Exploiting correlogram structure for robust speech recognition with multiple speech sources

This paper addresses the problem of separating and recognising speech in a monaural acoustic mixture with the presence of competing speech sources. The proposed system treats sound source separation and speech recognition as tightly coupled processes. In the first stage sound source separation is performed in the correlogram domain. For periodic sounds, the correlogram exhibits symmetric tree-like structures whose stems are located on the delay that corresponds to multiple pitch periods. These pitch-related structures are exploited in the study to group spectral components at each time frame. Local pitch estimates are then computed for each spectral group and are used to form simultaneous pitch tracks for temporal integration. These processes segregate a spectral representation of the acoustic mixture into several time-frequency regions such that the energy in each region is likely to have originated from a single periodic sound source. The identified time-frequency regions, together with the spectral representation, are employed by a `speech fragment decoder' which employs `missing data' techniques with clean speech models to simultaneously search for the acoustic evidence that best matches model sequences. The paper presents evaluations based on artificially mixed simultaneous speech utterances. A coherence-measuring experiment is first reported which quantifies the consistency of the identified fragments with a single source. The system is then evaluated in a speech recognition task and compared to a conventional fragment generation approach. Results show that the proposed system produces more coherent fragments over different conditions, which results in significantly better recognition accuracy

Score-Informed Source Separation for Musical Audio Recordings [An overview]

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Jointly Tracking and Separating Speech Sources Using Multiple Features and the generalized labeled multi-Bernoulli Framework

This paper proposes a novel joint multi-speaker tracking-and-separation method based on the generalized labeled multi-Bernoulli (GLMB) multi-target tracking filter, using sound mixtures recorded by microphones. Standard multi-speaker tracking algorithms usually only track speaker locations, and ambiguity occurs when speakers are spatially close. The proposed multi-feature GLMB tracking filter treats the set of vectors of associated speaker features (location, pitch and sound) as the multi-target multi-feature observation, characterizes transitioning features with corresponding transition models and overall likelihood function, thus jointly tracks and separates each multi-feature speaker, and addresses the spatial ambiguity problem. Numerical evaluation verifies that the proposed method can correctly track locations of multiple speakers and meanwhile separate speech signals

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Identifying Cover Songs Using Information-Theoretic Measures of Similarity

This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/This paper investigates methods for quantifying similarity between audio signals, specifically for the task of cover song detection. We consider an information-theoretic approach, where we compute pairwise measures of predictability between time series. We compare discrete-valued approaches operating on quantized audio features, to continuous-valued approaches. In the discrete case, we propose a method for computing the normalized compression distance, where we account for correlation between time series. In the continuous case, we propose to compute information-based measures of similarity as statistics of the prediction error between time series. We evaluate our methods on two cover song identification tasks using a data set comprised of 300 Jazz standards and using the Million Song Dataset. For both datasets, we observe that continuous-valued approaches outperform discrete-valued approaches. We consider approaches to estimating the normalized compression distance (NCD) based on string compression and prediction, where we observe that our proposed normalized compression distance with alignment (NCDA) improves average performance over NCD, for sequential compression algorithms. Finally, we demonstrate that continuous-valued distances may be combined to improve performance with respect to baseline approaches. Using a large-scale filter-and-refine approach, we demonstrate state-of-the-art performance for cover song identification using the Million Song Dataset.The work of P. Foster was supported by an Engineering and Physical Sciences Research Council Doctoral Training Account studentship

Sound Source Separation

This is the author's accepted pre-print of the article, first published as G. Evangelista, S. Marchand, M. D. Plumbley and E. Vincent. Sound source separation. In U. Zölzer (ed.), DAFX: Digital Audio Effects, 2nd edition, Chapter 14, pp. 551-588. John Wiley & Sons, March 2011. ISBN 9781119991298. DOI: 10.1002/9781119991298.ch14file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.2

ARSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-92-J-0225); Office of Naval Research (N00014-01-1-0624); Advanced Research Projects Agency (N00014-92-J-4015); British Petroleum (89A-1204); National Science Foundation (IRI-90-00530); American Society of Engineering Educatio