Problem
Research questions and friction points this paper is trying to address.
background noise
reverberation
music enhancement
non-professional recordings
quality degradation
Neural Music Enhancement with Dual Time-Frequency Spectral Representations for Prediction and Discrimination
🤖 AI Summary
本文提出了一种基于双时间-频率谱表示的音乐增强模型DSME，使用STFT生成干净音频并用CQT进行判别，以解决非专业录音中的噪音和混响问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Dual Time-Frequency Spectral Representations
Generative Adversarial Framework
Chroma-Spectrum Loss
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