2,155 research outputs found
Acoustic Space Learning for Sound Source Separation and Localization on Binaural Manifolds
In this paper we address the problems of modeling the acoustic space
generated by a full-spectrum sound source and of using the learned model for
the localization and separation of multiple sources that simultaneously emit
sparse-spectrum sounds. We lay theoretical and methodological grounds in order
to introduce the binaural manifold paradigm. We perform an in-depth study of
the latent low-dimensional structure of the high-dimensional interaural
spectral data, based on a corpus recorded with a human-like audiomotor robot
head. A non-linear dimensionality reduction technique is used to show that
these data lie on a two-dimensional (2D) smooth manifold parameterized by the
motor states of the listener, or equivalently, the sound source directions. We
propose a probabilistic piecewise affine mapping model (PPAM) specifically
designed to deal with high-dimensional data exhibiting an intrinsic piecewise
linear structure. We derive a closed-form expectation-maximization (EM)
procedure for estimating the model parameters, followed by Bayes inversion for
obtaining the full posterior density function of a sound source direction. We
extend this solution to deal with missing data and redundancy in real world
spectrograms, and hence for 2D localization of natural sound sources such as
speech. We further generalize the model to the challenging case of multiple
sound sources and we propose a variational EM framework. The associated
algorithm, referred to as variational EM for source separation and localization
(VESSL) yields a Bayesian estimation of the 2D locations and time-frequency
masks of all the sources. Comparisons of the proposed approach with several
existing methods reveal that the combination of acoustic-space learning with
Bayesian inference enables our method to outperform state-of-the-art methods.Comment: 19 pages, 9 figures, 3 table
Spectrum of the Laplace-Beltrami Operator and the Phase Structure of Causal Dynamical Triangulation
We propose a new method to characterize the different phases observed in the
non-perturbative numerical approach to quantum gravity known as Causal
Dynamical Triangulation. The method is based on the analysis of the eigenvalues
and the eigenvectors of the Laplace-Beltrami operator computed on the
triangulations: it generalizes previous works based on the analysis of
diffusive processes and proves capable of providing more detailed information
on the geometric properties of the triangulations. In particular, we apply the
method to the analysis of spatial slices, showing that the different phases can
be characterized by a new order parameter related to the presence or absence of
a gap in the spectrum of the Laplace-Beltrami operator, and deriving an
effective dimensionality of the slices at the different scales. We also propose
quantities derived from the spectrum that could be used to monitor the running
to the continuum limit around a suitable critical point in the phase diagram,
if any is found.Comment: 21 pages, 26 figures, 2 table
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