Many-to-Many Singing Performance Style Transfer on Pitch and Energy Contours

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Signal Processing Letters Pub Date : 2024-11-25 DOI:10.1109/LSP.2024.3506858
Yu-Teng Hsu;Jun-You Wang;Jyh-Shing Roger Jang
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Abstract

Singing voice conversion (SVC) aims to convert the singer identity of a singing voice to that of another singer. However, most existing SVC systems only perform the conversion of timbre information, while leaving other information unchanged. This approach does not consider other aspects of singer identity, particularly a singer's performance style, which is reflected in the pitch (F0) and the energy (volume dynamics) contours of singing. To address this issue, this paper proposes a many-to-many singing performance style transfer system that converts the pitch and energy contours of one singer's style to another singer's. To achieve this target, we utilize two AutoVC-like autoencoders with an information bottleneck to automatically disentangle performance style from other musical contents, one for the pitch contour while another for the energy contour. Experiment results suggested that the proposed model can perform singing performance style transfer in a many-to-many conversion scenario, resulting in improved singer identity similarity to the target singer.
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多对多歌唱表演风格在音高和能量轮廓上的传递
歌唱声音转换(SVC)的目的是将一个歌唱声音的歌手身份转换为另一个歌手的歌手身份。然而,现有的SVC系统大多只对音色信息进行转换,其他信息保持不变。这种方法没有考虑歌手身份的其他方面,特别是歌手的表演风格,这反映在演唱的音高(F0)和能量(音量动态)轮廓上。为了解决这个问题,本文提出了一个多对多的演唱风格转换系统,将一个歌手的音高和能量轮廓转换为另一个歌手的风格。为了实现这一目标,我们使用了两个带有信息瓶颈的类似autovc的自动编码器来自动从其他音乐内容中分离出演奏风格,一个用于音高轮廓,另一个用于能量轮廓。实验结果表明,该模型可以在多对多转换场景下进行演唱风格迁移,从而提高歌手与目标歌手的身份相似度。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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