio because of the fact that the average rate of the wavelet coefficients corresponds to the average rate in time domain. The formula of calculating the energy of sub-band [9] is as follows: energy = 1 N x(i) 2 N 3.5 Generation of fingerprint The formula of the Hash-bit value sequence of the variance of wavelet coefficient is as follows: { 1, σ(n, m) σ(n, m + 1) (σ(n + 1, m) σ(n + 1, m + 1)) > 0 F 1 (n, m) = (1) 0, σ(n, m) σ(n, m + 1) (σ(n + 1, m) σ(n + 1, m + 1)) 0 Where, F 1 (n, m) represents the m-th bit value in the n-th frame. Besides, the formulas of the Hash-bit value of the zero-crossing rate of the wavelet coefficients, the centroid of the wavelet domain and the energy of the wavelet domain are as follows: 1, S c (n) N S c (i) > 0 F 2 (n, c) = 0, S c (n) N (2) S c (i) 0 Where, F 2 (n, c) corresponds to the Hash bit value of the zero-crossing rate of the wavelet coefficients, the centroid of the wavelet domain and the energy in wavelet domain, S c (n) represents the zero-crossing rate of the wavelet coefficients, the centroid of the wavelet domain or the energy in wavelet domain for the n-th frame. Set c = 1, which represents the zero-crossing rate of the wavelet coefficients; Set c = 2, which represents the centroid of the wavelet domain; Set c = 3, which represents the energy in wavelet domain. We can get the final formula of the audio fingerprint bit value for per frame: F 1 (n, m), 0 < m 5 F 2 (n, 1), m = 6 F (n, m) = F 3 (n, 2), m = 7 F 2 (n, 3), m = 8 (3)
5 J. JIANG et al. / Journal of Information & Computational Science 8: 14 (2011) Simulation Results and Comparison The simulation uses 100 randomly selected popular songs as test audios. Randomly select 4 initial points and intercept audio clips as long as 3.3s for each test audio, so there are 400 audio clips in total as experimental samples. After attack treatment, use the algorithm proposed in this paper and the traditional Mel frequency cepstrum coefficients (MFCC) algorithm respectively to make a simulation comparison. The results show that the algorithm proposed in this paper has better robustness for general content attacks, especially for linear speed change attack. The simulation uses Bit Error Rate(BER), Correct Identification Rate(CIR) and Best Recognition Rate(BRR) to measure the robustness of the algorithm [10,11]. The experimental environment is Windows XP, CPU 1.61GHz, 512MB memory; The tools used in the experiment include MATLAB 6.5, Adobe Audition The robustness analysis on the attack treatment of the common stick signal content For the attack treatment of the common stick signal content, the average BER between the fingerprint of the attacked audio clips and the fingerprint of the source audio is shown in Fig. 2. (Of all the figures in this paper, the dotted line represents the algorithm based on db4 wavelet characteristics, and the solid line represents the MFCC algorithm). Fig. 2: Comparison of the average bit error rate(ber) under different attack for the algorithm In Fig. 2, attack type 1-20 are respectively 32Kbps MP3 Compression Attack,128Kbps MP3 Compression Attack, Band-pass filter(bpk) attack, Amplitude Compression Attack,Equalization Attack, Echo Attack, Time Scale Modification Attack(TSM are separately ±2%, ±4% and ±5%, and the principle that the negative after the positive is taken in the figure) and Liner Speed Change Attack(LSC are separately ±1%, ±2%, ±3%, ±4% and ±5%). Fig. 2 shows that the average BER of the algorithm under attack is more stable than the MFC-
6 3032 J. JIANG et al. / Journal of Information & Computational Science 8: 14 (2011) C algorithm, especially in terms of Liner Speed Change Attack [12] and Time Scale Modification Attack; while MFCC has advantages relatively in terms of 32Kbps MP3 Compression Attack, 128Kbps MP3 Compression Attack, Band-pass filter(bpk) attack, Amplitude Compression Attack and Equalization Attack. The analysis on the correct identification rate and the best recognition rate of the algorithm under different attacks are shown in Fig. 3. Fig. 3 shows that the algorithm based on db4 wavelet (a) Comparison of correct identification rate (CIR) (b) Comparison of best recognition rate (BRR) Fig. 3: Performance of the algorithm when under different attacks statistical characteristics has stronger robustness in terms of Liner Speed Change Attack, so as to overcome the shortcoming of weak robustness for most algorithms. 4.2 The robustness analysis of the additive white gaussian noise The robustness of the additive white Gaussian noise when the algorithm is used under different degrees is shown in Fig. 4. In this experiment, the signal to noise ratio(snr)of the additive white Gaussian noise is separately set to 20dB, 15dB, 10dB, 5dB, 3dB and 2dB. Fig. 4 shows that the average BER of the algorithm based on db4 wavelet statistical characteristics is more stable than that of the MFCC algorithm when under the attack of the additive white Gaussian noise; while in terms of the correct identification rate, the algorithm based on db4 wavelet statistical characteristics shows a better performance in low signal to noise ratio(snr), but it doesn t improve much as the SNR grows; in terms of the best recognition rate, the algorithm based on db4 wavelet statistical characteristics is better than the MFCC algorithm in the case of low signal to noise ratio (SNR). 