Bearing-only acoustic tracking of moving speakers for robot audition

This paper focuses on speaker tracking in robot audition for human-robot interaction. Using only acoustic signals, speaker tracking in enclosed spaces is subject to missing detections and spurious clutter measurements due to speech inactivity, reverberation and interference. Furthermore, many acoustic localization approaches estimate speaker direction, hence providing bearing-only measurements without range information. This paper presents a probability hypothesis density (PHD) tracker that augments the bearing-only speaker directions of arrival with a cloud of range hypotheses at speaker initiation and propagates the random variates through time. Furthermore, due to their formulation PHD filters explicitly model, and hence provide robustness against, clutter and missing detections. The approach is verified using experimental results

Feedback control of sound

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Frequency-domain adaptation of causal digital filters

The adaptation of causal FIR digital filters in the discrete frequency domain is considered, and it is shown how the bin-normalized form of the LMS algorithm can converge to a biased solution for problems such as linear prediction. A discrete frequency-domain version of Newton's algorithm is derived, and it is demonstrated how this can converge to the optimal causal solution, even for linear prediction problems. The algorithm employs a spectral factorization of the estimated power spectral density of the reference signal, the entirely noncausal part of which is used before the causality constraint in the adaptation algorithm, and the entirely causal part is applied after the causality constraint. The spectral factors can be calculated online from a recursive estimate of the power spectral density without too great a loss of convergence speed. The extension of the algorithm to the adaptation of feedforward controllers is also described, in which case, the spectral factors of the reference signals filtered by the plant response are required, and these are shown to be equal to the spectral factors of the reference signal multiplied by the minimum phase part or the plant frequency response

Sound-field analysis by plane-wave decomposition using spherical microphone array

Directional sound-field information is becoming more important in sound-field analysis and auditorium acoustics, and, as a consequence, a variety of microphone arrays have recently been studied that provide such information. In particular, spherical microphone arrays have been proposed that provide three-dimensional information by decomposing the sound field into spherical harmonics. The theoretical formulation of the plane-wave decomposition and array performance analysis were also presented. In this paper, as a direct continuation of the recent work, a spherical microphone array configured around a rigid sphere is designed, analyzed using simulation, and then used experimentally to decompose the sound field in an anechoic chamber and an auditorium into waves. The array employs a maximum of 98 measurement positions around the sphere, and is used to compute spherical harmonics up to order 6. In the current paper we investigate the factors affecting the performance of plane-wave decomposition, showing that the direct sound and several reflections in an auditorium can be identified experimentally. This suggests that the microphone arrays studied here can be employed in various acoustic applications to identify the characteristics of reverberant sound fields