Hybrid Y-Net Architecture for Singing Voice Separation

• URL: http://arxiv.org/abs/2303.02599v1
• Date: Sun, 5 Mar 2023 07:54:49 GMT
• Title: Hybrid Y-Net Architecture for Singing Voice Separation
• Authors: Rashen Fernando, Pamudu Ranasinghe, Udula Ranasinghe, Janaka Wijayakulasooriya, Pantaleon Perera
• Abstract summary: The proposed architecture performs end-to-end hybrid source separation by extracting features from both spectrogram and waveform domains. Inspired by the U-Net architecture, Y-Net predicts a spectrogram mask to separate vocal sources from a mixture signal.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research paper presents a novel deep learning-based neural network architecture, named Y-Net, for achieving music source separation. The proposed architecture performs end-to-end hybrid source separation by extracting features from both spectrogram and waveform domains. Inspired by the U-Net architecture, Y-Net predicts a spectrogram mask to separate vocal sources from a mixture signal. Our results demonstrate the effectiveness of the proposed architecture for music source separation with fewer parameters. Overall, our work presents a promising approach for improving the accuracy and efficiency of music source separation.

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