5 Conclusion This paper proposes an audio fingerprinting algorithm based on db4 wavelet statistical characteristics. Use the plus-minus change of low-frequency sub-band s wavelet coefficients after wavelet transform, the energy distribution center in wavelet domain, the energy of sub-band in wavelet domain, and the variance of wavelet coefficients as parameters of extracting audio fingerprinting. The results of simulation and comparison with the MFCC algorithm show that the algorithm has
7 J. JIANG et al. / Journal of Information & Computational Science 8: 14 (2011) (a) Comparison of average bit error rate (BER) (b) Comparison of correct identification rate (CIR) (c) Comparison of the best recognition rate (BRR) Fig. 4: Performance of the algorithm when under additive white gaussian noise attack better robustness and higher recognition rate. But it has relatively weak ability while dealing with band-pass filter attack, amplitude compression attack and equilibrium attack. In future research, it is necessary to have a further study in the improvement of fingerprint size and the robustness while coping with the above three attacks, so as to increase the efficiency and the correct recognition rate of the algorithm. Acknowledgement This work is supported by the Fundamental Research Funds for the Defense of China (NO. D ). References [1] Yaduo Liu, Wei Li, Xiaoqiang Li, A Robust Compressed-Domain Music Fingerprinting Technique Based on MDCT Spectral Entropy, ACTA ELECTRONICA SINICA, 38: , 2010.
8 3034 J. JIANG et al. / Journal of Information & Computational Science 8: 14 (2011) [2] C. S. Lu, Audio Fingerprinting Based on Analyzing Ttme-Frequency Localization of Signals, Multimedia Signal Processing, pp , [3] L. Ghouti and A. Bouridane, A Robust Perceptual Audio Hashing Using Balanced Multiwavelets, In Intemational Conference 011 Acoustics, Speech and Signal Processing, 5 : , [4] Y. Ke, D. Hoiem, R. Sukthankar, Computer Vision for Music Identification, Proceedings of Computer Vision and Pattem Recognition, pp , [5] S. Bahja and M. Covell, Content Fingerprinting Using Wavelets, In Conference on Visual Media Production, pp , [6] S. Bahja and M. Covell, Audio Fingerprinting Combining Computer Vision and Data Stream Processing, In International Conference On Acoustics, Speech and Signal Processing, 2: , [7] G. H. Li, D. F. Wu, J. Zhang, Concept framework for audio information retrieva: ARF, Journal of Computer Science and Technology, 18: , [8] J. Haitsma and T. Kalker, A Highly Robust Audio Fingerprinting System, In Proceedings of International Conference on Music Information Retrieval, [9] Jiming Zheng, Guohua Wei, Yu Wu, New effective method on content based audio feature extraction, COMPUTER ENGINEERING AND APPLICATIONS, 45: , [10] S. Baluja and M. Covell, Waveprint: Efficient Wavelet-based Audio Fingerprinting, Pattern Recognition, 41: , November, [11] Y. Jiao, B. Yang, M. Li and X. Niu, MDCT-Based Perceptual Hashing for Compressed Audio Content Identification, In IEEE Workshop on Multimedia Signal Processing, PP , [12] Jingbing LI, Yingbin WEI, A Novel Watermarking Algorithm Robust to Local Nonlinear Geometrical Attacks, Journal of Computational Information Systems, Vol. 3 (5): , 2007.
Audio Fingerprinting using Fractional Fourier Transform
Audio Fingerprinting using Fractional Fourier Transform Swati V. Sutar 1, D. G. Bhalke 2 1 (Department of Electronics & Telecommunication, JSPM s RSCOE college of Engineering Pune, India) 2 (Department,
More informationMFCC-based perceptual hashing for compressed domain of speech content identification
Available online www.jocpr.com Journal o Chemical and Pharmaceutical Research, 014, 6(7):379-386 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 MFCC-based perceptual hashing or compressed domain
More informationHigh capacity robust audio watermarking scheme based on DWT transform
High capacity robust audio watermarking scheme based on DWT transform Davod Zangene * (Sama technical and vocational training college, Islamic Azad University, Mahshahr Branch, Mahshahr, Iran) davodzangene@mail.com
More informationTHE STATISTICAL ANALYSIS OF AUDIO WATERMARKING USING THE DISCRETE WAVELETS TRANSFORM AND SINGULAR VALUE DECOMPOSITION
THE STATISTICAL ANALYSIS OF AUDIO WATERMARKING USING THE DISCRETE WAVELETS TRANSFORM AND SINGULAR VALUE DECOMPOSITION Mr. Jaykumar. S. Dhage Assistant Professor, Department of Computer Science & Engineering
More informationDWT BASED AUDIO WATERMARKING USING ENERGY COMPARISON
DWT BASED AUDIO WATERMARKING USING ENERGY COMPARISON K.Thamizhazhakan #1, S.Maheswari *2 # PG Scholar,Department of Electrical and Electronics Engineering, Kongu Engineering College,Erode-638052,India.
