Proceedings Article•
Polyphonic Instrument Recognition Using Spectral Clustering.
01 Jan 2007-pp 213-218
TL;DR: This paper proposes a framework for the sound source separation and timbre classification of polyphonic, multi-instrumental music signals, inspired by ideas from Computational Auditory Scene Analysis and formulated as a graph partitioning problem.
Abstract: The identification of the instruments playing in a polyphonic music signal is an important and unsolved problem in Music Information Retrieval. In this paper, we propose a framework for the sound source separation and timbre classification of polyphonic, multi-instrumental music signals. The sound source separation method is inspired by ideas from Computational Auditory Scene Analysis and formulated as a graph partitioning problem. It utilizes a sinusoidal analysis front-end and makes use of the normalized cut, applied as a global criterion for segmenting graphs. Timbre models for six musical instruments are used for the classification of the resulting sound sources. The proposed framework is evaluated on a dataset consisting of mixtures of a variable number of simultaneous pitches and instruments, up to a maximum of four concurrent notes.
Citations
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TL;DR: Bregman as discussed by the authors argues that there are two kinds of principle for auditory grouping and segregation: schema-based and primitive, and provides a comprehensive review and interpretation of perceptual experiments up to 1989, so his book pre-dates recent attempts to implement auditory grouping principles in computational models.
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
273 citations
TL;DR: It is demonstrated that, to be successful, music audio signal processing techniques must be informed by a deep and thorough insight into the nature of music itself.
Abstract: Music signal processing may appear to be the junior relation of the large and mature field of speech signal processing, not least because many techniques and representations originally developed for speech have been applied to music, often with good results. However, music signals possess specific acoustic and structural characteristics that distinguish them from spoken language or other nonmusical signals. This paper provides an overview of some signal analysis techniques that specifically address musical dimensions such as melody, harmony, rhythm, and timbre. We will examine how particular characteristics of music signals impact and determine these techniques, and we highlight a number of novel music analysis and retrieval tasks that such processing makes possible. Our goal is to demonstrate that, to be successful, music audio signal processing techniques must be informed by a deep and thorough insight into the nature of music itself.
246 citations
TL;DR: A number of methods for early and late temporal integration are proposed and an in-depth experimental study on their interest for the task of musical instrument recognition on solo musical phrases is provided.
Abstract: Nowadays, it appears essential to design automatic indexing tools which provide meaningful and efficient means to describe the musical audio content. There is in fact a growing interest for music information retrieval (MIR) applications amongst which the most popular are related to music similarity retrieval, artist identification, musical genre or instrument recognition. Current MIR-related classification systems usually do not take into account the mid-term temporal properties of the signal (over several frames) and lie on the assumption that the observations of the features in different frames are statistically independent. The aim of this paper is to demonstrate the usefulness of the information carried by the evolution of these characteristics over time. To that purpose, we propose a number of methods for early and late temporal integration and provide an in-depth experimental study on their interest for the task of musical instrument recognition on solo musical phrases. In particular, the impact of the time horizon over which the temporal integration is performed will be assessed both for fixed and variable frame length analysis. Also, a number of proposed alignment kernels will be used for late temporal integration. For all experiments, the results are compared to a state of the art musical instrument recognition system.
129 citations
Cites background from "Polyphonic Instrument Recognition U..."
...The authors are with the Institut TELECOM, TELCOM ParisTech, CNRS LTCI, F-75014 Paris, France....
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01 Jan 2014
TL;DR: The approach taken is to interpret the sound event as a two-dimensional spectrogram image, with the two axes as the time and frequency dimensions, which enables novel methods for SER to be developed based on spectrogramimage processing, which are inspired by techniques from the field of image processing.
Abstract: The objective of this research is to develop feature extraction and classification techniques for the task of sound event recognition (SER) in unstructured environments. Although this field is traditionally overshadowed by the popular field of automatic speech recognition (ASR), an SER system that can achieve human-like sound recognition performance opens up a range of novel application areas. These include acoustic surveillance, bio-acoustical monitoring, environmental context detection, healthcare applications and more generally the rich transcription of acoustic environments. The challenge in such environments are the adverse effects such as noise, distortion and multiple sources, which are more likely to occur with distant microphones compared to the close-talking microphones that are more common in ASR. In addition, the characteristics of acoustic events are less well defined than those of speech, and there is no sub-word dictionary available like the phonemes in speech. Therefore, the performance of ASR systems typically degrades dramatically in these challenging unstructured environments, and it is important to develop new methods that can perform well for this challenging task. In this thesis, the approach taken is to interpret the sound event as a two-dimensional spectrogram image, with the two axes as the time and frequency dimensions. This enables novel methods for SER to be developed based on spectrogram image processing, which are inspired by techniques from the field of image processing. The motivation for such an approach is based on finding an automatic approach to “spectrogram reading”, where it is possible for humans to visually recognise the different sound event signatures in the spectrogram. The advantages of such an approach are twofold. Firstly, the sound event image representation makes it possible to naturally capture the sound information in a two-dimensional feature. This has advantages over conventional onedimensional frame-based features, which capture only a slice of spectral information
62 citations
Cites methods from "Polyphonic Instrument Recognition U..."
