Hierarchical Graph-based Neural Network for Singing Melody Extraction

Singing melody extraction from polyphonic music is a critical and challenging task in music information retrieval (MIR). However, due to the interfere of the accompaniment and the background noise, it is key and challenging to obtain a global semantic representation that discriminates the singing melody line. To address this issue, we consider the two aspects that regards to obtaining the global semantic representation: the global relationships in the spectrum and the relationships between channels. In this paper, we propose a novel hierarchical graph-based network for singing melody extraction. In particular, according to its characteristics of the spectrum, we first model the spectrum into graph structure, a two-layer graph convolution network is used to obtain the global semantic representation in the spectrum. Then to capture the relationships between channels, channel-wise graph convolution module is devised to capture and reasoning the relationship between channels. The conducted experiments demonstrate the effectiveness of the proposed network.