The process of locating an acoustic source given measurements of the sound field at multiple microphones is of significant interest as both a classical array signal processing problem, and more recently, as a solution to the problems of automatic camera steering, teleconferencing, hands-free processing, and others. Despite the proven efficacy of steered-beamformer approaches to localization in harsh conditions, their practical application to real-time settings is hindered by undesirably high computational demands. This paper presents a computationally viable implementation of the steered response power (SRP) source localization method. The conventional approach is generalized by introducing an inverse mapping that maps relative delays to sets of candidate locations. Instead of traversing the three-dimensional location space, the one-dimensional relative delay space is traversed; at each lag, all locations which are inverse mapped by that delay are updated. This means that the computation of the SRP map is no longer performed sequentially in space. Most importantly, by subsetting the space of relative delays to only those that achieve a high level of cross-correlation, the required number of algorithm updates is drastically reduced without compromising localization accuracy. The generalization is scalable in the sense that the level of subsetting is an algorithm parameter. It is shown that this generalization may be viewed as a spatial decomposition of the SRP energy map into weighted basis functions-in this context, it becomes evident that the full SRP search considers all basis functions (even the ones with very low weighting). On the other hand, it is shown that by only including a few basis functions per microphone pair, the SRP map is quite accurately represented. As a result, in a real environment, the proposed generalization achieves virtually the same anomaly rate as the full SRP search while only performing 10% the amount of algorithm updates as the full search.

A Generalized Steered Response Power Method for Computationally Viable Source Localization

Interaural level difference (ILD) is an important cue for acoustic localization. Although its behavior has been studied extensively in natural systems, it remains an untapped resource for computer-based systems. We investigate the possibility of using ILD for acoustic localization, deriving constraints on the location of a sound source given the relative energy level of the signals received by two microphones. We then present an algorithm for computing the sound source location by combining likelihood functions, one for each microphone pair. Experimental results show that accurate acoustic localization can be achieved using ILD alone.

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Acoustic localization by interaural level difference

We propose a new two-stage framework for time delay estimation in the presence of correlated noise and reverberation. The new framework allows us to develop a set of new approaches as well as to unify existing ones. We further develop the maximum likelihood estimation when reverberation is present. The corresponding weighting function is a more accurate form of the weighting function proposed by H. Wang and P. Chu (Proc. ICASSP, 1997), one of the best existing techniques. We compare our new algorithms with the existing ones and report superior performance.

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Time delay estimation in the presence of correlated noise and reverberation

This paper proposes a new DOA (direction of arrival) estimation method based on circular microphone array. For an arbitrary number of microphones, it is analytically shown that DOA estimation reduces to an efficient non-linear optimization problem. Simulation results demonstrate that deviation of the estimation error for 20 and 10 dB SNR is smaller than 0.7 degree which is comparable to high resolution DOA estimation methods. A larger number of microphones provide a more omni-directional spatial resolution.

A new DOA estimation method using a circular microphone array

When more than two microphones are used, the traditional time-delay-of-arrival (TDOA) based sound source localization (SSL) approach involves two steps. The first step computes TDOA for each microphone pair, and the second step combines these estimates. This two-step process discards relevant information in the first step, thus degrading the SSL accuracy and robustness. Although less used, one-step processes do exist. In this paper, we review these processes, create a unified framework, and introduce two new one-step algorithms. We compare our proposed approaches against existing 1and 2-step approaches and demonstrate significantly better SSL performance.

/pdf/new-direct-approaches-to-robust-sound-source-localization-t4ts3fpptv.pdf

New direct approaches to robust sound source localization

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.

S.T. Birchfield

Papers

Acoustic source direction by hemisphere sampling