# Hybrid Y-Net Architecture for Singing Voice Separation

arXiv: https://arxiv.org/abs/2303.02599 · HTML: https://mlsanity.com/paper/2303.02599/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 5 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Hybrid Transformers for Music Source Separation](https://mlsanity.com/paper/2211.08553/) (2022)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [End-to-end music source separation: is it possible in the waveform domain?](https://mlsanity.com/paper/1810.12187/) (2018)
- [Monaural Speech Enhancement using Deep Neural Networks by Maximizing a Short-Time Objective Intelligibility Measure](https://mlsanity.com/paper/1802.00604/) (2018)
- [Sampling Frequency Independent Dialogue Separation](https://mlsanity.com/paper/2206.02124/) (2022)
- [Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation](https://mlsanity.com/paper/1910.09266/) (2019)
- [Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source](https://mlsanity.com/paper/1810.11520/) (2018)
- [SpectNet : End-to-End Audio Signal Classification Using Learnable Spectrograms](https://mlsanity.com/paper/2211.09352/) (2022)

## Abstract (arXiv)

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.