3D acoustic source localization in the spherical harmonic domain based on optimized grid search

2016 24th European Signal Processing Conference (EUSIPCO)

2016

Self Cite

Multiple source localization is an important task in acoustic signal processing with applications including dereverberation, source separation, source tracking and environment mapping. When using spherical microphone arrays, it has been previously shown that Pseudo-intensity Vectors (PIV), and Augmented Intensity Vectors (AIV), are an effective approach for direction of arrival estimation of a sound source. In this paper, we evaluate AIV-based localization in acoustic scenarios involving multiple sound sources. Simulations are conducted where the number of sources, their angular separation and the reverberation time of the room are varied. The results indicate that AIV outperforms PIV and Steered Response Power (SRP) with an average accuracy between 5 and 10 degrees for sources with angular separation of 30 degrees or more. AIV also shows better robustness to reverberation time than PIV and SRP.

Section: Introductionmentioning

confidence: 99%

Section: Augmented Intensity Vectorsmentioning

confidence: 99%

Multiple source localization in the spherical harmonic domain using augmented intensity vectors based on grid search

2016 24th European Signal Processing Conference (EUSIPCO)

2016

Self Cite

“…The initial DOA estimates (one per TF bin) can be obtained by any SS DOA estimator such as PIV [10], as used in this work, Augmented Intensity Vectors (AIVs) [12], [13], Steered Response Power-based methods [14] or SS MUSIC. The initial DOA estimates are weighted based on their consistency within a time interval and the ones with the strongest weights are selected as the most consistent DOAs using the assumption of stationary sources.…”

Section: A Msecmentioning

confidence: 99%

Multiple DOA estimation based on estimation consistency and spherical harmonic multiple signal classification

2017 25th European Signal Processing Conference (EUSIPCO)

2017

Self Cite

Abstract-A common approach to multiple Direction-ofArrival (DOA) estimation of speech sources is to identify TimeFrequency (TF) bins with dominant Single Source (SS) and apply DOA estimation such as Multiple Signal Classification (MUSIC) only on those TF bins. In the state-of-the-art Direct Path Dominance (DPD)-MUSIC, the covariance matrix, used as the input to MUSIC, is calculated using only the TF bins over a local TF region where only a SS is dominant. In this work, we propose an alternative approach to MUSIC in which all the SS-dominant TF bins for each speaker across TF domain are globally used to improve the quality of covariance matrix for MUSIC. Our recently proposed Multi-Source Estimation Consistency (MSEC) technique, which exploits the consistency of initial DOA estimates within a time frame based on adaptive clustering, is used to estimate the SS-dominant TF bins for each speaker. The simulation using spherical microphone array shows that our proposed MSEC-MUSIC significantly outperforms the state-of-the-art DPD-MUSIC with less than 6.5°mean estimation error and strong robustness to widely varying source separation for up to 5 sources in the presence of realistic reverberation and sensor noise.

“…PIVs are computationally efficient and have been shown in [17] to offer good localization accuracy for a single source in the absence of strong reflections. In common with most localization algorithms however, localization accuracy is reduced as the level of reverberation, the sensor noise or the number of sources increase [16], [38], [37]. An extension of PIV is Subspace PIV (SSPIV) [37] where the low order (≤ 1) spatial information of signal subspace is used to enhance the accuracy of PIV DOA estimates.…”

mentioning

confidence: 99%

“…In [38], [23] we proposed Augmented Intensity Vectors (AIVs) which exploits eigenbeams of order ≥ 2 to form vectors with improved DOA accuracy compared to PIVs. These vectors are obtained using spatially constrained grid search to minimize a cost function with initialisation derived from PIVs.…”

mentioning

confidence: 99%

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

IEEE/ACM Trans. Audio Speech Lang. Process.

2017

Self Cite

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.