A multi-scale fully convolutional network for singing melody extraction

The melody extraction can be considered as a se-quence-to-sequence task or a classification task. Many recent models based on semantic segmentation have been proven very effective in melody extraction. In this paper, we built up a fully convolutional network (FCN) for melody extraction from polyphonic music. Inspired by the state-of-the-art architecture of the semantic segmentation, we constructed the encoder in a dense way and designed the decoder accordingly for audio processing. The combined frequency and periodicity (CFP) representation, which contains spectral and cepstral information, was adopted as the input feature of the proposed model. We conducted performance comparison between the proposed model and several methods on various datasets. Experimental results show the proposed model achieves state-of-the-art performance with less computation and fewer parameters.