DOA estimation method for an arbitrary triangular microphone arrangement

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

A DOA Estimation Method For an Arbitrary Triangular Microphone Arrangement

This paper proposes a new DOA (direction of arrival) estimation method for an arbitrary triangular microphone arrangement. Using the phase rotation factors for the crosscorrelations between the adjacent-microphone signals, a general form of the integrated cross spectrum is derived. DOA estimation is reduced to a non-linear optimization problem of the general integrated cross spectrum. It is shown that a conventional DOA estimation for the equilateral triangular microphone arrangement is a special case of the proposed method. Sensitivity to the relative time-delay is derived in a closed form and demonstrated for different microphone arrangements. Simulation results demonstrate that the deviation of estimation error in the case of 20 dB SNR is less than 1 degree which is comparable to high resolution DOA estimation methods

Localization of sound sources by means of unidirectional microphones

This paper describes the results of a new approach devoted to the localization of ground borne acoustic sources. It is demonstrated that an array made of at least three unidirectional microphones can be exploited to identify the position of the source. Sound features extracted either in the time domain or in the frequency domain are used to localize the direction of the incoming sound. This information is then fed into a semi-analytical algorithm aimed at identifying the source location. The novelty of the method presented here consists in the use of unidirectional microphones rather than omnidirectional microphones and in the ability to extract the sound direction by considering features like sound amplitude rather than the time of arrival. Experimental tests have been undertaken in a closed environment and have demonstrated the feasibility of the proposed approach. It is believed that this method may pave the road toward a new generation of reduced-size sound detectors and localizers, and future work is described in the conclusions. © 2009 IOP Publishing Ltd

Multichannel source separation and tracking with phase differences by random sample consensus

Blind audio source separation (BASS) is a fascinating problem that has been tackled from many different angles. The use case of interest in this thesis is that of multiple moving and simultaneously-active speakers in a reverberant room. This is a common situation, for example, in social gatherings. We human beings have the remarkable ability to focus attention on a particular speaker while effectively ignoring the rest. This is referred to as the ``cocktail party effect'' and has been the holy grail of source separation for many decades. Replicating this feat in real-time with a machine is the goal of BASS. Single-channel methods attempt to identify the individual speakers from a single recording. However, with the advent of hand-held consumer electronics, techniques based on microphone array processing are becoming increasingly popular. Multichannel methods record a sound field from various locations to incorporate spatial information. If the speakers move over time, we need an algorithm capable of tracking their positions in the room. For compact arrays with 1-10 cm of separation between the microphones, this can be accomplished by applying a temporal filter on estimates of the directions-of-arrival (DOA) of the speakers. In this thesis, we review recent work on BSS with inter-channel phase difference (IPD) features and provide extensions to the case of moving speakers. It is shown that IPD features compose a noisy circular-linear dataset. This data is clustered with the RANdom SAmple Consensus (RANSAC) algorithm in the presence of strong reverberation to simultaneously localize and separate speakers. The remarkable performance of RANSAC is due to its natural tendency to reject outliers. To handle the case of non-stationary speakers, a factorial wrapped Kalman filter (FWKF) and a factorial von Mises-Fisher particle filter (FvMFPF) are proposed that track source DOAs directly on the unit circle and unit sphere, respectively. These algorithms combine directional statistics, Bayesian filtering theory, and probabilistic data association techniques to track the speakers with mixtures of directional distributions

Sensor fusion for tangible acoustic interfaces for human computer intreraction

This thesis presents the development of tangible acoustic interfaces for human computer interaction. The method adopted was to position sensors on the surface of a solid object to detect acoustic waves generated during an interaction, process the sensor signals and estimate either the location of a discrete impact or the trajectory of a moving point of contact on the surface. Higher accuracy and reliability were achieved by employing sensor fusion to combine the information collected from redundant sensors electively positioned on the solid object. Two different localisation approaches are proposed in the thesis. The learning-based approach is employed to detect discrete impact positions. With this approach, a signature vector representation of time-series patterns from a single sensor is matched with database signatures for known impact locations. For improved reliability, a criterion is proposed to extract the location signature from two vectors. The other approach is based on the Time Difference of Arrival (TDOA) of a source signal captured by a spatially distributed array of sensors. Enhanced positioning algorithms that consider near-field scenario, dispersion, optimisation and filtration are proposed to tackle the problems of passive acoustic localisation in solid objects. A computationally efficient algorithm for tracking a continuously moving source is presented. Spatial filtering of the estimated trajectory has been performed using Kalman filtering with automated initialisation

Acoustic Source Direction By Hemisphere Sampling

A method for estimating the direction to a sound source, using a compact array of microphones, is presented. For each pair of microphones, the signals are prefiltered and correlated. Rather than taking the peak of the correlation vectors as estimates for the time delay between the microphones, all the correlation vectors are accumulated in a common coordinate system, namely a unit hemisphere centered on the microphone array. The maximum cell in the hemisphere then indicates the azimuthal and elevation angles to the source. Unlike previous techniques, this algorithm is applicable to arbitrary microphone configurations, handles more than two microphone pairs, and has no blind spots. Experiments demonstrate significantly increased robustness to noise, compared with previous techniques