More informationIntroduction of Audio and Music
1 Introduction of Audio and Music Wei-Ta Chu 2009/12/3 Outline 2 Introduction of Audio Signals Introduction of Music 3 Introduction of Audio Signals Wei-Ta Chu 2009/12/3 Li and Drew, Fundamentals of Multimedia,
More informationSpeech Perceptual Hashing Authentication Algorithm Based on Spectral Subtraction and Energy to Entropy Ratio
International Journal of Network Security, Vol.19, No.5, PP.752-760, Sept. 2017 (DOI: 10.6633/IJNS.201709.19(5).13) 752 Speech Perceptual Hashing Authentication Algorithm Based on Spectral Subtraction
More informationAudio Similarity. Mark Zadel MUMT 611 March 8, Audio Similarity p.1/23
Audio Similarity Mark Zadel MUMT 611 March 8, 2004 Audio Similarity p.1/23 Overview MFCCs Foote Content-Based Retrieval of Music and Audio (1997) Logan, Salomon A Music Similarity Function Based On Signal
More informationDesign and Implementation of an Audio Classification System Based on SVM
Available online at www.sciencedirect.com Procedia ngineering 15 (011) 4031 4035 Advanced in Control ngineering and Information Science Design and Implementation of an Audio Classification System Based
More informationDigital Audio Watermarking With Discrete Wavelet Transform Using Fibonacci Numbers
Digital Audio Watermarking With Discrete Wavelet Transform Using Fibonacci Numbers P. Mohan Kumar 1, Dr. M. Sailaja 2 M. Tech scholar, Dept. of E.C.E, Jawaharlal Nehru Technological University Kakinada,
More informationAudio Watermarking Using Pseudorandom Sequences Based on Biometric Templates
72 JOURNAL OF COMPUTERS, VOL., NO., MARCH 2 Audio Watermarking Using Pseudorandom Sequences Based on Biometric Templates Malay Kishore Dutta Department of Electronics Engineering, GCET, Greater Noida,
More informationApplication of Adaptive Spectral-line Enhancer in Bioradar
International Conference on Computer and Automation Engineering (ICCAE ) IPCSIT vol. 44 () () IACSIT Press, Singapore DOI:.7763/IPCSIT..V44. Application of Adaptive Spectral-line Enhancer in Bioradar FU
More informationBlind Source Separation for a Robust Audio Recognition Scheme in Multiple Sound-Sources Environment
International Conference on Mechatronics, Electronic, Industrial and Control Engineering (MEIC 25) Blind Source Separation for a Robust Audio Recognition in Multiple Sound-Sources Environment Wei Han,2,3,
More informationPoS(CENet2015)037. Recording Device Identification Based on Cepstral Mixed Features. Speaker 2
Based on Cepstral Mixed Features 12 School of Information and Communication Engineering,Dalian University of Technology,Dalian, 116024, Liaoning, P.R. China E-mail:zww110221@163.com Xiangwei Kong, Xingang
More informationClassification of ships using autocorrelation technique for feature extraction of the underwater acoustic noise
Classification of ships using autocorrelation technique for feature extraction of the underwater acoustic noise Noha KORANY 1 Alexandria University, Egypt ABSTRACT The paper applies spectral analysis to
More informationLocalized Robust Audio Watermarking in Regions of Interest
Localized Robust Audio Watermarking in Regions of Interest W Li; X Y Xue; X Q Li Department of Computer Science and Engineering University of Fudan, Shanghai 200433, P. R. China E-mail: weili_fd@yahoo.com
More informationFPGA implementation of DWT for Audio Watermarking Application
FPGA implementation of DWT for Audio Watermarking Application Naveen.S.Hampannavar 1, Sajeevan Joseph 2, C.B.Bidhul 3, Arunachalam V 4 1, 2, 3 M.Tech VLSI Students, 4 Assistant Professor Selection Grade
More informationEvaluation of Audio Compression Artifacts M. Herrera Martinez
Evaluation of Audio Compression Artifacts M. Herrera Martinez This paper deals with subjective evaluation of audio-coding systems. From this evaluation, it is found that, depending on the type of signal
More informationHigh-speed Noise Cancellation with Microphone Array
Noise Cancellation a Posteriori Probability, Maximum Criteria Independent Component Analysis High-speed Noise Cancellation with Microphone Array We propose the use of a microphone array based on independent
More informationHIGH QUALITY AUDIO CODING AT LOW BIT RATE USING WAVELET AND WAVELET PACKET TRANSFORM
HIGH QUALITY AUDIO CODING AT LOW BIT RATE USING WAVELET AND WAVELET PACKET TRANSFORM DR. D.C. DHUBKARYA AND SONAM DUBEY 2 Email at: sonamdubey2000@gmail.com, Electronic and communication department Bundelkhand
More informationHTTP Compression for 1-D signal based on Multiresolution Analysis and Run length Encoding
0 International Conference on Information and Electronics Engineering IPCSIT vol.6 (0) (0) IACSIT Press, Singapore HTTP for -D signal based on Multiresolution Analysis and Run length Encoding Raneet Kumar
More informationTWO ALGORITHMS IN DIGITAL AUDIO STEGANOGRAPHY USING QUANTIZED FREQUENCY DOMAIN EMBEDDING AND REVERSIBLE INTEGER TRANSFORMS
