5,224 research outputs found
Reconstruction of Speech Signals from their Unpredictable Points Manifold
International audienceThis paper shows that a microcanonical approach to complexity, such as the Microcanonical Multiscale Formalism, provides new insights to analyze non-linear dynamics of speech, specifically in relation to the problem of speech samples classification according to their information content. Central to the approach is the precise computation of Local Predictability Exponents (LPEs) according to a procedure based on the evaluation of the degree of reconstructibility around a given point. We show that LPEs are key quantities related to predictability in the framework of reconstructible systems: it is possible to reconstruct the whole speech signal by applying a reconstruction kernel to a small subset of points selected according to their LPE value. This provides a strong indication of the importance of the Unpredictable Points Manifold(UPM), already demonstrated for other types of complex signals. Experiments show that a UPM containing around 12% of the points providesvery good perceptual reconstruction quality
Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression
This paper addresses the problem of localizing audio sources using binaural
measurements. We propose a supervised formulation that simultaneously localizes
multiple sources at different locations. The approach is intrinsically
efficient because, contrary to prior work, it relies neither on source
separation, nor on monaural segregation. The method starts with a training
stage that establishes a locally-linear Gaussian regression model between the
directional coordinates of all the sources and the auditory features extracted
from binaural measurements. While fixed-length wide-spectrum sounds (white
noise) are used for training to reliably estimate the model parameters, we show
that the testing (localization) can be extended to variable-length
sparse-spectrum sounds (such as speech), thus enabling a wide range of
realistic applications. Indeed, we demonstrate that the method can be used for
audio-visual fusion, namely to map speech signals onto images and hence to
spatially align the audio and visual modalities, thus enabling to discriminate
between speaking and non-speaking faces. We release a novel corpus of real-room
recordings that allow quantitative evaluation of the co-localization method in
the presence of one or two sound sources. Experiments demonstrate increased
accuracy and speed relative to several state-of-the-art methods.Comment: 15 pages, 8 figure
Chaos in the segments from Korean traditional singing and western singing
We investigate the time series of the segments from a Korean traditional song
``Gwansanyungma'' and a western song ``La Mamma Morta'' using chaotic analysis
techniques.
It is found that the phase portrait in the reconstructed state space of the
time series of the segment from the Korean traditional song has a more complex
structure in comparison with the segment from the western songs. The segment
from the Korean traditional song has the correlation dimension 4.4 and two
positive Lyapunov exponents which show that the dynamic related to the Korean
traditional song is a high dimensional hyperchaotic process. On the other hand,
the segment from the western song with only one positive Lyapunov exponent and
the correlation dimension 2.5 exhibits low dimensional chaotic behavior.Comment: 23 pages including 10 eps figures, latex, to appear in J. Acoust.
Soc. A
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
Tensor Networks for Dimensionality Reduction and Large-Scale Optimizations. Part 2 Applications and Future Perspectives
Part 2 of this monograph builds on the introduction to tensor networks and
their operations presented in Part 1. It focuses on tensor network models for
super-compressed higher-order representation of data/parameters and related
cost functions, while providing an outline of their applications in machine
learning and data analytics. A particular emphasis is on the tensor train (TT)
and Hierarchical Tucker (HT) decompositions, and their physically meaningful
interpretations which reflect the scalability of the tensor network approach.
Through a graphical approach, we also elucidate how, by virtue of the
underlying low-rank tensor approximations and sophisticated contractions of
core tensors, tensor networks have the ability to perform distributed
computations on otherwise prohibitively large volumes of data/parameters,
thereby alleviating or even eliminating the curse of dimensionality. The
usefulness of this concept is illustrated over a number of applied areas,
including generalized regression and classification (support tensor machines,
canonical correlation analysis, higher order partial least squares),
generalized eigenvalue decomposition, Riemannian optimization, and in the
optimization of deep neural networks. Part 1 and Part 2 of this work can be
used either as stand-alone separate texts, or indeed as a conjoint
comprehensive review of the exciting field of low-rank tensor networks and
tensor decompositions.Comment: 232 page
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