Auditory scene analysis

Abstract: Preface to the Fifth Edition The nature of sound and the structure and function of the auditory system Absolute thresholds Frequency selectivity, masking and the critical band The perception of loudness Temporal processing in the auditory system Pitch perception Space perception Auditory pattern and object perception Speech perception Practical applications References Glossary Index

Abstract: Auditory Scene Analysis addresses the problem of hearing complex auditory environments, using a series of creative analogies to describe the process required of the human auditory system as it analyzes mixtures of sounds to recover descriptions of individual sounds. In a unified and comprehensive way, Bregman establishes a theoretical framework that integrates his findings with an unusually wide range of previous research in psychoacoustics, speech perception, music theory and composition, and computer modeling.

Abstract: The world is full of sources of sound. As I write this review, I can hear the humming of the word processor, the creaking of a door in the wind, the distant rumble of an aeroplane, the passage of a car close by, a bird twittering, my neighbour talking on his doorstep, music from his son’s hi-fi, and someone speaking on the radio in the next room. Although each source generates a particular pattern of changes in air-pressure, the changes have summed together by the time they reach my ears, yet I perceive each source distinctly. What principles of perceptual grouping and segregation do listeners use to partition such mixtures of sound? Which principles are applied automatically to all sounds? Which are specialized for particular classes of sound, such as speech? In what ways have the principles been exploited in musical composition? These are the major concerns of this lengthy, scholarly, but readable book. Bregman’s approach is functional not physiological, empirical not computational. He provides a comprehensive review and interpretation of perceptual experiments up to about 1989, so his book pre-dates recent attempts to implement auditory grouping principles in computational models and to find a physiological substrate for them. One important distinction is sustained throughout the book. Bregman argues that there are two kinds of principle for auditory grouping and segregation: “schema-based’’ and “primitive”. Schema-based principles are specific to particular types of source. They are learnt by listeners, and their application is under attentional control. One example may be the use of the knowledge of the timbre of an instrument to follow its part in an ensemble. Another example may be the use of phonetic knowledge to integrate acoustic cues in speech perception. Primitive grouping principles, in contrast, are innate, learnt through evolution. They automatically exploit fundamental physical properties of sounds and sound sources. For example: the sizes of resonators generally change slowly; they often generate energy simultaneously over a wide frequency range; when they vibrate, they create energy at the discrete