TWO ALGORITHMS IN DIGITAL AUDIO STEGANOGRAPHY USING QUANTIZED FREQUENCY DOMAIN EMBEDDING AND REVERSIBLE INTEGER TRANSFORMS Sos S. Agaian 1, David Akopian 1 and Sunil A. D Souza 1 1Non-linear Signal Processing
More informationEnvironmental Sound Recognition using MP-based Features
Environmental Sound Recognition using MP-based Features Selina Chu, Shri Narayanan *, and C.-C. Jay Kuo * Speech Analysis and Interpretation Lab Signal & Image Processing Institute Department of Computer
More informationDWT based high capacity audio watermarking
LETTER DWT based high capacity audio watermarking M. Fallahpour, student member and D. Megias Summary This letter suggests a novel high capacity robust audio watermarking algorithm by using the high frequency
More informationOpen Access Research of Dielectric Loss Measurement with Sparse Representation
Send Orders for Reprints to reprints@benthamscience.ae 698 The Open Automation and Control Systems Journal, 2, 7, 698-73 Open Access Research of Dielectric Loss Measurement with Sparse Representation Zheng
More informationUniversity of Colorado at Boulder ECEN 4/5532. Lab 1 Lab report due on February 2, 2015
University of Colorado at Boulder ECEN 4/5532 Lab 1 Lab report due on February 2, 2015 This is a MATLAB only lab, and therefore each student needs to turn in her/his own lab report and own programs. 1
More informationDigital Watermarking Using Homogeneity in Image
Digital Watermarking Using Homogeneity in Image S. K. Mitra, M. K. Kundu, C. A. Murthy, B. B. Bhattacharya and T. Acharya Dhirubhai Ambani Institute of Information and Communication Technology Gandhinagar
More informationSound Quality Evaluation for Audio Watermarking Based on Phase Shift Keying Using BCH Code
IEICE TRANS. INF. & SYST., VOL.E98 D, NO.1 JANUARY 2015 89 LETTER Special Section on Enriched Multimedia Sound Quality Evaluation for Audio Watermarking Based on Phase Shift Keying Using BCH Code Harumi
More informationA variable step-size LMS adaptive filtering algorithm for speech denoising in VoIP
7 3rd International Conference on Computational Systems and Communications (ICCSC 7) A variable step-size LMS adaptive filtering algorithm for speech denoising in VoIP Hongyu Chen College of Information
More informationMel Spectrum Analysis of Speech Recognition using Single Microphone
International Journal of Engineering Research in Electronics and Communication Mel Spectrum Analysis of Speech Recognition using Single Microphone [1] Lakshmi S.A, [2] Cholavendan M [1] PG Scholar, Sree
More informationRobust Voice Activity Detection Based on Discrete Wavelet. Transform
Robust Voice Activity Detection Based on Discrete Wavelet Transform Kun-Ching Wang Department of Information Technology & Communication Shin Chien University kunching@mail.kh.usc.edu.tw Abstract This paper
More informationIMAGE TYPE WATER METER CHARACTER RECOGNITION BASED ON EMBEDDED DSP
IMAGE TYPE WATER METER CHARACTER RECOGNITION BASED ON EMBEDDED DSP LIU Ying 1,HAN Yan-bin 2 and ZHANG Yu-lin 3 1 School of Information Science and Engineering, University of Jinan, Jinan 250022, PR China
More informationA New Fake Iris Detection Method
A New Fake Iris Detection Method Xiaofu He 1, Yue Lu 1, and Pengfei Shi 2 1 Department of Computer Science and Technology, East China Normal University, Shanghai 200241, China {xfhe,ylu}@cs.ecnu.edu.cn
More informationComparison of Spectral Analysis Methods for Automatic Speech Recognition
INTERSPEECH 2013 Comparison of Spectral Analysis Methods for Automatic Speech Recognition Venkata Neelima Parinam, Chandra Vootkuri, Stephen A. Zahorian Department of Electrical and Computer Engineering
More informationSound pressure level calculation methodology investigation of corona noise in AC substations
International Conference on Advanced Electronic Science and Technology (AEST 06) Sound pressure level calculation methodology investigation of corona noise in AC substations,a Xiaowen Wu, Nianguang Zhou,
More informationOpen Access Sparse Representation Based Dielectric Loss Angle Measurement
566 The Open Electrical & Electronic Engineering Journal, 25, 9, 566-57 Send Orders for Reprints to reprints@benthamscience.ae Open Access Sparse Representation Based Dielectric Loss Angle Measurement
More informationAuditory modelling for speech processing in the perceptual domain
ANZIAM J. 45 (E) ppc964 C980, 2004 C964 Auditory modelling for speech processing in the perceptual domain L. Lin E. Ambikairajah W. H. Holmes (Received 8 August 2003; revised 28 January 2004) Abstract
More informationColor Image Segmentation in RGB Color Space Based on Color Saliency
Color Image Segmentation in RGB Color Space Based on Color Saliency Chen Zhang 1, Wenzhu Yang 1,*, Zhaohai Liu 1, Daoliang Li 2, Yingyi Chen 2, and Zhenbo Li 2 1 College of Mathematics and Computer Science,
More informationSpeech and Music Discrimination based on Signal Modulation Spectrum.
