10,427 research outputs found
Score-Informed Source Separation for Musical Audio Recordings [An overview]
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The Sound Manifesto
Computing practice today depends on visual output to drive almost all user
interaction. Other senses, such as audition, may be totally neglected, or used
tangentially, or used in highly restricted specialized ways. We have excellent
audio rendering through D-A conversion, but we lack rich general facilities for
modeling and manipulating sound comparable in quality and flexibility to
graphics. We need co-ordinated research in several disciplines to improve the
use of sound as an interactive information channel.
Incremental and separate improvements in synthesis, analysis, speech
processing, audiology, acoustics, music, etc. will not alone produce the
radical progress that we seek in sonic practice. We also need to create a new
central topic of study in digital audio research. The new topic will assimilate
the contributions of different disciplines on a common foundation. The key
central concept that we lack is sound as a general-purpose information channel.
We must investigate the structure of this information channel, which is driven
by the co-operative development of auditory perception and physical sound
production. Particular audible encodings, such as speech and music, illuminate
sonic information by example, but they are no more sufficient for a
characterization than typography is sufficient for a characterization of visual
information.Comment: To appear in the conference on Critical Technologies for the Future
of Computing, part of SPIE's International Symposium on Optical Science and
Technology, 30 July to 4 August 2000, San Diego, C
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
Post-tonal analytical techniques: Stravinsky’s symphonies of wind instruments
The analysis of post-tonal music remains problematic. Analytical methodologies designed specifically for tonal or atonal music require substantial modification if they are to effectively analyse post-tonal works. Stravinsky's Symphonies of Wind Instruments is a fine model of post-tonal originality and is a difficult piece for analysis. Following a discussion of various analytical approaches, this paper presents a detailed analytical examination of Symphonies of Wind Instruments. The paper closes by developing conclusions and acknowledging the continuing advances of music analysis
Score-Informed Source Separation for Music Signals
In recent years, the processing of audio recordings by exploiting additional musical knowledge has turned out to be a promising research direction. In particular, additional note information as specified by a musical score or a MIDI file has been employed to support various audio processing tasks such as source separation, audio parameterization, performance analysis, or instrument equalization. In this contribution, we provide an overview of approaches for score-informed source separation and illustrate their potential by discussing innovative applications and interfaces. Additionally, to illustrate some basic principles behind these approaches, we demonstrate how score information can be integrated into the well-known non-negative matrix factorization (NMF) framework. Finally, we compare this approach to advanced methods based on parametric models
Stable super-resolution limit and smallest singular value of restricted Fourier matrices
Super-resolution refers to the process of recovering the locations and
amplitudes of a collection of point sources, represented as a discrete measure,
given of its noisy low-frequency Fourier coefficients. The recovery
process is highly sensitive to noise whenever the distance between the
two closest point sources is less than . This paper studies the {\it
fundamental difficulty of super-resolution} and the {\it performance guarantees
of a subspace method called MUSIC} in the regime that .
The most important quantity in our theory is the minimum singular value of
the Vandermonde matrix whose nodes are specified by the source locations. Under
the assumption that the nodes are closely spaced within several well-separated
clumps, we derive a sharp and non-asymptotic lower bound for this quantity. Our
estimate is given as a weighted sum, where each term only depends on
the configuration of each individual clump. This implies that, as the noise
increases, the super-resolution capability of MUSIC degrades according to a
power law where the exponent depends on the cardinality of the largest clump.
Numerical experiments validate our theoretical bounds for the minimum singular
value and the resolution limit of MUSIC.
When there are point sources located on a grid with spacing , the
fundamental difficulty of super-resolution can be quantitatively characterized
by a min-max error, which is the reconstruction error incurred by the best
possible algorithm in the worst-case scenario. We show that the min-max error
is closely related to the minimum singular value of Vandermonde matrices, and
we provide a non-asymptotic and sharp estimate for the min-max error, where the
dominant term is .Comment: 47 pages, 8 figure
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