Abstract:
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Time overlapping of acoustic signals, which so often occurs in real
life, is a challenge for current state-of-the-art sound recognition
systems. In this work, we propose an approach for detecting,
identifying and positioning a set of simultaneous acoustic events in
a room environment, using multiple arbitrarily-located microphone
arrays, and working in real time. Assuming a set of estimated
acoustic source positions, the use of a frequency invariant nullsteering
beamformer for each position and each array yields a set
of signals which show different balances among the various
acoustic sources. For each signal, a model-based likelihood
computation is carried out to obtain a matrix of likelihood scores.
Then a MAP criterion is used to jointly detect the event classes and
assign each of them to a given source position. Experimental
results with two sources, one of which is speech, and two threemicrophone
linear arrays are reported, and a comparison with
alternatives approaches is carried out. |