524 research outputs found
Convexity in source separation: Models, geometry, and algorithms
Source separation or demixing is the process of extracting multiple
components entangled within a signal. Contemporary signal processing presents a
host of difficult source separation problems, from interference cancellation to
background subtraction, blind deconvolution, and even dictionary learning.
Despite the recent progress in each of these applications, advances in
high-throughput sensor technology place demixing algorithms under pressure to
accommodate extremely high-dimensional signals, separate an ever larger number
of sources, and cope with more sophisticated signal and mixing models. These
difficulties are exacerbated by the need for real-time action in automated
decision-making systems.
Recent advances in convex optimization provide a simple framework for
efficiently solving numerous difficult demixing problems. This article provides
an overview of the emerging field, explains the theory that governs the
underlying procedures, and surveys algorithms that solve them efficiently. We
aim to equip practitioners with a toolkit for constructing their own demixing
algorithms that work, as well as concrete intuition for why they work
Application of sound source separation methods to advanced spatial audio systems
This thesis is related to the field of Sound Source Separation (SSS). It addresses the development
and evaluation of these techniques for their application in the resynthesis of high-realism sound scenes by
means of Wave Field Synthesis (WFS). Because the vast majority of audio recordings are preserved in twochannel
stereo format, special up-converters are required to use advanced spatial audio reproduction formats,
such as WFS. This is due to the fact that WFS needs the original source signals to be available, in order to
accurately synthesize the acoustic field inside an extended listening area. Thus, an object-based mixing is
required.
Source separation problems in digital signal processing are those in which several signals have been mixed
together and the objective is to find out what the original signals were. Therefore, SSS algorithms can be applied
to existing two-channel mixtures to extract the different objects that compose the stereo scene. Unfortunately,
most stereo mixtures are underdetermined, i.e., there are more sound sources than audio channels. This
condition makes the SSS problem especially difficult and stronger assumptions have to be taken, often related to
the sparsity of the sources under some signal transformation.
This thesis is focused on the application of SSS techniques to the spatial sound reproduction field. As a result,
its contributions can be categorized within these two areas. First, two underdetermined SSS methods are
proposed to deal efficiently with the separation of stereo sound mixtures. These techniques are based on a
multi-level thresholding segmentation approach, which enables to perform a fast and unsupervised separation of
sound sources in the time-frequency domain. Although both techniques rely on the same clustering type, the
features considered by each of them are related to different localization cues that enable to perform separation
of either instantaneous or real mixtures.Additionally, two post-processing techniques aimed at
improving the isolation of the separated sources are proposed. The performance achieved by
several SSS methods in the resynthesis of WFS sound scenes is afterwards evaluated by means of
listening tests, paying special attention to the change observed in the perceived spatial attributes.
Although the estimated sources are distorted versions of the original ones, the masking effects
involved in their spatial remixing make artifacts less perceptible, which improves the overall
assessed quality. Finally, some novel developments related to the application of time-frequency
processing to source localization and enhanced sound reproduction are presented.Cobos Serrano, M. (2009). Application of sound source separation methods to advanced spatial audio systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8969Palanci
Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments
We address the problem of online localization and tracking of multiple moving
speakers in reverberant environments. The paper has the following
contributions. We use the direct-path relative transfer function (DP-RTF), an
inter-channel feature that encodes acoustic information robust against
reverberation, and we propose an online algorithm well suited for estimating
DP-RTFs associated with moving audio sources. Another crucial ingredient of the
proposed method is its ability to properly assign DP-RTFs to audio-source
directions. Towards this goal, we adopt a maximum-likelihood formulation and we
propose to use an exponentiated gradient (EG) to efficiently update
source-direction estimates starting from their currently available values. The
problem of multiple speaker tracking is computationally intractable because the
number of possible associations between observed source directions and physical
speakers grows exponentially with time. We adopt a Bayesian framework and we
propose a variational approximation of the posterior filtering distribution
associated with multiple speaker tracking, as well as an efficient variational
expectation-maximization (VEM) solver. The proposed online localization and
tracking method is thoroughly evaluated using two datasets that contain
recordings performed in real environments.Comment: IEEE Journal of Selected Topics in Signal Processing, 201
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