IsoNet: Face-Guided Speech Extraction for Compact Microphone Arrays
Dinanath Padhya, Sajen Maharjan, Binita Adhikari, Ishwor Raj Pokharel
Listening to one chosen talker in a crowded room is effortless for people but hard for small devices, whose microphone arrays are too compact for classical beamforming to separate nearby voices. We present IsoNet, a select-to-listen system: the user selects a face in the camera view, and a 4-microphone array with a 9.4 cm aperture returns that person's speech. IsoNet fuses multi-channel complex spectra, GCC-PHAT spatial cues, and a face embedding at the bottleneck of a U-Net mask estimator, trained with auxiliary direction-of-arrival supervision and an SNR curriculum on simulated reverberant VoxCeleb mixtures. On a hard test set spanning -1 to 10 dB SNR, IsoNet reaches 9.31 dB SI-SDR, a 4.85 dB improvement over the mixture, and raises PESQ from 1.40 to 2.13 and STOI from 0.72 to 0.84. The gains are largest where the task is hardest (5.71 dB SI-SDR improvement at -1 to 1 dB SNR), and IsoNet outperforms oracle-steered delay-and-sum and MVDR beamformers by 9.7 and 10.9 dB SI-SDR. Ablations show that visual and spatial cues are complementary, and a 4.9M-parameter audio-spatial variant stays within 0.16 dB of the full model while running 26 times faster than real time.
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