Speech Enhancement Based on Adaptive Harmonic Model Using Maximum Likelihood Method
Authors: 
Xizhong Shen, Lei ZhangAuthors Info & Claims
Xizhong Shen, Lei ZhangAuthors Info & ClaimsPages 212 - 217
Abstract
Speech enhancement is a hot topic in the modern society due to its extensive applications such as automatic speech recognition, mobile communication, etc. Spectral subtraction is a very valid and direct denoising algorithm, but it is still needed to be further developed due to complex application. In this paper, we firstly assume adaptive harmonic model to model the speech signal. Speech enhancement is then achieved by spectral subtraction. To further enhance the speech intelligibility, we attempt to estimate the harmonic model. Maximum likelihood method is considered to derive the phase and amplitude update formulae of the modelled harmonic signal, which are aimed to depress the distortion due to spectral subtraction. Different from convention spectral subtraction, both the amplitude and phase parameters of adaptive harmonic model are combined to be updated. We assume the additive noise is correlated along the time sequence. By the optimal solution of maximum likelihood method, we obtain the updated version of speech enhancement. Simulation results show the effectiveness of the new algorithm, and further improvement of spectral subtraction.
References
[1]
Bahja F, Martino J D, Elhaj E I, Aboutajdine D (2015) An overview of the CATE algorithms for real-time pitch determination. Signal Image & Video Processing, 9 (3): 589--599.
[2]
Boll S F (1979) Suppression of acoustic noise in speech using spectral subtraction. IEEE Transaction on Acoustics, Speech and Signal Processing, vol. 27, pp: 113--130.
[3]
Gold B, Morgan N, Ellis D (2011) Speech and Audio Signal Processing: Processing and Perception of Speech and Music. John Wiley & Sons.
[4]
Kamath S, Loizou P C (2002) A multi-band spectral subtraction method for enhancing speech corrupted by colored noise[C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando, FL, USA: IV-4164--IV-4164.
[5]
Kay S M (1993) Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory. Prentice Hall.
[6]
Kulmer J, Mowlaee P, Watanabe M K (2014) A probabilistic approach for phase estimation in single-channel speech enhancement using von mises phase priors. IEEE Workshop on Machine Learning for Signal Processing, Sept. 2014.
[7]
Loizou P C, Ma J (2011) Extending the articulation index to account for non-linear distortions introduced by noise suppression algorithms. Journal of the Acoustical Society of America, 130(2), pp: 986--995.
[8]
Martin R (2001) Noise Power Spectral Density Estimation Based on Optimal Smoothing and Minimum Statistics. IEEE Transactions on Speech and Audio Processing 9(5):504--512.
[9]
Mowlaee P, Kulmer J (2015) Harmonic Phase Estimation in Single-Channel Speech Enhancement Using Phase Decomposition and SNR Information. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 23, No. 9, pp: 1521--1532.
[10]
Schonhoff T, Giordano A A (2007) Detection and Estimation Theory and Its Aplications. Prentice Hall.
[11]
Sugiyama A, Miyahara R (2013) Phase randomization -- a new paradigm for single-channel signal enhancement. ICASSP 2013, pp: 7487--7491.
[12]
Wang D, Lim J (1982) The unimportance of phase in speech enhancement. IEEE Transaction on Acoustics, Speech and Signal Processing, vol. 30, no. 4, pp: 679--681.
Index Terms
- Speech Enhancement Based on Adaptive Harmonic Model Using Maximum Likelihood Method
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Published In
June 2020
383 pages
ISBN:9781450376877
DOI:10.1145/3408127
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- University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
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Association for Computing Machinery
New York, NY, United States
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Published: 10 September 2020
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ICDSP 2020
ICDSP 2020: 2020 4th International Conference on Digital Signal Processing
June 19 - 21, 2020
Chengdu, China
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