...Other approaches include detecting ridges in the spectrogram and extracting features by combining different segments to form hypotheses [255, 256]....
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TL;DR: The strategy presented in this paper explores the spectral disjointness among instruments by identifying isolated partials, from which a number of features are extracted, and is used to infer which instrument is more likely to have generated that partial.
Abstract: In a musical signals, the spectral and temporal contents of instruments often overlap. If the number of channels is at least the same as the number of instruments, it is possible to apply statistical tools to highlight the characteristics of each instrument, making their identification possible. However, in the underdetermined case, in which there are fewer channels than sources, the task becomes challenging. One possible way to solve this problem is to seek for regions in the time and/or frequency domains in which the content of a given instrument appears isolated. The strategy presented in this paper explores the spectral disjointness among instruments by identifying isolated partials, from which a number of features are extracted. The information contained in those features, in turn, is used to infer which instrument is more likely to have generated that partial. Hence, the only condition for the method to work is that at least one isolated partial exists for each instrument somewhere in the signal. If several isolated partials are available, the results are summarized into a single, more accurate classification. Experimental results using 25 instruments demonstrate the good discrimination capabilities of the method.
50 citations
Cites methods from "Polyphonic Instrument Recognition U..."
...To make the estimation of each partial frequency more accurate, the following procedure was adopted for each F0....
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References
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TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Abstract: We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We applied this approach to segmenting static images, as well as motion sequences, and found the results to be very encouraging.
13,789 citations
17 Jun 1997
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Abstract: We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images and found results very encouraging.
11,827 citations
"Polyphonic Instrument Recognition U..." refers background in this paper
...The template describes the typical evolution in time of the spectral envelope of a note....
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Book•
01 Jun 1990TL;DR: Auditory Scene Analysis as discussed by the authors 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.
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.
2,968 citations
TL;DR: A sinusoidal model for the speech waveform is used to develop a new analysis/synthesis technique that is characterized by the amplitudes, frequencies, and phases of the component sine waves, which forms the basis for new approaches to the problems of speech transformations including time-scale and pitch-scale modification, and midrate speech coding.
Abstract: A sinusoidal model for the speech waveform is used to develop a new analysis/synthesis technique that is characterized by the amplitudes, frequencies, and phases of the component sine waves. These parameters are estimated from the short-time Fourier transform using a simple peak-picking algorithm. Rapid changes in the highly resolved spectral components are tracked using the concept of "birth" and "death" of the underlying sine waves. For a given frequency track a cubic function is used to unwrap and interpolate the phase such that the phase track is maximally smooth. This phase function is applied to a sine-wave generator, which is amplitude modulated and added to the other sine waves to give the final speech output. The resulting synthetic waveform preserves the general waveform shape and is essentially perceptually indistinguishable from the original speech. Furthermore, in the presence of noise the perceptual characteristics of the speech as well as the noise are maintained. In addition, it was found that the representation was sufficiently general that high-quality reproduction was obtained for a larger class of inputs including: two overlapping, superposed speech waveforms; music waveforms; speech in musical backgrounds; and certain marine biologic sounds. Finally, the analysis/synthesis system forms the basis for new approaches to the problems of speech transformations including time-scale and pitch-scale modification, and midrate speech coding [8], [9].
1,659 citations
"Polyphonic Instrument Recognition U..." refers methods in this paper
...Once each single-note cluster of sinusoidal parameters has been extracted, it is classified into an instrument from a predefined set of six: piano (p), oboe (o), clarinet (c), trumpet (t), violin (v) and alto sax (s)....
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TL;DR: This paper focuses on the development of model-Based Speech Segregation in CASA systems, which was first introduced in 2000 and has since been upgraded to a full-blown model-based system.
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.
940 citations