Journal ArticleDOI
Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator
Yariv Ephraim,David Malah +1 more
TLDR
In this article, a system which utilizes a minimum mean square error (MMSE) estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the "spectral subtraction" algorithm.Abstract:
This paper focuses on the class of speech enhancement systems which capitalize on the major importance of the short-time spectral amplitude (STSA) of the speech signal in its perception. A system which utilizes a minimum mean-square error (MMSE) STSA estimator is proposed and then compared with other widely used systems which are based on Wiener filtering and the "spectral subtraction" algorithm. In this paper we derive the MMSE STSA estimator, based on modeling speech and noise spectral components as statistically independent Gaussian random variables. We analyze the performance of the proposed STSA estimator and compare it with a STSA estimator derived from the Wiener estimator. We also examine the MMSE STSA estimator under uncertainty of signal presence in the noisy observations. In constructing the enhanced signal, the MMSE STSA estimator is combined with the complex exponential of the noisy phase. It is shown here that the latter is the MMSE estimator of the complex exponential of the original phase, which does not affect the STSA estimation. The proposed approach results in a significant reduction of the noise, and provides enhanced speech with colorless residual noise. The complexity of the proposed algorithm is approximately that of other systems in the discussed class.read more
Citations
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Journal ArticleDOI
An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech
TL;DR: A short-time objective intelligibility measure (STOI) is presented, which shows high correlation with the intelligibility of noisy and time-frequency weighted noisy speech (e.g., resulting from noise reduction) of three different listening experiments and showed better correlation with speech intelligibility compared to five other reference objective intelligible models.
Journal ArticleDOI
Noise power spectral density estimation based on optimal smoothing and minimum statistics
TL;DR: An unbiased noise estimator is developed which derives the optimal smoothing parameter for recursive smoothing of the power spectral density of the noisy speech signal by minimizing a conditional mean square estimation error criterion in each time step.
Journal ArticleDOI
A statistical model-based voice activity detection
TL;DR: An effective hang-over scheme which considers the previous observations by a first-order Markov process modeling of speech occurrences is proposed which shows significantly better performances than the G.729B VAD in low signal-to-noise ratio (SNR) and vehicular noise environments.
Journal ArticleDOI
A regression approach to speech enhancement based on deep neural networks
TL;DR: The proposed DNN approach can well suppress highly nonstationary noise, which is tough to handle in general, and is effective in dealing with noisy speech data recorded in real-world scenarios without the generation of the annoying musical artifact commonly observed in conventional enhancement methods.
Journal ArticleDOI
Supervised Speech Separation Based on Deep Learning: An Overview
DeLiang Wang,Jitong Chen +1 more
TL;DR: A comprehensive overview of deep learning-based supervised speech separation can be found in this paper, where three main components of supervised separation are discussed: learning machines, training targets, and acoustic features.
References
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Table of Integrals, Series, and Products
TL;DR: Combinations involving trigonometric and hyperbolic functions and power 5 Indefinite Integrals of Special Functions 6 Definite Integral Integral Functions 7.Associated Legendre Functions 8 Special Functions 9 Hypergeometric Functions 10 Vector Field Theory 11 Algebraic Inequalities 12 Integral Inequality 13 Matrices and related results 14 Determinants 15 Norms 16 Ordinary differential equations 17 Fourier, Laplace, and Mellin Transforms 18 The z-transform
Journal ArticleDOI
Suppression of acoustic noise in speech using spectral subtraction
TL;DR: A stand-alone noise suppression algorithm that resynthesizes a speech waveform and can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
Proceedings ArticleDOI
Enhancement of speech corrupted by acoustic noise
TL;DR: This paper describes a method for enhancing speech corrupted by broadband noise based on the spectral noise subtraction method, which can automatically adapt to a wide range of signal-to-noise ratios, as long as a reasonable estimate of the noise spectrum can be obtained.
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An Introduction to Statistical Communication Theory
TL;DR: This IEEE Classic Reissue provides at an advanced level, a uniquely fundamental exposition of the applications of Statistical Communication Theory to a vast spectrum of important physical problems.