DOA estimation with histogram analysis of spatially constrained active intensity vectors

Abstract

The active intensity vector (AIV) is a common descriptor of the sound field. In microphone array processing, AIV is commonly approximated with beamforming operations and utilized as a direction of arrival (DOA) estimator. However, in its original form, it provides inaccurate estimates in sound field conditions where coherent sound sources are simultaneously active. In this work we utilize a higher order intensitybased DOA estimator on spatially-constrained regions (SCR) to overcome such limitations. We then apply 1-dimensional (1D) histogram processing on the noisy estimates for multiple DOA estimation. The performance of the estimator is shown with a 7-channel microphone array, fitted on a rigid mobile-like device, in reverberant conditions and under different signal-to-noise ratios.

Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained With Noise Signals

“…Over the years, several approaches have been developed for the task of broadband DOA estimation. Some popular approaches are: i) subspace based approaches such as multiple signal classification (MUSIC) [1], [2], ii) time difference of arrival (TDOA) based approaches that use the family of generalized cross correlation (GCC) methods [3], [4], iii) generalizations of the cross-correlation methods such as steered response power with phase transform (SRP-PHAT) [5], and multichannel cross correlation coefficient (MCCC) [6], iv) adaptive multichannel time delay estimation using blind system identification based methods [7], v) probabilistic model based methods such as maximum likelihood method [8] and vi) methods based on histogram analysis of narrowband DOA estimates [9], [10]. These methods are generally formulated under the assumption of free-field propagation of sound waves, however in indoor acoustic environments this assumption is violated due to the presence of reverberation leading to severe degradation in their performance.…”

Applications of Spatially Localized Active-Intensity Vectors for Sound-Field visualization

“…Alternatively, by performing histogram analysis on the estimated DoAs, with a sufficiently long temporal window, one may also generate more traditional activity maps [8,9]; an approach which still retains much of the computational benefits granted by the SLAI approach.…”

“…Once the DoA estimates have been extracted from multiple sectors, sound-field visualization may be subsequently established using one of the following three approaches, which are detailed and evaluated in this work: I) Directly depicting the estimates as icons, with their relative size dictated by the corresponding energy of each sector [7]. II) Generating traditional activity maps via histogram analysis of the DoA estimates [8,9]. III) Using the DoA estimates to reassign energy and subsequently sharpen traditional beamformer-based activity maps [10].…”

Sharpening of Angular Spectra Based on a Directional Re-assignment Approach for Ambisonic Sound-field Visualisation

“…Such a spatially-localised intensity vector was first used for directional analysis and sound-field reproduction in [11], and further formulated in [12,13]. Recently, it has been used for DoA estimation using a histogram-clustering approach [14]. Herein, the sharpening operation is both non-parametric and preserves the directional energy of the recording, which is a desirable characteristic for visualisation and acoustic analysis purposes.…”