Speech and Music Discrimination based on Signal Modulation Spectrum. Pavel Balabko June 24, 1999 1 Introduction. This work is devoted to the problem of automatic speech and music discrimination. As we
More informationFrequency Demodulation Analysis of Mine Reducer Vibration Signal
International Journal of Mineral Processing and Extractive Metallurgy 2018; 3(2): 23-28 http://www.sciencepublishinggroup.com/j/ijmpem doi: 10.11648/j.ijmpem.20180302.12 ISSN: 2575-1840 (Print); ISSN:
More informationMultiple Watermarking Scheme Using Adaptive Phase Shift Keying Technique
Multiple Watermarking Scheme Using Adaptive Phase Shift Keying Technique Wen-Yuan Chen, Jen-Tin Lin, Chi-Yuan Lin, and Jin-Rung Liu Department of Electronic Engineering, National Chin-Yi Institute of Technology,
More informationPerformance Analysis of MFCC and LPCC Techniques in Automatic Speech Recognition
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume - 3 Issue - 8 August, 2014 Page No. 7727-7732 Performance Analysis of MFCC and LPCC Techniques in Automatic
More informationResearch on Analysis of Aircraft Echo Characteristics and Classification of Targets in Low-Resolution Radars Based on EEMD
Progress In Electromagnetics Research M, Vol. 68, 61 68, 2018 Research on Analysis of Aircraft Echo Characteristics and Classification of Targets in Low-Resolution Radars Based on EEMD Qiusheng Li *, Huaxia
More informationSpeech/Music Discrimination via Energy Density Analysis
Speech/Music Discrimination via Energy Density Analysis Stanis law Kacprzak and Mariusz Zió lko Department of Electronics, AGH University of Science and Technology al. Mickiewicza 30, Kraków, Poland {skacprza,
More informationIntroduction to Audio Watermarking Schemes
Introduction to Audio Watermarking Schemes N. Lazic and P. Aarabi, Communication over an Acoustic Channel Using Data Hiding Techniques, IEEE Transactions on Multimedia, Vol. 8, No. 5, October 2006 Multimedia
More informationEMC ANALYSIS OF ANTENNAS MOUNTED ON ELECTRICALLY LARGE PLATFORMS WITH PARALLEL FDTD METHOD
Progress In Electromagnetics Research, PIER 84, 205 220, 2008 EMC ANALYSIS OF ANTENNAS MOUNTED ON ELECTRICALLY LARGE PLATFORMS WITH PARALLEL FDTD METHOD J.-Z. Lei, C.-H. Liang, W. Ding, and Y. Zhang National
More informationRhythmic Similarity -- a quick paper review. Presented by: Shi Yong March 15, 2007 Music Technology, McGill University
Rhythmic Similarity -- a quick paper review Presented by: Shi Yong March 15, 2007 Music Technology, McGill University Contents Introduction Three examples J. Foote 2001, 2002 J. Paulus 2002 S. Dixon 2004
More informationRhythm Analysis in Music
Rhythm Analysis in Music EECS 352: Machine Perception of Music & Audio Zafar Rafii, Winter 24 Some Definitions Rhythm movement marked by the regulated succession of strong and weak elements, or of opposite
More informationSound Recognition. ~ CSE 352 Team 3 ~ Jason Park Evan Glover. Kevin Lui Aman Rawat. Prof. Anita Wasilewska
Sound Recognition ~ CSE 352 Team 3 ~ Jason Park Evan Glover Kevin Lui Aman Rawat Prof. Anita Wasilewska What is Sound? Sound is a vibration that propagates as a typically audible mechanical wave of pressure
More informationSONG RETRIEVAL SYSTEM USING HIDDEN MARKOV MODELS
SONG RETRIEVAL SYSTEM USING HIDDEN MARKOV MODELS AKSHAY CHANDRASHEKARAN ANOOP RAMAKRISHNA akshayc@cmu.edu anoopr@andrew.cmu.edu ABHISHEK JAIN GE YANG ajain2@andrew.cmu.edu younger@cmu.edu NIDHI KOHLI R
More informationStudy on OFDM Symbol Timing Synchronization Algorithm
Vol.7, No. (4), pp.43-5 http://dx.doi.org/.457/ijfgcn.4.7..4 Study on OFDM Symbol Timing Synchronization Algorithm Jing Dai and Yanmei Wang* College of Information Science and Engineering, Shenyang Ligong
More informationIMPROVING AUDIO WATERMARK DETECTION USING NOISE MODELLING AND TURBO CODING
IMPROVING AUDIO WATERMARK DETECTION USING NOISE MODELLING AND TURBO CODING Nedeljko Cvejic, Tapio Seppänen MediaTeam Oulu, Information Processing Laboratory, University of Oulu P.O. Box 4500, 4STOINF,
More informationLaser Printer Source Forensics for Arbitrary Chinese Characters
Laser Printer Source Forensics for Arbitrary Chinese Characters Xiangwei Kong, Xin gang You,, Bo Wang, Shize Shang and Linjie Shen Information Security Research Center, Dalian University of Technology,
More informationAutomatic Morse Code Recognition Under Low SNR
2nd International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018) Automatic Morse Code Recognition Under Low SNR Xianyu Wanga, Qi Zhaob, Cheng Mac, * and Jianping