Abstract: 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 Cheveigne). 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). 4.1 Introduction. 4.2 Source Separation as Inference. 4.3 Hidden Markov Models. 4.4 Aspects of Model-Based Systems. 4.4.1 Constraints: Types and Representations. 4.4.2 Fitting Models. 4.4.3 Generating Output. 4.5 Discussion. 4.5.1 Unknown Interference. 4.5.2 Ambiguity and Adaptation. 4.5.3 Relations to Other Separation Approaches. 4.6 Conclusions. References. 5. Binaural Sound Localization (Richard M. Stern, Guy J. Brown, and DeLiang Wang). 5.1 Introduction. 5.2 Physical and Physiological Mechanisms Underlying Auditory Localization. 5.2.1 Physical Cues. 5.2.2 Physiological Estimation of ITD and IID. 5.3 Spatial Perception of Single Sources. 5.3.1 Sensitivity to Differences in Interaural Time and Intensity. 5.3.2 Lateralization of Single Sources. 5.3.3 Localization of Single Sources. 5.3.4 The Precedence Effect. 5.4 Spatial Perception of Multiple Sources. 5.4.1 Localization of Multiple Sources. 5.4.2 Binaural Signal Detection. 5.5 Models of Binaural Perception. 5.5.1 Classical Models of Binaural Hearing. 5.5.2 Cross-Correlation-Based Models of Binaural Interaction. 5.5.3 Some Extensions to Cross-Correlation-Based Binaural Models. 5.6 Multisource Sound Localization. 5.6.1 Estimating Source Azimuth from Interaural Cross-Correlation. 5.6.2 Methods for Resolving Azimuth Ambiguity. 5.6.3 Localization of Moving Sources. 5.7 General Discussion. Acknowledgments. References. 6. Localization-Based Grouping (Albert S. Feng and Douglas L. Jones). 6.1 Introduction. 6.2 Classical Beamforming Techniques. 6.2.1 Fixed Beamforming Techniques. 6.2.2 Adaptive Beamforming Techniques. 6.2.3 Independent Component Analysis Techniques. 6.2.4 Other Localization-Based Techniques. 6.3 Location-Based Grouping Using Interaural Time Difference Cue. 6.4 Location-Based Grouping Using Interaural Intensity Difference Cue. 6.5 Location-Based Grouping Using Multiple Binaural Cues. 6.6 Discussion and Conclusions. Acknowledgments. References. 7. Reverberation (Guy J. Brown and Kalle J. Palomaki). 7.1 Introduction. 7.2 Effects of Reverberation on Listeners. 7.2.1 Speech Perception. 7.2.2 Sound Localization. 7.2.3 Source Separation and Signal Detection. 7.2.4 Distance Perception. 7.2.5 Auditory Spatial Impression. 7.3 Effects of Reverberation on Machines. 7.4 Mechanisms Underlying Robustness to Reverberation in Human Listeners. 7.4.1 The Role of Slow Temporal Modulations in Speech Perception. 7.4.2 The Binaural Advantage. 7.4.3 The Precedence Effect. 7.4.4 Perceptual Compensation for Spectral Envelope Distortion. 7.5 Reverberation-Robust Acoustic Processing. 7.5.1 Dereverberation. 7.5.2 Reverberation-Robust Acoustic Features. 7.5.3 Reverberation Masking. 7.6 CASA and Reverberation. 7.6.1 Systems Based on Directional Filtering. 7.6.2 CASA for Robust ASR in Reverberant Conditions. 7.6.3 Systems that Use Multiple Cues. 7.7 Discussion and Conclusions. Acknowledgments. References. 8. Analysis of Musical Audio Signals (Masataka Goto). 8.1 Introduction. 8.2 Music Scene Description. 8.2.1 Music Scene Descriptions. 8.2.2 Difficulties Associated with Musical Audio Signals. 8.3 Estimating Melody and Bass Lines. 8.3.1 PreFEst-front-end: Forming the Observed Probability Density Functions. 8.3.2 PreFEst-core: Estimating the F0's Probability Density Function. 8.3.3 PreFEst-back-end: Sequential F0 Tracking by Multiple-Agent Architecture. 8.3.4 Other Methods. 8.4 Estimating Beat Structure. 8.4.1 Estimating Period and Phase. 8.4.2 Dealing with Ambiguity. 8.4.3 Using Musical Knowledge. 8.5 Estimating Chorus Sections and Repeated Sections. 8.5.1 Extracting Acoustic Features and Calculating Their Similarity. 8.5.2 Finding Repeated Sections. 8.5.3 Grouping Repeated Sections. 8.5.4 Detecting Modulated Repetition. 8.5.5 Selecting Chorus Sections. 8.5.6 Other Methods. 8.6 Discussion and Conclusions. 8.6.1 Importance. 8.6.2 Evaluation Issues. 8.6.3 Future Directions. References. 9. Robust Automatic Speech Recognition (Jon Barker). 9.1 Introduction. 9.2 ASA and Speech Perception in Humans. 9.2.1 Speech Perception and Simultaneous Grouping. 9.2.2 Speech Perception and Sequential Grouping. 9.2.3 Speech Schemes. 9.2.4 Challenges to the ASA Account of Speech Perception. 9.2.5 Interim Summary. 9.3 Speech Recognition by Machine. 9.3.1 The Statistical Basis of ASR. 9.3.2 Traditional Approaches to Robust ASR. 9.3.3 CASA-Driven Approaches to ASR. 9.4 Primitive CASA and ASR. 9.4.1 Speech and Time-Frequency Masking. 9.4.2 The Missing-Data Approach to ASR. 9.4.3 Marginalization-Based Missing-Data ASR Systems. 9.4.4 Imputation-Based Missing-Data Solutions. 9.4.5 Estimating the Missing-Data Mask. 9.4.6 Difficulties with the Missing-Data Approach. 9.5 Model-Based CASA and ASR. 9.5.1 The Speech Fragment Decoding Framework. 9.5.2 Coupling Source Segregation and Recognition. 9.6 Discussion and Conclusions. 9.7 Concluding Remarks. References. 10. Neural and Perceptual Modeling (Guy J. Brown and DeLiang Wang). 10.1 Introduction. 10.2 The Neural Basis of Auditory Grouping. 10.2.1 Theoretical Solutions to the Binding Problem. 10.2.2 Empirical Results on Binding and ASA. 10.3 Models of Individual Neurons. 10.3.1 Relaxation Oscillators. 10.3.2 Spike Oscillators. 10.3.3 A Model of a Specific Auditory Neuron. 10.4 Models of Specific Perceptual Phenomena. 10.4.1 Perceptual Streaming of Tone Sequences. 10.4.2 Perceptual Segregation of Concurrent Vowels with Different F0s. 10.5 The Oscillatory Correlation Framework for CASA. 10.5.1 Speech Segregation Based on Oscillatory Correlation. 10.6 Schema-Driven Grouping. 10.7 Discussion. 10.7.1 Temporal or Spatial Coding of Auditory Grouping. 10.7.2 Physiological Support for Neural Time Delays. 10.7.3 Convergence of Psychological, Physiological, and Computational Approaches. 10.7.4 Neural Models as a Framework for CASA. 10.7.5 The Role of Attention. 10.7.6 Schema-Based Organization. Acknowledgments. References. Index.