IPDnet: A Universal Direct-Path IPD Estimation Network for Sound Source Localization
CoRR(2024)
摘要
Extracting direct-path spatial feature is crucial for sound source
localization in adverse acoustic environments. This paper proposes the IPDnet,
a neural network that estimates direct-path inter-channel phase difference
(DP-IPD) of sound sources from microphone array signals. The estimated DP-IPD
can be easily translated to source location based on the known microphone array
geometry. First, a full-band and narrow-band fusion network is proposed for
DP-IPD estimation, in which alternating narrow-band and full-band layers are
responsible for estimating the rough DP-IPD information in one frequency band
and capturing the frequency correlations of DP-IPD, respectively. Second, a new
multi-track DP-IPD learning target is proposed for the localization of flexible
number of sound sources. Third, the IPDnet is extend to handling variable
microphone arrays, once trained which is able to process arbitrary microphone
arrays with different number of channels and array topology. Experiments of
multiple-moving-speaker localization are conducted on both simulated and
real-world data, which show that the proposed full-band and narrow-band fusion
network and the proposed multi-track DP-IPD learning target together achieves
excellent sound source localization performance. Moreover, the proposed
variable-array model generalizes well to unseen microphone arrays.
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