Clustered blind beamforming from ad-hoc microphone arrays
Data(s) |
2010
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Resumo |
Microphone arrays have been used in various applications to capture conversations, such as in meetings and teleconferences. In many cases, the microphone and likely source locations are known \emph{a priori}, and calculating beamforming filters is therefore straightforward. In ad-hoc situations, however, when the microphones have not been systematically positioned, this information is not available and beamforming must be achieved blindly. In achieving this, a commonly neglected issue is whether it is optimal to use all of the available microphones, or only an advantageous subset of these. This paper commences by reviewing different approaches to blind beamforming, characterising them by the way they estimate the signal propagation vector and the spatial coherence of noise in the absence of prior knowledge of microphone and speaker locations. Following this, a novel clustered approach to blind beamforming is motivated and developed. Without using any prior geometrical information, microphones are first grouped into localised clusters, which are then ranked according to their relative distance from a speaker. Beamforming is then performed using either the closest microphone cluster, or a weighted combination of clusters. The clustered algorithms are compared to the full set of microphones in experiments on a database recorded on different ad-hoc array geometries. These experiments evaluate the methods in terms of signal enhancement as well as performance on a large vocabulary speech recognition task. |
Formato |
application/pdf |
Identificador | |
Publicador |
IEEE |
Relação |
http://eprints.qut.edu.au/34235/1/c34235.pdf DOI:10.1109/TASL.2010.2055560 Himawan, Ivan, Mccowan, Iain, & Sridharan, Sridha (2010) Clustered blind beamforming from ad-hoc microphone arrays. IEEE Transactions on Audio, Speech, and Language Processing, 19(4), pp. 661-676. |
Direitos |
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Fonte |
Faculty of Built Environment and Engineering; Information Security Institute; School of Engineering Systems |
Palavras-Chave | #090609 Signal Processing #Array Signal Processing #Speech Enhancement #Speech Recognition |
Tipo |
Journal Article |