3D localization of multiple sound sources with intensity vector estimates in single source zones

Pavlidi, Despoina; Delikaris-Manias, Symeon; Pulkki, Ville; Mouchtaris, Athanasios

doi:10.1109/eusipco.2015.7362645

Cited by 38 publications

(39 citation statements)

References 15 publications

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“…At the optimized look direction Ω s , we have Ψ (Ω s ) = 0, which by substituting (36) and (37) into (34) gives…”

Section: Doa Error Analysismentioning

confidence: 99%

“…Statistical DOA estimation methods using directional sparsity of sound sources have also been proposed in [31], [32]. (4) Intensity-based methods [33] determine the direction and the magnitude of the flow of sound energy within a narrow frequency band [17], [34], [35], [16], [36], [37]. Pseudointensity vector (PIV) [17] methods use sound field information with low spatial resolution.…”

mentioning

confidence: 99%

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Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Hafezi

Moore

Naylor

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Pseudointensity vectors (PIVs) provide a means of Direction of Arrival (DOA) estimation for Spherical Microphone Arrays (SMAs) using only the zeroth and the first-order spherical harmonics. An Augmented Intensity Vector (AIV) is proposed which improves the accuracy of PIVs by exploiting higher order spherical harmonics. We compared DOA estimation using our proposed AIVs against PIVs, Steered Response Power (SRP) and subspace methods where the number of sources, their angular separation, the reverberation time of the room and the sensor noise level are varied. The results show that the proposed approach outperforms the baseline methods and performs at least as accurately as the state-of-the-art method with strong robustness to reverberation, sensor noise and number of sources. In the single and multiple source scenarios tested, which include realistic levels of reverberation and noise, the proposed method had average error of 1.5°and 2°, respectively.

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“…At the optimized look direction Ω s , we have Ψ (Ω s ) = 0, which by substituting (36) and (37) into (34) gives…”

Section: Doa Error Analysismentioning

confidence: 99%

mentioning

confidence: 99%

Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Hafezi

Moore

Naylor

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

show abstract

“…Therefore to approximate (14) using the SH coefficients of the plane-wave density function the PIV is formulated [8] I…”

Section: A Review Of Sound Intensity and Pseudo-intensitymentioning

confidence: 99%

“…Intensity-based DOA estimation [8], [11], [13], [14] differs from the previously discussed methods because, by directly computing the direction of energy flow, there is no need to compute a spatial cost function. This has the potential for significant computational savings.…”

Section: Introductionmentioning

confidence: 99%

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Direction of Arrival Estimation in the Spherical Harmonic Domain Using Subspace Pseudointensity Vectors

Moore

Evers

Naylor

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Direction of Arrival (DOA) estimation is a fundamental problem in acoustic signal processing. It is used in a diverse range of applications, including spatial filtering, speech dereverberation, source separation and diarization. Intensity vector-based DOA estimation is attractive, especially for spherical sensor arrays, because it is computationally efficient. Two such methods are presented which operate on a spherical harmonic decomposition of a sound field observed using a spherical microphone array. The first uses Pseudo-Intensity Vectors (PIVs) and works well in acoustic environments where only one sound source is active at any time. The second uses Subspace Pseudo-Intensity Vectors (SSPIVs) and is targeted at environments where multiple simultaneous sources and significant levels of reverberation make the problem more challenging. Analytical models are used to quantify the effects of an interfering source, diffuse noise and sensor noise on PIVs and SSPIVs. The accuracy of DOA estimation using PIVs and SSPIVs is compared against the state-of-the-art in simulations including realistic reverberation and noise for single and multiple, stationary and moving sources. Finally, robust performance of the proposed methods is demonstrated using speech recordings in real acoustic environments.

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Sound Source Localization Based on the Simple Cross-correlation Method and Probabilistic Neural Networks

Tsybulskaya

Пермяков

Drugov

et al. 2019

Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin's Readings”

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3D localization of multiple sound sources with intensity vector estimates in single source zones

Cited by 38 publications

References 15 publications

Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain

Direction of Arrival Estimation in the Spherical Harmonic Domain Using Subspace Pseudointensity Vectors

Sound Source Localization Based on the Simple Cross-correlation Method and Probabilistic Neural Networks

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