Multi-Resolution Singing Voice Separation

Yih Liang Shen, Ya Ching Lai, Tai Shih Chi

研究成果: Conference contribution同行評審

摘要

It has been shown that time-domain neural networks achieve higher performance than networks working on the short-time Fourier transform (STFT) domain. The fully-convolutional time-domain audio separation network (Conv-TasNet) is an end-to-end separation model with outstanding performance. However, the fixed convolution kernel length in Conv-TasNet implies that it analyzes signals using the frequency resolution constrained by the kernel length. This paper proposes a multi-frequency-resolution (MF) architecture, which analyzes sig-nals using more frequency resolutions, and compares the MF model with Conv-TasNet on singing voice separation. The results show that the MF architecture improves performance of Conv-TasNet. In addition, we also demonstrate the MF architecture does not provide consistent benefits to the STFT-domain separation model.

原文English
主出版物標題2024 27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024 - Proceedings
編輯Ming-Hsiang Su, Jui-Feng Yeh, Yuan-Fu Liao, Chi-Chun Lee, Yu Taso
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9798331506032
DOIs
出版狀態Published - 2024
事件27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024 - Hsinchu, 台灣
持續時間: 17 10月 202419 10月 2024

出版系列

名字2024 27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024 - Proceedings

Conference

Conference27th Conference on the Oriental COCOSDA International Committee for the Co-Ordination and Standardisation of Speech Databases and Assessment Techniques, O-COCOSDA 2024
國家/地區台灣
城市Hsinchu
期間17/10/2419/10/24

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