2,944 research outputs found
Over-Determined Source Separation and Localization Using Distributed Microphones
This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1
SoundCompass: a distributed MEMS microphone array-based sensor for sound source localization
Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field
Structured Sparsity Models for Multiparty Speech Recovery from Reverberant Recordings
We tackle the multi-party speech recovery problem through modeling the
acoustic of the reverberant chambers. Our approach exploits structured sparsity
models to perform room modeling and speech recovery. We propose a scheme for
characterizing the room acoustic from the unknown competing speech sources
relying on localization of the early images of the speakers by sparse
approximation of the spatial spectra of the virtual sources in a free-space
model. The images are then clustered exploiting the low-rank structure of the
spectro-temporal components belonging to each source. This enables us to
identify the early support of the room impulse response function and its unique
map to the room geometry. To further tackle the ambiguity of the reflection
ratios, we propose a novel formulation of the reverberation model and estimate
the absorption coefficients through a convex optimization exploiting joint
sparsity model formulated upon spatio-spectral sparsity of concurrent speech
representation. The acoustic parameters are then incorporated for separating
individual speech signals through either structured sparse recovery or inverse
filtering the acoustic channels. The experiments conducted on real data
recordings demonstrate the effectiveness of the proposed approach for
multi-party speech recovery and recognition.Comment: 31 page
Ad Hoc Microphone Array Calibration: Euclidean Distance Matrix Completion Algorithm and Theoretical Guarantees
This paper addresses the problem of ad hoc microphone array calibration where
only partial information about the distances between microphones is available.
We construct a matrix consisting of the pairwise distances and propose to
estimate the missing entries based on a novel Euclidean distance matrix
completion algorithm by alternative low-rank matrix completion and projection
onto the Euclidean distance space. This approach confines the recovered matrix
to the EDM cone at each iteration of the matrix completion algorithm. The
theoretical guarantees of the calibration performance are obtained considering
the random and locally structured missing entries as well as the measurement
noise on the known distances. This study elucidates the links between the
calibration error and the number of microphones along with the noise level and
the ratio of missing distances. Thorough experiments on real data recordings
and simulated setups are conducted to demonstrate these theoretical insights. A
significant improvement is achieved by the proposed Euclidean distance matrix
completion algorithm over the state-of-the-art techniques for ad hoc microphone
array calibration.Comment: In Press, available online, August 1, 2014.
http://www.sciencedirect.com/science/article/pii/S0165168414003508, Signal
Processing, 201
Sound Source Separation
This is the author's accepted pre-print of the article, first published as G. Evangelista, S. Marchand, M. D. Plumbley and E. Vincent. Sound source separation. In U. Zölzer (ed.), DAFX: Digital Audio Effects, 2nd edition, Chapter 14, pp. 551-588. John Wiley & Sons, March 2011. ISBN 9781119991298. DOI: 10.1002/9781119991298.ch14file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.26file: Proof:e\EvangelistaMarchandPlumbleyV11-sound.pdf:PDF owner: markp timestamp: 2011.04.2
Multi-modal Blind Source Separation with Microphones and Blinkies
We propose a blind source separation algorithm that jointly exploits
measurements by a conventional microphone array and an ad hoc array of low-rate
sound power sensors called blinkies. While providing less information than
microphones, blinkies circumvent some difficulties of microphone arrays in
terms of manufacturing, synchronization, and deployment. The algorithm is
derived from a joint probabilistic model of the microphone and sound power
measurements. We assume the separated sources to follow a time-varying
spherical Gaussian distribution, and the non-negative power measurement
space-time matrix to have a low-rank structure. We show that alternating
updates similar to those of independent vector analysis and Itakura-Saito
non-negative matrix factorization decrease the negative log-likelihood of the
joint distribution. The proposed algorithm is validated via numerical
experiments. Its median separation performance is found to be up to 8 dB more
than that of independent vector analysis, with significantly reduced
variability.Comment: Accepted at IEEE ICASSP 2019, Brighton, UK. 5 pages. 3 figure
Joint model-based recognition and localization of overlapped acoustic events using a set of distributed small microphone arrays
In the analysis of acoustic scenes, often the occurring sounds have to be
detected in time, recognized, and localized in space. Usually, each of these
tasks is done separately. In this paper, a model-based approach to jointly
carry them out for the case of multiple simultaneous sources is presented and
tested. The recognized event classes and their respective room positions are
obtained with a single system that maximizes the combination of a large set of
scores, each one resulting from a different acoustic event model and a
different beamformer output signal, which comes from one of several
arbitrarily-located small microphone arrays. By using a two-step method, the
experimental work for a specific scenario consisting of meeting-room acoustic
events, either isolated or overlapped with speech, is reported. Tests carried
out with two datasets show the advantage of the proposed approach with respect
to some usual techniques, and that the inclusion of estimated priors brings a
further performance improvement.Comment: Computational acoustic scene analysis, microphone array signal
processing, acoustic event detectio
Source localization using acoustic vector sensors: a music approach
Traditionally, a large array of microphones is used to localize multiple far field sources in acoustics. We present a sound source localization technique that requires far less channels and measurement locations (affecting data channels, setup times and cabling issues). This is achieved by using an acoustic vector sensor (AVS) in air that consists of four collocated sensors: three orthogonally placed acoustic particle velocity sensors and an omnidirectional sound pressure transducer. Experimental evidence is presented demonstrating that a single 4 channel AVS based approach accurately localizes two uncorrelated sources. The method is extended to multiple AVS, increasing the number of sources that can be identified. Theory and measurement results are presented. Attention is paid to the theoretical possibilities and limitations of this approach, as well as the signal processing techniques based on the MUSIC method
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
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Conventional speaker localization algorithms, based merely on the received
microphone signals, are often sensitive to adverse conditions, such as: high
reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in
meeting rooms or cars, it can be assumed that the source position is confined
to a predefined area, and the acoustic parameters of the environment are
approximately fixed. Such scenarios give rise to the assumption that the
acoustic samples from the region of interest have a distinct geometrical
structure. In this paper, we show that the high dimensional acoustic samples
indeed lie on a low dimensional manifold and can be embedded into a low
dimensional space. Motivated by this result, we propose a semi-supervised
source localization algorithm which recovers the inverse mapping between the
acoustic samples and their corresponding locations. The idea is to use an
optimization framework based on manifold regularization, that involves
smoothness constraints of possible solutions with respect to the manifold. The
proposed algorithm, termed Manifold Regularization for Localization (MRL), is
implemented in an adaptive manner. The initialization is conducted with only
few labelled samples attached with their respective source locations, and then
the system is gradually adapted as new unlabelled samples (with unknown source
locations) are received. Experimental results show superior localization
performance when compared with a recently presented algorithm based on a
manifold learning approach and with the generalized cross-correlation (GCC)
algorithm as a baseline
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