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Speech Enhancement: Theory and Practice

TLDR
Clear and concise, this book explores how human listeners compensate for acoustic noise in noisy environments and suggests steps that can be taken to realize the full potential of these algorithms under realistic conditions.
Abstract
With the proliferation of mobile devices and hearing devices, including hearing aids and cochlear implants, there is a growing and pressing need to design algorithms that can improve speech intelligibility without sacrificing quality. Responding to this need, Speech Enhancement: Theory and Practice, Second Edition introduces readers to the basic problems of speech enhancement and the various algorithms proposed to solve these problems. Updated and expanded, this second edition of the bestselling textbook broadens its scope to include evaluation measures and enhancement algorithms aimed at improving speech intelligibility. Fundamentals, Algorithms, Evaluation, and Future Steps Organized into four parts, the book begins with a review of the fundamentals needed to understand and design better speech enhancement algorithms. The second part describes all the major enhancement algorithms and, because these require an estimate of the noise spectrum, also covers noise estimation algorithms. The third part of the book looks at the measures used to assess the performance, in terms of speech quality and intelligibility, of speech enhancement methods. It also evaluates and compares several of the algorithms. The fourth part presents binary mask algorithms for improving speech intelligibility under ideal conditions. In addition, it suggests steps that can be taken to realize the full potential of these algorithms under realistic conditions. Whats New in This Edition Updates in every chapter A new chapter on objective speech intelligibility measures A new chapter on algorithms for improving speech intelligibility Real-world noise recordings (on accompanying CD) MATLAB code for the implementation of intelligibility measures (on accompanying CD) MATLAB and C/C++ code for the implementation of algorithms to improve speech intelligibility (on accompanying CD) Valuable Insights from a Pioneer in Speech Enhancement Clear and concise, this book explores how human listeners compensate for acoustic noise in noisy environments. Written by a pioneer in speech enhancement and noise reduction in cochlear implants, it is an essential resource for anyone who wants to implement or incorporate the latest speech enhancement algorithms to improve the quality and intelligibility of speech degraded by noise. Includes a CD with Code and Recordings The accompanying CD provides MATLAB implementations of representative speech enhancement algorithms as well as speech and noise databases for the evaluation of enhancement algorithms.

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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

Evaluation of Objective Quality Measures for Speech Enhancement

TL;DR: The evaluation of correlations of several objective measures with these three subjective rating scales is reported on and several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.
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

On training targets for supervised speech separation

TL;DR: Results in various test conditions reveal that the two ratio mask targets, the IRM and the FFT-MASK, outperform the other targets in terms of objective intelligibility and quality metrics, and that masking based targets, in general, are significantly better than spectral envelope based targets.
Journal ArticleDOI

Supervised Speech Separation Based on Deep Learning: An Overview

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.
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