本内斯蒂关于语音增强的著作，值得大家下载下来仔细研究

Speech Enhancement Techniques for Digital Hearing Aids

【论文：麦克风阵列增强】Speech Enhancement Based on the General Transfer Function GSC and Postfiltering...-附件资源

Title: Speech Enhancement: Theory and Practice, 2nd Edition Author: Philipos C. Loizou Length: 711 pages Edition: 2 Language: English Publisher: CRC Press Publication Date: 2013-02-25 ISBN-10: 1466504218 ISBN-13: 9781466504219 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. What’s 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. Table of Contents Chapter 1 Introduction Chapter 2 Discrete-Time Signal Processing and Short-Time Fourier Analysis Chapter 3 Speech Production and Perception Chapter 4 Noise Compensation by Human Listeners Chapter 5 Spectral-Subtractive Algorithms Chapter 6 Wiener Filtering Chapter 7 Statistical-Model-Based Methods Chapter 8 Subspace Algorithms Chapter 9 Noise-Estimation Algorithms Chapter 10 Evaluating Performance of Speech Enhancement Algorithms Chapter 11 Objective Quality and Intelligibility Measures Chapter 12 Comparison of Speech Enhancement Algorithms Chapter 13 Algorithms That Can Improve Speech Intelligibility Appendix A: Special Functions and Integrals Appendix B: Derivation of the MMSE Estimator Appendix C: MATLAB ® Code and Speech/Noise Databases

Speech enhancement based on adaptive wavelet denoising on multitaper spectrum matlab

书名：SPEECH ENHANCEMENT Theory and Practice 很好的书！学习语音增强的同学可以看看。

做语音增强了两种基本方法，kalman滤波和维纳滤波的方法。希望对学习增强的同学有帮助。

基于神经网络的语音分离必读论文和教程列表 该存储库包含用于纯语音分离和多模式语音分离的论文。 通过Kai Li（如果有任何建议，请与我联系！电子邮件： ）。 提示：对于语音分离初学者，我建议您阅读“深度群集”和“ PIT＆uPIT”作品，这将有助于理解问题。 如果您发现以下某些文章的代码，欢迎添加链接。 纯语音分离 :check_mark: [用于单声道信号源分离的蒙版和深度递归神经网络的联合优化，黄波森，TASLP 2015] :check_mark: [用于单声道语音分离的复杂比率掩盖，DS Williamson，TASLP，2015年] :check_mark: [深度聚类：用于分段和分离的区分嵌入，JR Hershey，ICASSP 2016] :check_mark: [使用深度聚类的单通道多扬声器分离，Y Isik，Interspeech 2016] :check_mark: [用于与说话者无关的多说话者语音分离的深度模型的置换

言语增强 文件DCUnet.ipynb源自的程序。 已进行了一些修改，以使该程序可以在2021年2月在Colab的版本上运行。具体地说，笔记本电脑使用以下程序包： torchaudio == 0.5.0a0 + 738ccba tqdm == 4.56.2 numpy == 1.19.2 pesq == 0.0.2 scipy == 1.4.1 matplotlib == 3.3.1 火炬== 1.5.1 DCUnet16.ipynb：在16kHz波形文件上进行训练和测试DCUnet48.ipynb：在48kHz波形文件上进行训练和测试（类似于 ）

用于单通道语音增强的深噪声抑制模型的比较评估 考虑到视频会议系统和流传输工具的日益增加的使用，具有计算有效和有效的语音增强器变得有利和必要。 Microsoft DNS挑战极大地促进了该领域的创新，但仍有很大的改进空间。 这项工作比较了此挑战中提出的两种用于语音增强的深度学习模型：NSNet2和双信号转换LSTM网络（DTLN）。 在基于混响时间RT60和信噪比（SNR）规范的两种对比条件下，分别使用两个数据集和三种不同的以语音质量为中心的措施对这两种模型进行了比较：语音质量的感知评估（PESQ），深噪声抑制平均意见分数（DNSMOS）和虚拟语音质量目标听众（ViSQOL）。 概述 这是“单声道语音增强的深噪声抑制模型的比较评估”研究报告的伴随代码，该研究由EstebanGómez进行，该研究是巴塞罗那Pompeu Fabra大学的声音和音乐计算硕士学位的学生，是音乐信息的一部分检索过程。