M4Singer: A Multi-Style, Multi-Singer and Musical Score Provided Mandarin Singing Corpus

By Lichao Zhang, Ruiqi Li, Shoutong Wang, Liqun Deng, Jinglin Liu, Yi Ren, Jinzheng He, Rongjie Huang, Jieming Zhu, Xiao Chen and 1 more. 2022

123 citations recorded by the source corpus.

The lack of publicly available high-quality and accurately labeled datasets has long been a major bottleneck for singing voice synthesis (SVS). To tackle this problem, we present M4Singer , a free-to-use M ulti-style, M ulti-singer M andarin singing collection with elaborately annotated M usical scores as well as its benchmarks. Specifically, 1) we construct and release a large high-quality Chinese singing voice corpus, which is recorded by 20 professional singers, covering 700 Chinese pop songs as well as all the four SATB types (i.e., soprano, alto, tenor, and bass); 2) we take extensive efforts to manually compose the musical scores for each recorded song, which is necessary to the study of the prosody modeling for SVS. 3) To facilitate the use and demonstrate the quality of M4Singer, we conduct four different benchmark experiments: score-based SVS, controllable singing voice (CSV), singing voice conversion (SVC) and automatic music transcription (AMT). Audio samples can be found at http://m4singer.github.io.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Rongjie Huang
2. Zhou Zhao
3. Jinglin Liu
4. Yi Ren
5. Chenye Cui
6. Dongchao Yang
7. Songxiang Liu
8. Xuejiao Tan

In Signal Processing

1. Rongjie Huang
2. Zhou Zhao
3. Jinglin Liu
4. Yi Ren
5. Chenye Cui
6. Dongchao Yang
7. Xuejiao Tan
8. Songxiang Liu

Citations in the corpus

• UniAudio: An Audio Foundation Model Toward Universal Audio Generation (2023, methodology): “In this work, these textual instructions are represented as Task Conditions Audio Target Text-to-Speech (TTS) (Wang et al., 2023a) phoneme, speaker prompt speech Voice Conversion (VC) ♣ (Wang et al., 2023e) semantic token, speaker prompt speech Speech Enhancement (SE) ♣ (Wang et al., 2023b) noisy sp”
• Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024, neutral): “Following (Liu et al., 2022; Zhang et al., 2022a), the SVS system typically includes the MIDI-to-F0 converter to predict F0 explicitly.”
• GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech Synthesis (2022, background): “A growing number of applications [51, 11, 50, 52], such as voice assistant services and long-form reading, have been actively developed and deployed to real-world speech platforms.”