3D DOA estimation of multiple sound sources based on spatially constrained beamforming driven by intensity vectors

Abstract

Sound source localization in three dimensions with microphone arrays is an active field of research, applicable in sound enhancement, source separation, and sound field analysis. In this contribution we propose a method for three dimensional multiple sound source localization in reverberant environments. We employ a spatially constrained steered response beamformer on a spherical sector centered at the direction of arrival (DOA) estimates of the intensity vector. Experiments are performed in both simulated and real acoustical environments with a spherical microphone array for multiple sound sources under different reverberation and signal-to-noise ratio (SNR) conditions. The performance of the proposed method is compared with our previously proposed work and a subspace method in the spherical harmonic domain. The results demonstrate a significant improvement in terms of localization accuracy.Index Termsdirection of arrival, 3D, multiple sources, spherical microphone array processing, sound intensity

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

“…The employed SC-SRP is the generalized variation of the proposed method in [48] in which SC-SRP is applied only on the TF bins with an active single source unlike our SC-SRP which is applied on all TF bins. The radius of spherical cap search window in AIV-GS and SC-SRP was set to 10°as, in our experiments, more than 95% of PIVs were within 10°of the true DOA.…”

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

“…14. From (22) and (27), the computational cost of calculating PIVs and SSPIVs does not depend on the resolution of the DOA analysis. However, the grid density determines the number of directions for which the summation in (54) must be performed.…”

“…Whilst (27) depends only on the 0 and 1st order components of U s , through (25) and (26), their values do depend on the higher order SH terms of a lm . As with the PIV method, the benefit of this approach is that a direction is obtained for each TF-region directly, i.e.…”

“…Without loss of generality, let x 1 point in the direction of the desired source such that the Signal-to-Interference Ratio (SIR) in dB is 10 log 10 (α Figure 2 shows the error angle ∠(x 1 ,Ĩ ss ) as a function of the interferer angle ∠(x 1 , x 2 ) for SIRs of 20 dB and 3 dB and for different values of L. These plots were produced by collating, without averaging, SSPIVs calculated according to (25), (26) and (27) for interferers at 794 approximately equally distributed directions and 5 random target directions. The variation in error as a function of interferer angle has multiple peaks and nulls corresponding to the number of lobes in the real (or imaginary) part of highest order SH considered but is independent of the target direction.…”

“…In [11] DOA estimation of multiple sources was achieved using k-means clustering of PIVs. In both [27] and [28] a DOA was obtained for each TF-bin by first finding the PIV and then refining the direction by evaluating a cost function using higher order SHs over a spatially constrained grid around the PIV direction. The final estimates of multiple sources' DOAs were then obtained by identifying the peaks of a histogram of all the individual direction estimates.…”