Acoustic direction of arrival estimation, a comparison between root-music and SRP-PHAT

Johansson, Anders; Cook, Gregory J.; Nordholm, Sven

doi:10.1109/tencon.2004.1414674

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…Apopular approach for TDOA-based localization is the steered response power (SRP), which has been studied extensively [36,37,38,7,8,39,25,10,40] and it has been followed by various modifications and optimizations [38,39,40]. In the SRP-TDOA method the TDOAestimation functions are combined spatially by addition.…”

Section: Tdoaestimation and Tdoa-based Localizationmentioning

confidence: 99%

Acoustic Reflection Localization from Room Impulse Responses

Tervo¹,

Pätynen²,

Lokki³

2012

Acta Acustica united with Acustica

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The localization of acoustic reflections, i.e., the image-sources, is of interest when analyzing the acoustics of concert halls and auditoriums. The location is needed, for example, in room acoustic studies, auralization, inference of room geometry,orwhen estimating the acoustic properties of surfaces. This article studies the localization of acoustic reflections from spatial impulse responses. The contribution of this article is threefold. First, the article proposes anew method for localization that takes advantage of the time of arrival(TOA) estimation. Secondly, it is proposed that TOAand time difference of arrival(TDOA) information, present in the spatial room impulse responses, are combined in twon ovelw ays. Thirdly,t he performance of the proposed localization methods is compared to the existing state-of-the-art localization methods in the acoustic reflection localization task. Theoretical performance is investigated and experiments using real and simulated data are conducted. The TOA-based methods are found to achieve reasonably good performance in the reflection localization task. When TOAa nd TDOAinformation is combined the performance clearly improves. PACS no. 43.60.Hg, 43.60.Jn 418 ©S.Hirzel Verlag · EAA Tervo et al.:R eflection localization from impulse responses ACTA ACUSTICA UNITED WITH ACUSTICA Vol. 98 (2012) SRP-TOA MLE-S ACTA ACUSTICA UNITED WITH ACUSTICA Vol. 98 (2012)

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Section: Tdoaestimation and Tdoa-based Localizationmentioning

confidence: 99%

Acoustic Reflection Localization from Room Impulse Responses

Tervo¹,

Pätynen²,

Lokki³

2012

Acta Acustica united with Acustica

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“…There are existing methods reported in the literature for the estimation of DoA such as estimation of subspace rotational in variance technology (ESPRIT) algorithm, multiple signal classification (MUSIC), MUSIC group delay [11,18,19], the steered response power with phase transform (SRP-PHAT) [30][31][32][33][34][35], generalized cross-correlation (GCC) [36], adaptive eigenvalue decomposition [37], onedimensional MUSIC [38], and order aware method [39]. MUSIC has been applied to the SH structure in NF (MUSIC-SH) [33,[40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Source Localization of EM Waves in the Near‐Field of Spherical Antenna Array in the Presence of Unknown Mutual Coupling

Famoriji

Shongwe

2021

Wireless Communications and Mobile Computing

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To obtain an antenna array with isotropic radiation, spherical antenna array (SAA) is the right array configuration. The challenges of locating signals transmitted within the proximity of antenna array have been investigated considerably in the literature. However, near-field (NF) source localization of signals has hitherto not been investigated effectively using SAA in the presence of mutual coupling (MC). MC is another critical problem in antenna arrays. This paper presents an NF range and direction-of-arrival (DoA) estimation technique via the direction-independent and signal invariant spherical harmonics (SH) characteristics in the presence of mutual coupling. The energy of electromagnetic (EM) signal on the surface of SAA is captured successfully using a proposed pressure interpolation approach. The DoA estimation within the NF region is then calculated via the distribution of pressure. The direction-independent and signal invariant characteristics, which are SH features, are obtained using the DoA estimates in the NF region. We equally proposed a learning scheme that uses the source activity detection and convolutional neural network (CNN) to estimate the range of the NF source via the direction-independent and signal invariant features. Considering the MC problem and using the DoA estimates, an accurate spectrum peak in the multipath situation in conjunction with MC and a sharper spectrum peak from a unique MC structure and smoothing algorithms are obtained. For ground truth performance evaluation of the SH features within the context of NF localization, a numerical experiment is conducted and measured data were used for analysis to incorporate the MC and consequently computed the root mean square error (RMSE) of the source range and NF DoA estimate. The results obtained from numerical experiments and measured data indicate the validity and effectiveness of the proposed approach. In addition, these results are motivating enough for the deployment of the proposed method in practical applications.

