Computational Auditory Scene Analysis: Principles, Algorhythms And Applications计算机听觉分析原理、算法与应用 夸克云 txt pdf umd tct 百度云 kindle 下载

　　How can we engineer systems capable of "cocktail party" listening?

Human listeners are able to perceptually segregate one sound source from an acoustic mixture, such as a single voice from a mixture of other voices and music at a busy cocktail party. How can we engineer "machine listening" systems that achieve this perceptual feat?

Albert Bregman's book Auditory Scene Analysis, published in 1990, drew an analogy between the perception of auditory scenes and visual scenes, and described a coherent framework for understanding the perceptual organization of sound. His account has stimulated much interest in computational studies of hearing. Such studies are motivated in part by the demand for practical sound separation systems, which have many applications including noise-robust automatic speech recognition, hearing prostheses, and automatic music tran*ion. This emerging field has become known as computational auditory scene analysis (CASA).

Computational Auditory Scene Analysis: Principles, Algorithms, and Applications provides a comprehensive and coherent account of the state of the art in CASA, in terms of the underlying principles, the algorithms and system architectures that are employed, and the potential applications of this exciting new technology. With a Foreword by Bregman, its chapters are written by leading researchers and cover a wide range of topics including:

·Estimation of multiple fundamental frequencies

·Feature-based and model-based approaches to CASA

·Sound separation based on spatial location

·Processing for reverberant environments

·Segregation of speech and musical signals

·Automatic speech recognition in noisy environments

·Neural and perceptual modeling of auditory organization

The text is written at a level that will be accessible to graduate students and researchers from related science and engineering disciplines. The extensive bibliography accompanying each chapter will also make this book a valuable reference source. A web site accompanying the text, http://www.casabook.org, features software tools and sound demonstrations.

Foreword.

Preface.

Contributors.

Acronyms.

1. Fundamentals of Computational Auditory Scene Analysis (DeLiang Wang and Guy J. Brown).

　1.1 Human Auditory Scene Analysis.

　　1.1.1 Structure and Function of the Auditory System.

　　1.1.2 Perceptual Organization of Simple Stimuli.

　　1.1.3 Perceptual Segregation of Speech from Other Sounds.

　　1.1.4 Perceptual Mechanisms.

　1.2 Computational Auditory Scene Analysis (CASA).

　　1.2.1 What Is CASA?

　　1.2.2 What Is the Goal of CASA?

　　1.2.3 Why CASA?

　1.3 Basics of CASA Systems.

　　1.3.1 System Architecture.

　　1.3.2 Cochleagram.

　　1.3.3 Correlogram.

　　1.3.4 Cross-Correlogram.

　　1.3.5 Time-Frequency Masks.

　　1.3.6 Resynthesis.

　1.4 CASA Evaluation.

　　1.4.1 Evaluation Criteria.

　　1.4.2 Corpora.

　1.5 Other Sound Separation Approaches.

　1.6 A Brief History of CASA (Prior to 2000).

　　1.6.1 Monaural CASA Systems.

　　1.6.2 Binaural CASA Systems.

　　1.6.3 Neural CASA Models.

　1.7 Conclusions 36

　Acknowledgments.

　References.

2. Multiple F0 Estimation (Alain de Cheveigné).

　2.1 Introduction.

　2.2 Signal Models.

　2.3 Single-Voice F0 Estimation.

　　2.3.1 Spectral Approach.

　　2.3.2 Temporal Approach.

　　2.3.3 Spectrotemporal Approach.

　2.4 Multiple-Voice F0 Estimation.

　　2.4.1 Spectral Approach.

　　2.4.2 Temporal Approach.

　　2.4.3 Spectrotemporal Approach.

　2.5 Issues.

　　2.5.1 Spectral Resolution.

　　2.5.2 Temporal Resolution.

　　2.5.3 Spectrotemporal Resolution.

　2.6 Other Sources of Information.

　　2.6.1 Temporal and Spectral Continuity.

　　2.6.2 Instrument Models.

　　2.6.3 Learning-Based Techniques.

　2.7 Estimating the Number of Sources.

　2.8 Evaluation.

　2.9 Application Scenarios.

　2.10 Conclusion.

　Acknowledgments.

　References.

3. Feature-Based Speech Segregation (DeLiang Wang).

　3.1 Introduction.

　3.2 Feature Extraction.

　　3.2.1 Pitch Detection.

　　3.2.2 Onset and Offset Detection.

　　3.2.3 Amplitude Modulation Extraction.

　　3.2.4 Frequency Modulation Detection.

　3.3 Auditory Segmentation.

　　3.3.1 What Is the Goal of Auditory Segmentation?

　　3.3.2 Segmentation Based on Cross-Channel Correlation and Temporal Continuity.

　　3.3.3 Segmentation Based on Onset and Offset Analysis.

　3.4 Simultaneous Grouping.

　　3.4.1 Voiced Speech Segregation.

　　3.4.2 Unvoiced Speech Segregation.

　3.5 Sequential Grouping.

　　3.5.1 Spectrum-Based Sequential Grouping.

　　3.5.2 Pitch-Based Sequential Grouping.

　　3.5.3 Model-Based Sequential Grouping.

　3.6 Discussion.

　Acknowledgments.

　References.

4. Model-Based Scene Analysis (Daniel P. W. Ellis).

5. Binaural Sound Localization (Richard M. Stern, Guy J. Brown, and DeLiang Wang).

6. Localization-Based Grouping (Albert S. Feng and Douglas L. Jones).

7. Reverberation (Guy J. Brown and Kalle J. Palomäki).

8. Analysis of Musical Audio Signals (Masataka Goto).

9. Robust Automatic Speech Recognition (Jon Barker).

10. Neural and Perceptual Modeling (Guy J. Brown and DeLiang Wang).

Index.

Editors DeLIANG WANG and GUY J. BROWN are well-known for their contributions to the development of CASA. Wang is a Professor in the Department of Computer Science and Engineering and the Center for Cognitive Science at The Ohio State University. He is an

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