HC-AINet: An Auditory-Inspired Network with Harmonic Compensation for Low-Complexity Speech Enhancement

Overview & Synopsis

Speech enhancement on resource-constrained devices requires noise suppression with limited computation and minimal loss of spectral detail. Auditory-inspired subband processing reduces computation, but its smooth gains have limited ability to distinguish harmonic peaks from neighboring noise. We propose a harmonic-compensated auditory-inspired network (HC-AINet) for low-complexity speech enhancement. A triangular equivalent rectangular bandwidth (ERB) filterbank supplies compact features to a lightweight network comprising inverted-residual units, temporal Conformer layers, and parallel frequency-gated convolutions. The network predicts global and harmonic gains together with fusion weights. A harmonic-location map derived from the noisy spectrum confines the harmonic contribution to selected bins on the original frequency grid, while the global branch controls the broader spectral envelope. The compensated magnitude, noisy phase, and a magnitude-derived minimum-phase cue jointly condition a compact phase estimator for waveform reconstruction. On VoiceBank-DEMAND, the 64-band model achieves a PESQ of 3.20 and a STOI of 0.95 with approximately 0.27 million parameters.