发表机构
National Engineering Research Center of Speech and Language Information Processing, University of Science and Technology of China（中国科学技术大学国家语音及语言信息处理工程研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对非专业音乐录音的噪声混响问题，提出基于双时频谱的DSME模型，采用STFT生成与CQT判别，引入色度谱损失，实验验证其增强效果优于基线。
AI 中文摘要
网上分享的非专业音乐录音常存在背景噪声和混响，降低了感知质量并限制了其复用。本文提出DSME，一种基于双时频谱表示的音乐增强模型。在生成对抗框架内，DSME使用短时傅里叶变换（STFT）谱进行生成，使用恒Q变换（CQT）谱进行判别。利用STFT的固定窗口、可逆性和可预测性，生成器从降质输入估计纯净的幅度-相位谱，并通过逆STFT重构波形。利用CQT的对数频率、与音乐八度对齐的可变窗口结构，我们设计了八度分段的CQT判别器。我们还引入了色度谱损失以强调音高与谐波一致性。实验表明，DSME在客观与主观测试中均优于基线模型，验证了双谱方法的有效性。
英文摘要
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.
CommentsAccepted by ISCSLP 2026
