基於人聲旋律的流行音樂即時伴奏生成系統

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基於人聲旋律的流行音樂即時伴奏生成系統

Real-time pop music accompaniment generation accordingto vocal melody by deep learning models

林子軒 , Masters　　Advisor：蘇豐文　　

英文

深度學習；即時；伴奏生成；流行音樂； DeepLearning ； Real-Time ； Accompaniment ； Pop-music

Abstract │ Reference (25) │ Altmetrics

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Abstract
The goal of this work is to propose a real-time accompaniment system to assist singers in complete a simple demo by themselves.
By current audio signal technology, we have seen some achievements on accompani-ment generation and some on vocal transcription. Basing on these great works, we propose a novel “Real-time accompany generation system” to combine current state-of-arts and further improve the efficiency to reach real-time human interactive mode. To reach a ac-ceptable computing efficiency, we do a lot pruning on original model and apply DenseNet concept to enhance its gradient propagate.
In this system, we integrate efficiency improved vocal transcription model and sim-plified HMM-base accompaniment generation model which can better fit small training set situation to output musical accompaniment in limited time. We believe that this work will benefit many solo singers to deliver their live shows or demos by themselves whenever they need.
As a result, we reach real-time under 180 BPM which covers most of pop music and propose a highly improved vocal transcription model with 1/1000 parameters and 1/50 FLOPs.

Reference ( 25 ) 〈TOP〉

[1] Nan Jiang, Sheng Jin, Zhiyao Duan, and Changshui Zhang. Rl-duet: Online music accompaniment generation using deep reinforcement learning, 2020.
[2] Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, and Tie-Yan Liu. Popmag: Pop music accompaniment generation, 2020.
[3] Yi Ren, Jinzheng He, Xu Tan, Tao Qin, Zhou Zhao, and Tie-Yan Liu. Popmag: Pop music accompaniment generation. pages 1198–1206, 10 2020. doi: 10.1145/ 3394171.3413721.
[4] Andrew McLeod, Rodrigo Schramm, Mark Steedman, and Emmanouil Benetos. Au-tomatic transcription of polyphonic vocal music. Applied Sciences, 7(12), 2017. ISSN 2076-3417. doi: 10.3390/app7121285. URL https://www.mdpi.com/ 2076-3417/7/12/1285.
[5] Li Su Zih-Sing Fu. Hierarchical classification networks for singing voice segmenta-tion and transcription. International Society for Music Information Retrieval Confer-ence (ISMIR), 2019.

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