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Neural Music Enhancement with Dual Time-Frequency Spectral Representations for Prediction and Discrimination

2026-09-03 04:28:51
Fei Liu, Yang Ai, Zhen-Hua Ling
arXiv_SD
         
Abstract

Non-professional music recordings shared online often suffer from background noise and reverberation, degrading perceived quality and limiting reuse. This paper proposes DSME, a music enhancement model based on dual time-frequency spectral representations. Within a generative adversarial framework, DSME uses short-time Fourier transform (STFT) spectra for generation and constant-Q transform (CQT) spectra for discrimination. Leveraging STFT's fixed window, invertibility, and predictability, the generator estimates clean amplitude-phase spectra from degraded inputs and reconstructs waveforms via inverse STFT. Exploiting CQT's log-frequency, variable-window structure aligned with musical octaves, we design an octave-segmented CQT discriminator. We also introduce a chroma-spectrum loss to emphasize pitch and harmonic consistency. Experiments show DSME outperforms baselines in objective and subjective tests, validating the effectiveness of the dual-spectrum approach.

Abstract (translated)

URL

https://arxiv.org/abs/2609.03357

PDF

https://arxiv.org/pdf/2609.03357.pdf

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