Particle methods for real-time sound source localization based on the Multiple Signal Classification algorithm

Hao, H.C.; Liang, Hang-Ming; Liu, Yiwen

doi:10.1109/igbsg.2014.6835269

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Traditional techniques like MUSIC [36], [37], [38] uses techniques such as correlation and eigen-space analysis for a robust and relatively good results. The effectiveness of these techniques depends on the success of the correlation and eigen-space analysis.…”

Section: Rank-one Correlated Linear Array Signal Model For Noise Suppmentioning

confidence: 99%

Sparse frame DOA estimations via a rank-one correlation model for low SNR and limited snapshots

Zeng

Zhu

et al. 2016

Applied and Computational Harmonic Analysis

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As a typical problem of sparse frame representations or compressed sensing with frames, direction of arrival (DOA) estimations, via sparse recovery methodologies such as nonlinear optimizations or various greedy algorithms, suffer seriously under severe noisy measurement without proper treatment. In this article, a crucial and effective correlation operation is outlined to mitigate the severe noise effect from the sparse representation point of view. A fast and super resolution method for DOA estimations is therefore established under very low signal-to-noise ratio, and by a sparse recovery technique of null space tuning in the context of compressed sensing and sparse representations. Improvement to the technique using the thresholding difference and alternating grid refinements are also outlined to estimate the source/target numbers dynamically and to improve the precision. Another characteristic is that only a very small number of snapshots are needed in comparison with other techniques. Simulation results demonstrate apparent advantages of the proposed technique over known approaches. The proposed method has the effectiveness at simultaneous high resolution, robustness to noise, and source number estimations. The algorithm is also computationally efficient, which is critical for large array systems and/or mission critical real-time applications.

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Section: Rank-one Correlated Linear Array Signal Model For Noise Suppmentioning

confidence: 99%

Sparse frame DOA estimations via a rank-one correlation model for low SNR and limited snapshots

Zeng

Zhu

et al. 2016

Applied and Computational Harmonic Analysis

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“…Regardless of the specific architectural considerations, many SEL systems incorporate temporal context into the estimation process. While classical methods usually rely on recursive Bayesian estimation techniques [15,16], modern systems make use of recurrent structures like long-short term memory (LSTM) networks [17]. However, conventional neural network-based systems lack the ability to associate uncertainty with the estimated sound event locations; a feature that tradi- tional Bayesian approaches naturally provide.…”

Section: Introductionmentioning

confidence: 99%

PILOT: Introducing Transformers for Probabilistic Sound Event Localization

Schymura¹,

Bönninghoff²,

Ochiai³

et al. 2021

Preprint

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Sound event localization aims at estimating the positions of sound sources in the environment with respect to an acoustic receiver (e.g. a microphone array). Recent advances in this domain most prominently focused on utilizing deep recurrent neural networks. Inspired by the success of transformer architectures as a suitable alternative to classical recurrent neural networks, this paper introduces a novel transformer-based sound event localization framework, where temporal dependencies in the received multi-channel audio signals are captured via self-attention mechanisms. Additionally, the estimated sound event positions are represented as multivariate Gaussian variables, yielding an additional notion of uncertainty, which many previously proposed deep learning-based systems designed for this application do not provide. The framework is evaluated on three publicly available multi-source sound event localization datasets and compared against state-of-the-art methods in terms of localization error and event detection accuracy. It outperforms all competing systems on all datasets with statistical significant differences in performance.

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“…It has been highly valued in both military and civilian fields [2,3] .Therefore, in order to realize sound source location, researchers have proposed many effective sound source location algorithms. At present, the localization algorithm of this technology is mainly divided into three categories: acoustic source localization algorithm based on arrival delay [4] , acoustic source localization algorithm based on high resolution spectrum estimation [5,6] and acoustic source localization algorithm based on beamforming [7,8] .The DOA estimation's accuracy of MUSIC algorithm based on highresolution spectral estimation is better than other algorithms, which consequently has been studied and improved by many scholars. Literature [9]proposed the Chicken swarm optimization based on MUSIC algorithm, which improves the accuracy of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Sound source location for low-altitude aircraft based on sub-band extraction

Zhu¹,

Cheng²,

Li³

et al. 2021

MATEC Web Conf.

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The acoustic signal of low-altitude aircraft shows regular distribution in frequency and has obvious harmonic crest of both fundamental frequency and double frequency.Therefore, this paper presents a low complexity algorithm of acoustic location based on feature sub-band extraction for low-altitude aircraft. The algorithm firstly searches the eigenfrequency points which occupy the main energy in the sound signal. Then the cost function is constructed based on the MUSIC method by the sub-band corresponding to the eigenfrequency point. Finally, the amplitude is weighted by the maximum ratio combination principle to obtain the spectral function of array space, by which DOA estimation is realized for the spatial spectrum. Simulation results show that the algorithm is less complex than traditional wide-band DOA algorithm, and its main lobe is easier to recognize and has better spatial resolution.

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Particle methods for real-time sound source localization based on the Multiple Signal Classification algorithm

Cited by 8 publications

References 4 publications

Sparse frame DOA estimations via a rank-one correlation model for low SNR and limited snapshots

Sparse frame DOA estimations via a rank-one correlation model for low SNR and limited snapshots

PILOT: Introducing Transformers for Probabilistic Sound Event Localization

Sound source location for low-altitude aircraft based on sub-band extraction

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