More informationTRANSIENT NOISE REDUCTION BASED ON SPEECH RECONSTRUCTION
TRANSIENT NOISE REDUCTION BASED ON SPEECH RECONSTRUCTION Jian Li 1,2, Shiwei Wang 1,2, Renhua Peng 1,2, Chengshi Zheng 1,2, Xiaodong Li 1,2 1. Communication Acoustics Laboratory, Institute of Acoustics,
More informationA multi-class method for detecting audio events in news broadcasts
A multi-class method for detecting audio events in news broadcasts Sergios Petridis, Theodoros Giannakopoulos, and Stavros Perantonis Computational Intelligence Laboratory, Institute of Informatics and
More informationRhythm Analysis in Music
Rhythm Analysis in Music EECS 352: Machine Perception of Music & Audio Zafar RAFII, Spring 22 Some Definitions Rhythm movement marked by the regulated succession of strong and weak elements, or of opposite
More informationResearch Article A Robust Zero-Watermarking Algorithm for Audio
Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008, Article ID 453580, 7 pages doi:10.1155/2008/453580 Research Article A Robust Zero-Watermarking Algorithm for
More informationVoice Activity Detection for Speech Enhancement Applications
Voice Activity Detection for Speech Enhancement Applications E. Verteletskaya, K. Sakhnov Abstract This paper describes a study of noise-robust voice activity detection (VAD) utilizing the periodicity
More informationPerformance study of Text-independent Speaker identification system using MFCC & IMFCC for Telephone and Microphone Speeches
Performance study of Text-independent Speaker identification system using & I for Telephone and Microphone Speeches Ruchi Chaudhary, National Technical Research Organization Abstract: A state-of-the-art
More informationApplication of Singular Value Energy Difference Spectrum in Axis Trace Refinement
Sensors & Transducers 204 by IFSA Publishing, S. L. http://www.sensorsportal.com Application of Singular Value Energy Difference Spectrum in Ais Trace Refinement Wenbin Zhang, Jiaing Zhu, Yasong Pu, Jie
More informationWatermarking-based Image Authentication with Recovery Capability using Halftoning and IWT
Watermarking-based Image Authentication with Recovery Capability using Halftoning and IWT Luis Rosales-Roldan, Manuel Cedillo-Hernández, Mariko Nakano-Miyatake, Héctor Pérez-Meana Postgraduate Section,
More informationLPSO-WNN DENOISING ALGORITHM FOR SPEECH RECOGNITION IN HIGH BACKGROUND NOISE
LPSO-WNN DENOISING ALGORITHM FOR SPEECH RECOGNITION IN HIGH BACKGROUND NOISE LONGFU ZHOU 1,2, YONGHE HU 1,2,3, SHIYI XIAHOU 3, WEI ZHANG 3, CHAOQUN ZHANG 2 ZHENG LI 2, DAPENG HAO 2 1,The Department of
More informationMulti Modulus Blind Equalizations for Quadrature Amplitude Modulation
Multi Modulus Blind Equalizations for Quadrature Amplitude Modulation Arivukkarasu S, Malar R UG Student, Dept. of ECE, IFET College of Engineering, Villupuram, TN, India Associate Professor, Dept. of
More informationScienceDirect. Unsupervised Speech Segregation Using Pitch Information and Time Frequency Masking
Available online at www.sciencedirect.com ScienceDirect Procedia Computer Science 46 (2015 ) 122 126 International Conference on Information and Communication Technologies (ICICT 2014) Unsupervised Speech
More informationAdaptive Selection of Embedding. Spread Spectrum Watermarking of Compressed Audio
Adaptive Selection of Embedding Locations for Spread Spectrum Watermarking of Compressed Audio Alper Koz and Claude Delpha Laboratory Signals and Systems Univ. Paris Sud-CNRS-SUPELEC SUPELEC Outline Introduction
More informationAudio Watermarking Based on Multiple Echoes Hiding for FM Radio
INTERSPEECH 2014 Audio Watermarking Based on Multiple Echoes Hiding for FM Radio Xuejun Zhang, Xiang Xie Beijing Institute of Technology Zhangxuejun0910@163.com,xiexiang@bit.edu.cn Abstract An audio watermarking
More informationEfficient and Robust Audio Watermarking for Content Authentication and Copyright Protection
Efficient and Robust Audio Watermarking for Content Authentication and Copyright Protection Neethu V PG Scholar, Dept. of ECE, Coimbatore Institute of Technology, Coimbatore, India. R.Kalaivani Assistant
More informationReal time speaker recognition from Internet radio
Real time speaker recognition from Internet radio Radoslaw Weychan, Tomasz Marciniak, Agnieszka Stankiewicz, Adam Dabrowski Poznan University of Technology Faculty of Computing Science Chair of Control
More informationThe main object of all types of watermarking algorithm is to
