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

Fei Liu; Yang Ai; Zhen-Hua Ling

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arXiv · 2609.03357

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

Fei Liu·Yang Ai·Zhen-Hua Ling

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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.

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Fei Liu, Yang Ai, Zhen-Hua Ling. 2026-09-03. Neural Music Enhancement with Dual Time-Frequency Spectral Representations for Prediction and Discrimination. https://arxiv.org/abs/2609.03357

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