Machine listening in spatial acoustic scenes with deep networks in different microphone geometries
Multi-channel acoustic source localization evaluates direction-dependent
inter-microphone differences in order to estimate the position of an acoustic
source embedded in an interfering sound field. We here investigate a deep neural
network (DNN) approach to source localization that improves on previous work
with learned, linear support-vector-machine localizers. DNNs with depths
between 4 and 15 layers were trained to predict azimuth direction of target
speech in 72 directional bins of width 5 degree, embedded in an isotropic,
multi-speech-source noise field. Several system parameters were varied, in
particular number of microphones in the bilateral hearing aid scenario was
set to 2, 4, and 6, respectively.
Results show that DNNs provide a clear improvement in
localization performance over a linear classifier reference system.
Increasing the number of microphones from 2 to 4 results in a larger increase of
performance for the DNNs than for the linear system. However, 6 microphones
provide only a small additional gain. The DNN architectures perform better
with 4 microphones than the linear approach does with 6 microphones, thus
indicating that location-specific information in source-interference scenarios
is encoded non-linearly in the sound field.
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