Similarity-based Sound Source Localization with a Coincident Microphone Array
This paper presents a robust, accurate sound source localization method using a compact, near-coincident microphone array. We derive features by combining the microphone signals and determine the direction of a single sound source by similarity matching. Therefore, the observed features are compared with a set of previously measured reference features, which are stored in a look-up table. By proper processing in the similarity domain, we are able to deal with signal pauses and low SNR without the need of a separate detection algorithm. For practical evaluation, we made recordings of speech signals (both loudspeaker-playback and human speaker) with a planar 4-channel prototype array in a medium-sized room. The proposed approach clearly outperforms existing coincident localization methods. We achieve high accuracy (2◦ mean absolute azimuth error at 0 dB SNR) for static sources, while being able to quickly follow rapid source angle changes.