Measurement combination for acoustic source localization in a room environment
EURASIP Journal on Audio, Speech, and Music Processing - Intelligent Audio, Speech, and Music Processing Applications
IEEE Transactions on Signal Processing
| Hi-index | 0.00 |
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 sea- rch.