IPDnet: A Universal Direct-Path IPD Estimation Network for Sound Source Localization

Extracting direct-path spatial feature is crucial for sound source localization in adverse acoustic environments. This paper proposes 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 adopted for DP-IPD estimation, in which combined narrow-band and full-band layers are responsible for estimating the raw 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 a flexible number of sound sources. Third, the network is extended to handle variable microphone arrays. This version of IPDnet is trained with a large set of different microphone arrays, and then it is able to infer the source locations using new microphone arrays not seen at training time. Experiments with multiple number of moving speakers are conducted on both simulated and real-world data, which show that the full-band and narrow-band fusion network and the proposed multi-track DP-IPD learning target together achieve excellent sound source localization performance. Moreover, the proposed variable-array model generalizes well to unseen microphone arrays.