MusicECAN: An Automatic Denoising Network for Music Recordings with Efficient Channel Attention

In this work, we address the long-standing problem of automatic recorded music denoising. In previous audio denoising research, the primary focus has been on speech, and music denoising works only considered noise types in indoor conversation scenarios or old gramophone recordings, neglecting the amateur music recording scenario. To this end, we first propose MusicECAN, an automatic music denoising method designed to filter out additional noise components in recorded music. The novel architecture comprises two key components, namely, a feature learning module and a noise filtering module, which can efficiently but effectively model, refine and denoise the noisy input. Specifically, in order to capture sufficient noisy music information, an ECA-U-SAM based feature learning module is designed by incorporating an efficient channel attention (ECA) mechanism into the traditional U-Net model with a supervised attention module (SAM). To train our MusicECAN, we collect M&N, a dataset containing various clean music and noise recordings. Through the combination of different clean-noise recording pairs, we can effectively simulate possible music performance environments with various background noise. Extensive quantitative and qualitative comparisons demonstrate that our MusicECAN outperforms the state-of-the-art audio denoising methods.