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“…Here, the spatial features can be exploited per frequency band (narrowband) or from all frequency bands (broadband). Signal processing-based methods to estimate the DOA from these features can be based on the inter-microphone cross-correlations [11][12][13], beamformers [14][15][16] or subspaces [16][17][18][19][20][21][22]. The inter-microphone cross-correlations, like the generalized-cross-correlation [23] with maximum likelihood or phase transform (PHAT) [13] weighting exploit the DOA and frequency dependency of the inter-microphone phase-differences to estimate the DOA.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, the DOA can be estimated using subspace-based methods like MUltiple SIgnal Classification (MUSIC) [16][17][18], based on noise subspaces, or estimation of signal parameters via rotational invariant techniques [19][20][21][22], based on sub-arrays. In [26], the authors exploit reflection patterns using semi-supervised manifold learning with a distributed microphone array to localize a single source.…”

Section: Introductionmentioning

confidence: 99%

“…In [24], a least square approach is used to minimize the phase differences obtained from measurements and an estimated DOA. Beamforming techniques like steered-response power, e.g., with PHAT weighting [15,16] (SRP-P), or a steered minimum-variance distortionless response beamformer [14] sample the DOA space by steering in pre-defined directions. The DOA is subsequently estimated by maximum picking.…”

Section: Introductionmentioning

confidence: 99%

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Signal-Aware Broadband DOA Estimation Using Attention Mechanisms

Mack

Bharadwaj

Chakrabarty

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The direction-of-arrival (DOA) of sound sources is an essential acoustic parameter used, e.g., for multi-channel speech enhancement or source tracking. Complex acoustic scenarios consisting of sources-of-interest, interfering sources, reverberation, and noise make the estimation of the DOAs corresponding to the sources-of-interest a challenging task. Recently proposed attention mechanisms allow DOA estimators to focus on the sources-of-interest and disregard interference and noise, i.e., they are signal-aware. The attention is typically obtained by a deep neural network (DNN) from a short-time Fourier transform (STFT) based representation of a single microphone signal. Subsequently, attention has been applied as binary or ratio weighting to STFT-based microphone signal representations to reduce the impact of frequency bins dominated by noise, interference, or reverberation. The impact of attention on DOA estimators and different training strategies for attention and DOA DNNs are not yet studied in depth. In this paper, we evaluate systems consisting of different DNNs and signal processing-based methods for DOA estimation when attention is applied. Additionally, we propose training strategies for attention-based DOA estimation optimized viaa DOA objective, i.e., end-to-end. The evaluation of the proposed and the baseline systems is performed using data generated with simulated and measured room impulse responses under various acoustic conditions, like reverberation times, noise, and source array distances. The best-performing systems are also evaluated using measured data. Our experiments show that DNNs used for DOA estimation are biased to the spectral source characteristics and the spectral attention distribution used during training (e.g., spectrally flat/sparse). We also show that this bias in the DOA estimator can be avoided if signal-processing methods are used in combination with attention. Overall, DOA estimation using attention in combination with signal-processing methods exhibits a far lower computational complexity than a fully DNN-based system; however, it yields comparable results.

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Acoustic direction of arrival estimation, a comparison between root-music and SRP-PHAT

Cited by 13 publications

References 6 publications

Acoustic Reflection Localization from Room Impulse Responses

Acoustic Reflection Localization from Room Impulse Responses

Source Localization of EM Waves in the Near‐Field of Spherical Antenna Array in the Presence of Unknown Mutual Coupling

Signal-Aware Broadband DOA Estimation Using Attention Mechanisms

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