用双谱图结构提升低质量音乐录音的清晰度
Neural Music Enhancement with Dual Time-Frequency Spectral Representations for Prediction and Discrimination
- 生成器用STFT谱图重建波形，判别器用CQT谱图识别音高和谐性
- 在客观与主观测试中均优于基线模型，尤其对混响噪声抑制效果显著
- 适合音频修复、音乐制作及智能听觉系统开发者参考
非专业音乐录制常因背景噪音和混响导致音质下降，限制其再利用。本文提出基于双时频谱表示的音乐增强模型DSME。在生成对抗框架下，生成器采用短时傅里叶变换（STFT）谱图进行波形重建，利用其固定窗口、可逆性和可预测性，从退化输入估计干净的幅值-相位谱并逆变换回波形；判别器则使用常量-Q变换（CQT）谱图，借助其对数频率、变窗结构与音乐八度对齐的特点，设计分八度的CQT判别器。同时引入音高谱损失以强化音高与谐波一致性。实验表明，DSME在客观指标与主观评测中均优于现有基线，验证了双谱图方法的有效性。
原文摘要 · Abstract (English)
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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