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Towards Balanced Spectral Reconstruction: Spectrally Adaptive Loss for Streaming Speech Enhancement
arXiv eess.AS0 views
arXiv:2608.30739v2 Announce Type: replace Abstract: This paper proposes two spectrally weighted STFT loss functions for lightweight streaming speech enhancement, addressing the magnitude over-attenuation in mid-to-high frequency regions caused by the magnitude-phase compensation effect. The proposed sigmoid-weighted loss applies a smooth frequency-dependent modulation to the phase-aware contribution, while the signal-dependent spectrally adaptive loss further conditions the modulation on the ground-truth log-magnitude spectrogram. To evaluate the proposed objectives, we additionally design HyST-Net, a lightweight and competitive backbone with hybrid MHA-GRU spectral-temporal modelling for low-latency streaming scenarios. Experimental results exhibit consistent improvements in high-frequency spectral reconstruction for both losses. The spectrally adaptive loss further enhances the mid-frequency region, resulting in a more balanced spectral reconstruction across the full frequency range.
Source:https://arxiv.org/abs/2608.30739PreviousHearing Like Humans? Sound Symbolism and Perceptual Alignment in Speech Language ModelsNextA Hybrid Classical-Learning Framework for Adaptive Decision Directed Speech Enhancement
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