690 research outputs found
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
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
Multimodal music information processing and retrieval: survey and future challenges
Towards improving the performance in various music information processing
tasks, recent studies exploit different modalities able to capture diverse
aspects of music. Such modalities include audio recordings, symbolic music
scores, mid-level representations, motion, and gestural data, video recordings,
editorial or cultural tags, lyrics and album cover arts. This paper critically
reviews the various approaches adopted in Music Information Processing and
Retrieval and highlights how multimodal algorithms can help Music Computing
applications. First, we categorize the related literature based on the
application they address. Subsequently, we analyze existing information fusion
approaches, and we conclude with the set of challenges that Music Information
Retrieval and Sound and Music Computing research communities should focus in
the next years
Neuromorphic Auditory Perception by Neural Spiketrum
Neuromorphic computing holds the promise to achieve the energy efficiency and
robust learning performance of biological neural systems. To realize the
promised brain-like intelligence, it needs to solve the challenges of the
neuromorphic hardware architecture design of biological neural substrate and
the hardware amicable algorithms with spike-based encoding and learning. Here
we introduce a neural spike coding model termed spiketrum, to characterize and
transform the time-varying analog signals, typically auditory signals, into
computationally efficient spatiotemporal spike patterns. It minimizes the
information loss occurring at the analog-to-spike transformation and possesses
informational robustness to neural fluctuations and spike losses. The model
provides a sparse and efficient coding scheme with precisely controllable spike
rate that facilitates training of spiking neural networks in various auditory
perception tasks. We further investigate the algorithm-hardware co-designs
through a neuromorphic cochlear prototype which demonstrates that our approach
can provide a systematic solution for spike-based artificial intelligence by
fully exploiting its advantages with spike-based computation.Comment: This work has been submitted to the IEEE for possible publicatio
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