A phase-based dual microphone method to count and locate audio sources in reverberant rooms

Chami, Zaher El; Guérin, Alexandre; Pham, Antoine; Servière, Christine

doi:10.1109/aspaa.2009.5346501

Cited by 13 publications

(10 citation statements)

References 4 publications

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“…The role of the "max" pooling function, its success and its potential limits should be better understood, so as to be able to propose more powerful non-linear pooling functions. The resulting localization performance should be assessed for different array geometries and the problem of estimating the number of sources remains open, with few methods proposed so far [29,14,24]. Finally, we still believe in the potential of clustering-based methods provided that the considered i.i.d.…”

Section: Resultsmentioning

confidence: 99%

“…and N (x(t, f ); 0, Ξ x (f, τ, v s , v b )) is defined by (14). We use an EM algorithm [30] to estimate the parameters θ in the maximum likelihood sense, considering the set of predominant source indices {n tf } t,f as latent data.…”

Section: Proposed Methods 321 Em Algorithm With One Predominant Somentioning

confidence: 99%

“…This approach can be used for any microphone spacing but is sensitive to the initialization of the parameters (i.e., clusters and TDOAs). The third approach [3,13,5,14,15] consists of building for each time-frequency bin a function of TDOA that is likely to exhibit a large value for true TDOAs, and pooling it across the time-frequency plane so as to obtain a so-called angular spectrum. The source TDOAs are then estimated as the highest peaks of this angular spectrum.…”

Section: Introductionmentioning

confidence: 99%

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Multi-source TDOA estimation in reverberant audio using angular spectra and clustering

2012

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-source TDOA estimation in reverberant audio using angular spectra and clustering. Signal Processing, Elsevier, 2012Elsevier, , 92, pp.1950Elsevier, -1960Elsevier, . <10.1016Elsevier, /j.sigpro.2011 Multi-source TDOA estimation in reverberant audio using angular spectra and clustering AbstractWe consider the problem of estimating the time differences of arrival (TDOAs) of multiple sources from a two-channel reverberant audio signal. While several clustering-based or angular spectrum-based methods have been proposed in the literature, only relatively small-scale experimental evaluations restricted to either category of methods have been carried out so far. We design and conduct the first large-scale experimental evaluation of these methods and investigate a two-step procedure combining angular spectra and clustering. In addition, we introduce and evaluate five new TDOA estimation methods inspired from signal-to-noise-ratio (SNR) weighting and probabilistic multi-source modeling techniques that have been successful for anechoic TDOA estimation and audio source separation. The results show that clustering-based methods do not improve upon angular spectrum-based methods. For 5 cm microphone spacing, the best TDOA estimation performance is achieved by one of the proposed SNR-based angular spectrum methods. For larger spacing, a variant of the generalized cross-correlation with phase transform (GCC-PHAT) method performs best.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Methods 321 Em Algorithm With One Predominant Somentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-source TDOA estimation in reverberant audio using angular spectra and clustering

2012

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“…PHAT gives us the cross-correlation vector of two microphone output signals. The number of dominant peaks of the cross-correlation vector gives us the number of dominant sound sources [66]. We consider only one source signal to be a periodic signal as:…”

Section: Source Counting Methodsmentioning

confidence: 99%

N-dimensional N-microphone sound source localization

Pourmohammad

Ahadi

2013

J AUDIO SPEECH MUSIC PROC.

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This paper investigates real-time N-dimensional wideband sound source localization in outdoor (far-field) and lowdegree reverberation cases, using a simple N-microphone arrangement. Outdoor sound source localization in different climates needs highly sensitive and high-performance microphones, which are very expensive. Reduction of the microphone count is our goal. Time delay estimation (TDE)-based methods are common for N-dimensional wideband sound source localization in outdoor cases using at least N + 1 microphones. These methods need numerical analysis to solve closed-form non-linear equations leading to large computational overheads and a good initial guess to avoid local minima. Combined TDE and intensity level difference or interaural level difference (ILD) methods can reduce microphone counts to two for indoor two-dimensional cases. However, ILD-based methods need only one dominant source for accurate localization. Also, using a linear array, two mirror points are produced simultaneously (half-plane localization). We apply this method to outdoor cases and propose a novel approach for N-dimensional entire-space outdoor far-field and low reverberation localization of a dominant wideband sound source using TDE, ILD, and headrelated transfer function (HRTF) simultaneously and only N microphones. Our proposed TDE-ILD-HRTF method tries to solve the mentioned problems using source counting, noise reduction using spectral subtraction, and HRTF. A special reflector is designed to avoid mirror points and source counting used to make sure that only one dominant source is active in the localization area. The simple microphone arrangement used leads to linearization of the non-linear closedform equations as well as no need for initial guess. Experimental results indicate that our implemented method features less than 0.2 degree error for angle of arrival and less than 10% error for three-dimensional location finding as well as less than 150-ms processing time for localization of a typical wideband sound source such as a flying object (helicopter).

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“…For two-channel data, a simple approach is to compute the time difference of arrival (TDOA) in each time-frequency bin and to find the peak of the resulting TDOA histogram [10]. Techniques such as generalized cross-correlation with phase transform (GCC-PHAT) [11] and multiple signal classification (MU-SIC) [12] estimate the DOA by finding the peak of a so-called angular spectrum instead [13][14][15][16]. These techniques assume that each time-frequency bin is dominated by the direct component of a single source.…”

Section: Introductionmentioning

confidence: 99%

Keyword Based Speaker Localization: Localizing a Target Speaker in a Multi-speaker Environment

Sivasankaran¹,

Fohr²

2018

Interspeech 2018

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To cite this version:Sunit Sivasankaran, Emmanuel Vincent, Dominique Fohr. Keyword-based speaker localization: Localizing a target speaker in a multi-speaker environment. Interspeech 2018 -19th AbstractSpeaker localization is a hard task, especially in adverse environmental conditions involving reverberation and noise. In this work we introduce the new task of localizing the speaker who uttered a given keyword, e.g., the wake-up word of a distantmicrophone voice command system, in the presence of overlapping speech. We employ a convolutional neural network based localization system and investigate multiple identifiers as additional inputs to the system in order to characterize this speaker.We conduct experiments using ground truth identifiers which are obtained assuming the availability of clean speech and also in realistic conditions where the identifiers are computed from the corrupted speech. We find that the identifier consisting of the ground truth time-frequency mask corresponding to the target speaker provides the best localization performance and we propose methods to estimate such a mask in adverse reverberant and noisy conditions using the considered keyword.

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A phase-based dual microphone method to count and locate audio sources in reverberant rooms

Cited by 13 publications

References 4 publications

Multi-source TDOA estimation in reverberant audio using angular spectra and clustering

Multi-source TDOA estimation in reverberant audio using angular spectra and clustering

N-dimensional N-microphone sound source localization

Keyword Based Speaker Localization: Localizing a Target Speaker in a Multi-speaker Environment

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