Our Paper got accepted to ISMIR 2020!

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Our Paper “Investigating U-Nets with various Intermediate Blocks for Spectrogram-based Singing Voice Separation” got accepted to ISMIR(International Society for Music Information Retrieval Conference)!

Abstract

Singing Voice Separation (SVS) tries to separate singing voice from a given mixed musical signal. Recently, many U-Net-based models have been proposed for the SVS task, but there were no existing works that evaluate and compare various types of intermediate blocks that can be used in the U-Net architecture. In this paper, we introduce a variety of intermediate spectrogram transformation blocks. We implement U-nets based on these blocks and train them on complex-valued spectrograms to consider both magnitude and phase. These networks are then compared on the SDR metric. When using a particular block composed of convolutional and fully-connected layers, it achieves state-of-the-art SDR on the MUSDB singing voice separation task by a large margin of 0.9 dB. Our code and models are available online.

Source code & Demenstration

• Github
• Interactive Demonstration (colab)
• ISMIR2020 poster link includes poster, paper, and short presentation video

Preprint

Investigating U-Nets with various Intermediate Blocks for Spectrogram-based Singing Voice Separation

About ISMIR

• ISMIR 2020
  • currently the 2nd ranked in the Music & Musicology subcategory of Humanities, Literature, and Arts
  • currently the 8th ranked publication in the Multimedia subcategory of Engineering and Computer Science.