With the proliferation of video platforms on the internet, recording musical performances by mobile devices has become commonplace. However, these recordings often suffer from degradation such as noise and reverberation, which negatively impact the listening experience. Consequently, the necessity for music audio enhancement (referred to as music enhancement from this point onward), involving the transformation of degraded audio recordings into pristine high-quality music, has surged to augment the auditory experience. To address this issue, we propose a music enhancement system based on the Conformer architecture that has demonstrated outstanding performance in speech enhancement tasks. Our approach explores the attention mechanisms of the Conformer and examines their performance to discover the best approach for the music enhancement task. Our experimental results show that our proposed model achieves state-of-the-art performance on single-stem music enhancement. Furthermore, our system can perform general music enhancement with multi-track mixtures, which has not been examined in previous work.

我们提出了一种基于Conformer架构的音乐增强系统，通过探索Conformer的注意机制并检验其性能，发现了用于音乐增强任务的最佳方法。实验结果表明，我们的模型在单音轨音乐增强方面取得了最先进的性能，并且我们的系统可以进行多音轨混音的音乐增强，这在以前的工作中尚未得到研究。

利用时频一致性对音乐音频进行增强