Transformed Domain Audio Watermarking Using DWT and DCT Mrs. Pooja Saxena and Prof. Sandeep Agrawal poojaetc@gmail.com Abstract The main object of all types of watermarking algorithm is to improve performance
More informationDifferent Approaches of Spectral Subtraction Method for Speech Enhancement
ISSN 2249 5460 Available online at www.internationalejournals.com International ejournals International Journal of Mathematical Sciences, Technology and Humanities 95 (2013 1056 1062 Different Approaches
More informationSpeech Synthesis using Mel-Cepstral Coefficient Feature
Speech Synthesis using Mel-Cepstral Coefficient Feature By Lu Wang Senior Thesis in Electrical Engineering University of Illinois at Urbana-Champaign Advisor: Professor Mark Hasegawa-Johnson May 2018 Abstract
More informationAdaptive filter and noise cancellation*
Advances in Engineering Research, volume 5 2nd Annual International Conference on Energy, Environmental & Sustainable Ecosystem Development (EESED 26) Adaptive filter and noise cancellation* Xing-Tuan
More informationA SCALABLE AUDIO FINGERPRINT METHOD WITH ROBUSTNESS TO PITCH-SHIFTING
12th International Society for Music Information Retrieval Conference (ISMIR 2011) A SCALABLE AUDIO FINGERPRINT METHOD WITH ROBUSTNESS TO PITCH-SHIFTING Sébastien Fenet, Gaël Richard, Yves Grenier Institut
More informationAn Improvement for Hiding Data in Audio Using Echo Modulation
An Improvement for Hiding Data in Audio Using Echo Modulation Huynh Ba Dieu International School, Duy Tan University 182 Nguyen Van Linh, Da Nang, VietNam huynhbadieu@dtu.edu.vn ABSTRACT This paper presents
More informationAN ANALYSIS OF SPEECH RECOGNITION PERFORMANCE BASED UPON NETWORK LAYERS AND TRANSFER FUNCTIONS
AN ANALYSIS OF SPEECH RECOGNITION PERFORMANCE BASED UPON NETWORK LAYERS AND TRANSFER FUNCTIONS Kuldeep Kumar 1, R. K. Aggarwal 1 and Ankita Jain 2 1 Department of Computer Engineering, National Institute
More informationCepstrum alanysis of speech signals
Cepstrum alanysis of speech signals ELEC-E5520 Speech and language processing methods Spring 2016 Mikko Kurimo 1 /48 Contents Literature and other material Idea and history of cepstrum Cepstrum and LP
More informationJournal of mathematics and computer science 11 (2014),
Journal of mathematics and computer science 11 (2014), 137-146 Application of Unsharp Mask in Augmenting the Quality of Extracted Watermark in Spatial Domain Watermarking Saeed Amirgholipour 1 *,Ahmad
More informationMulti-GI Detector with Shortened and Leakage Correlation for the Chinese DTMB System. Fengkui Gong, Jianhua Ge and Yong Wang
788 IEEE Transactions on Consumer Electronics, Vol. 55, No. 4, NOVEMBER 9 Multi-GI Detector with Shortened and Leakage Correlation for the Chinese DTMB System Fengkui Gong, Jianhua Ge and Yong Wang Abstract
More informationAtmospheric Signal Processing. using Wavelets and HHT
Journal of Computations & Modelling, vol.1, no.1, 2011, 17-30 ISSN: 1792-7625 (print), 1792-8850 (online) International Scientific Press, 2011 Atmospheric Signal Processing using Wavelets and HHT N. Padmaja
More informationAudio Watermarking Scheme in MDCT Domain
Santosh Kumar Singh and Jyotsna Singh Electronics and Communication Engineering, Netaji Subhas Institute of Technology, Sec. 3, Dwarka, New Delhi, 110078, India. E-mails: ersksingh_mtnl@yahoo.com & jsingh.nsit@gmail.com
More informationAnalysis of LMS Algorithm in Wavelet Domain
Conference on Advances in Communication and Control Systems 2013 (CAC2S 2013) Analysis of LMS Algorithm in Wavelet Domain Pankaj Goel l, ECE Department, Birla Institute of Technology Ranchi, Jharkhand,
More informationReversible data hiding based on histogram modification using S-type and Hilbert curve scanning
Advances in Engineering Research (AER), volume 116 International Conference on Communication and Electronic Information Engineering (CEIE 016) Reversible data hiding based on histogram modification using
More informationAnalysis on detection probability of satellite-based AIS affected by parameter estimation
2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) Analysis on detection probability of satellite-based AIS affected by parameter estimation Xiaofeng
More informationREpeating Pattern Extraction Technique (REPET)
REpeating Pattern Extraction Technique (REPET) EECS 32: Machine Perception of Music & Audio Zafar RAFII, Spring 22 Repetition Repetition is a fundamental element in generating and perceiving structure
