249 research outputs found
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An EM Algorithm for Localizing Multiple Sound: Sources in Reverberant Environments
We present a method for localizing and separating sound sources in stereo recordings that is robust to reverberation and does not make any assumptions about the source statistics. The method consists of a probabilistic model of binaural multisource recordings and an expectation maximization algorithm for finding the maximum likelihood parameters of that model. These parameters include distributions over delays and assignments of time-frequency regions to sources. We evaluate this method against two comparable algorithms on simulations of simultaneous speech from two or three sources. Our method outperforms the others in anechoic conditions and performs as well as the better of the two in the presence of reverberation
EM localization and separation using interaural level and phase cues
We describe a system for localizing and separating multiple sound sources from a reverberant two-channel recording. It consists of a probabilistic model of interaural level and phase differences and an EM algorithm for finding the maximum likelihood parameters of this model. By assigning points in the interaural spectrogram probabilistically to sources with the best-fitting parameters and then estimating the parameters of the sources from the points assigned to them, the system is able to separate and localize more sound sources than there are available channels. It is also able to estimate frequency-dependent level differences of sources in a mixture that correspond well to those measured in isolation. In experiments in simulated anechoic and reverberant environments, the proposed system improved the signal-to-noise ratio of target sources by 2.7 and 3.4dB more than two comparable algorithms on average
A Geometric Approach to Sound Source Localization from Time-Delay Estimates
This paper addresses the problem of sound-source localization from time-delay
estimates using arbitrarily-shaped non-coplanar microphone arrays. A novel
geometric formulation is proposed, together with a thorough algebraic analysis
and a global optimization solver. The proposed model is thoroughly described
and evaluated. The geometric analysis, stemming from the direct acoustic
propagation model, leads to necessary and sufficient conditions for a set of
time delays to correspond to a unique position in the source space. Such sets
of time delays are referred to as feasible sets. We formally prove that every
feasible set corresponds to exactly one position in the source space, whose
value can be recovered using a closed-form localization mapping. Therefore we
seek for the optimal feasible set of time delays given, as input, the received
microphone signals. This time delay estimation problem is naturally cast into a
programming task, constrained by the feasibility conditions derived from the
geometric analysis. A global branch-and-bound optimization technique is
proposed to solve the problem at hand, hence estimating the best set of
feasible time delays and, subsequently, localizing the sound source. Extensive
experiments with both simulated and real data are reported; we compare our
methodology to four state-of-the-art techniques. This comparison clearly shows
that the proposed method combined with the branch-and-bound algorithm
outperforms existing methods. These in-depth geometric understanding, practical
algorithms, and encouraging results, open several opportunities for future
work.Comment: 13 pages, 2 figures, 3 table, journa
Multilevel B-Splines-Based Learning Approach for Sound Source Localization
© 2001-2012 IEEE. In this paper, a new learning approach for sound source localization is presented using ad hoc either synchronous or asynchronous distributed microphone networks based on the time differences of arrival (TDOA) estimation. It is first to propose a new concept in which the coordinates of a sound source location are defined as the functions of TDOAs, computing for each pair of microphone signals in the network. Then, given a set of pre-recorded sound measurements and their corresponding source locations, the multilevel B-splines-based learning model is proposed to be trained by the input of the known TDOAs and the output of the known coordinates of the sound source locations. For a new acoustic source, if its sound signals are recorded, the correspondingly computed TDOAs can be fed into the learned model to predict the location of the new source. Superiorities of the proposed method are to incorporate the acoustic characteristics of a targeted environment and even remaining uncertainty of TDOA estimations into the learning model before conducting its prediction and to be applicable for both synchronous or asynchronous distributed microphone sensor networks. The effectiveness of the proposed algorithm in terms of localization accuracy and computational cost in comparisons with the state-of-the-art methods was extensively validated on both synthetic simulation experiments as well as in three real-life environments
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
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
Probabilistic Modeling Paradigms for Audio Source Separation
This is the author's final version of the article, first published as E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, M. E. Davies. Probabilistic Modeling Paradigms for Audio Source Separation. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 7, pp. 162-185. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch007file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, the authors focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. They show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. They compare the merits of either paradigm and report objective performance figures. They also,conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems
On enhancing model-based expectation maximization source separation in dynamic reverberant conditions using automatic Clifton effect
[EN] Source separation algorithms based on spatial cues generally face two major problems. The first one is their general performance degradation in reverberant environments and the second is their inability to differentiate closely located sources due to similarity of their spatial cues. The latter problem gets amplified in highly reverberant environments as reverberations have a distorting effect on spatial cues. In this paper, we have proposed a separation algorithm, in which inside an enclosure, the distortions due to reverberations in a spatial cue based source separation algorithm namely model-based expectation-maximization source separation and localization (MESSL) are minimized by using the Precedence effect. The Precedence effect acts as a gatekeeper which restricts the reverberations entering the separation system resulting in its improved separation performance. And this effect is automatically transformed into the Clifton effect to deal with the dynamic acoustic conditions. Our proposed algorithm has shown improved performance over MESSL in all kinds of reverberant conditions including closely located sources. On average, 22.55% improvement in SDR (signal to distortion ratio) and 15% in PESQ (perceptual evaluation of speech quality) is observed by using the Clifton effect to tackle dynamic reverberant conditions.This project is funded by Higher Education Commission (HEC), Pakistan, under project no. 6330/KPK/NRPU/R&D/HEC/2016.Gul, S.; Khan, MS.; Shah, SW.; Lloret, J. (2020). On enhancing model-based expectation maximization source separation in dynamic reverberant conditions using automatic Clifton effect. International Journal of Communication Systems. 33(3):1-18. https://doi.org/10.1002/dac.421011833
Acoustic Sensor Networks and Mobile Robotics for Sound Source Localization
© 2019 IEEE. Localizing a sound source is a fundamental but still challenging issue in many applications, where sound information is gathered by static and local microphone sensors. Therefore, this work proposes a new system by exploiting advances in sensor networks and robotics to more accurately address the problem of sound source localization. By the use of the network infrastructure, acoustic sensors are more efficient to spatially monitor acoustical phenomena. Furthermore, a mobile robot is proposed to carry an extra microphone array in order to collect more acoustic signals when it travels around the environment. Driving the robot is guided by the need to increase the quality of the data gathered by the static acoustic sensors, which leads to better probabilistic fusion of all the information gained, so that an increasingly accurate map of the sound source can be built. The proposed system has been validated in a real-life environment, where the obtained results are highly promising
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