CNN BASED TWO-STAGE MULTI-RESOLUTION END-TO-END MODEL FOR SINGING MELODY EXTRACTION

Abstract

Inspired by human hearing perception, we propose a two-stage multi-resolution end-to-end model for singing melody extraction in this paper. The convolutional neural network (CNN) is the core of the proposed model to generate multi-resolution representations. The 1-D and 2-D multi-resolution analysis on waveform and spectrogram-like graph are successively carried out by using 1-D and 2-D CNN kernels of different lengths and sizes. The 1-D CNNs with kernels of different lengths produce multi-resolution spectrogram-like graphs without suffering from the trade-off between spectral and temporal resolutions. The 2-D CNNs with kernels of different sizes extract features from spectro-temporal envelopes of different scales. Experiment results show the proposed model outperforms three compared systems in three out of five public databases.