More informationHigh Capacity Audio Watermarking Based on Fibonacci Series
2017 IJSRST Volume 3 Issue 8 Print ISSN: 2395-6011 Online ISSN: 2395-602X Themed Section: Scienceand Technology High Capacity Audio Watermarking Based on Fibonacci Series U. Hari krishna 1, M. Sreedhar
More informationFeature Extraction of Acoustic Emission Signals from Low Carbon Steel. Pitting Based on Independent Component Analysis and Wavelet Transforming
17th World Conference on Nondestructive Testing, 25-28 Oct 2008, Shanghai, China Feature Extraction of Acoustic Emission Signals from Low Carbon Steel Pitting Based on Independent Component Analysis and
More informationResearch on Extracting BPM Feature Values in Music Beat Tracking Algorithm
Research on Extracting BPM Feature Values in Music Beat Tracking Algorithm Yan Zhao * Hainan Tropical Ocean University, Sanya, China *Corresponding author(e-mail: yanzhao16@163.com) Abstract With the rapid
More informationDetection of Rail Fastener Based on Wavelet Decomposition and PCA Ben-yu XIAO 1, Yong-zhi MIN 1,* and Hong-feng MA 2
2017 2nd International Conference on Information Technology and Management Engineering (ITME 2017) ISBN: 978-1-60595-415-8 Detection of Rail Fastener Based on Wavelet Decomposition and PCA Ben-yu XIAO
More informationA Novel Algorithm for Hand Vein Recognition Based on Wavelet Decomposition and Mean Absolute Deviation
Sensors & Transducers, Vol. 6, Issue 2, December 203, pp. 53-58 Sensors & Transducers 203 by IFSA http://www.sensorsportal.com A Novel Algorithm for Hand Vein Recognition Based on Wavelet Decomposition
More informationGammatone Cepstral Coefficient for Speaker Identification
Gammatone Cepstral Coefficient for Speaker Identification Rahana Fathima 1, Raseena P E 2 M. Tech Student, Ilahia college of Engineering and Technology, Muvattupuzha, Kerala, India 1 Asst. Professor, Ilahia
More informationAn Adaptive Algorithm for Speech Source Separation in Overcomplete Cases Using Wavelet Packets
Proceedings of the th WSEAS International Conference on Signal Processing, Istanbul, Turkey, May 7-9, 6 (pp4-44) An Adaptive Algorithm for Speech Source Separation in Overcomplete Cases Using Wavelet Packets
More informationEnhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis
Enhancement of Speech Signal Based on Improved Minima Controlled Recursive Averaging and Independent Component Analysis Mohini Avatade & S.L. Sahare Electronics & Telecommunication Department, Cummins
More informationModulation Spectrum Power-law Expansion for Robust Speech Recognition
Modulation Spectrum Power-law Expansion for Robust Speech Recognition Hao-Teng Fan, Zi-Hao Ye and Jeih-weih Hung Department of Electrical Engineering, National Chi Nan University, Nantou, Taiwan E-mail:
More informationECE 556 BASICS OF DIGITAL SPEECH PROCESSING. Assıst.Prof.Dr. Selma ÖZAYDIN Spring Term-2017 Lecture 2
ECE 556 BASICS OF DIGITAL SPEECH PROCESSING Assıst.Prof.Dr. Selma ÖZAYDIN Spring Term-2017 Lecture 2 Analog Sound to Digital Sound Characteristics of Sound Amplitude Wavelength (w) Frequency ( ) Timbre
More informationAdvanced Functions of Java-DSP for use in Electrical and Computer Engineering Senior Level Courses
Advanced Functions of Java-DSP for use in Electrical and Computer Engineering Senior Level Courses Andreas Spanias Robert Santucci Tushar Gupta Mohit Shah Karthikeyan Ramamurthy Topics This presentation
More informationJournal of American Science 2015;11(7)
Design of Efficient Noise Reduction Scheme for Secure Speech Masked by Signals Hikmat N. Abdullah 1, Saad S. Hreshee 2, Ameer K. Jawad 3 1. College of Information Engineering, AL-Nahrain University, Baghdad-Iraq
More informationNoise Removal of Spaceborne SAR Image Based on the FIR Digital Filter
Noise Removal of Spaceborne SAR Image Based on the FIR Digital Filter Wei Zhang & Jinzhong Yang China Aero Geophysical Survey & Remote Sensing Center for Land and Resources, Beijing 100083, China Tel:
More informationApplications of Music Processing
Lecture Music Processing Applications of Music Processing Christian Dittmar International Audio Laboratories Erlangen christian.dittmar@audiolabs-erlangen.de Singing Voice Detection Important pre-requisite
More informationAn Improved Voice Activity Detection Based on Deep Belief Networks
e-issn 2455 1392 Volume 2 Issue 4, April 2016 pp. 676-683 Scientific Journal Impact Factor : 3.468 http://www.ijcter.com An Improved Voice Activity Detection Based on Deep Belief Networks Shabeeba T. K.
More information