| Challenge: | Current AST methods struggle with accuracy and robustness when used for practical annotation. |
| Approach: | They propose a model that converts singing recordings into note sequences for automatic annotation of singing datasets. |
| Outcome: | The proposed model outperforms baseline models on enlarged, automatically annotated datasets. |
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Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, ZheTao Chen, Wenhao Xu, Fei Wu, Zhou Zhao
| Challenge: | Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline. |
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Self-Supervised Singing Voice Pre-Training towards Speech-to-Singing Conversion (2024.findings-acl)
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| Challenge: | Existing studies on speech-to-singing voice conversion (STS) are limited by the scarcity of paired speech-song data and the suboptimal quality of outputs. |
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RMSSinger: Realistic-Music-Score based Singing Voice Synthesis (2023.findings-acl)
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| Challenge: | Existing methods for singing voice synthesis are limited to fine-grained music scores . manual adjustment destroys regularity of note durations, making fine-grain music scores "crushed" |
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AlignSTS: Speech-to-Singing Conversion via Cross-Modal Alignment (2023.findings-acl)
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| Challenge: | Existing approaches to speech-to-singing voice conversion are difficult to learn in text-free situations. |
| Approach: | They propose an STS model which views speech variance as different modalities . it uses a novel rhythm adaptor to predict the target rhythm representation . they also use the predicted rhythm representation to re-align the content . |
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| Challenge: | Singing Voice Synthesis (SVS) synthesizes pleasing vocals based on music scores and lyrics . current acoustic models ignore the significance of local modeling within the sequence and the hard-to-synthesize parts in the predicted mel-spectrogram . |
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Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)
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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao
| Challenge: | Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing. |
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Learning the Beauty in Songs: Neural Singing Voice Beautifier (2022.acl-long)
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| Challenge: | Existing techniques for pitch correction are limited to intonation but ignore the overall aesthetic quality. |
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TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis (2025.findings-acl)
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Yu Zhang, Wenxiang Guo, Changhao Pan, Dongyu Yao, Zhiyuan Zhu, Ziyue Jiang, Yuhan Wang, Tao Jin, Zhou Zhao
| Challenge: | Existing zero-shot singing voice synthesis models depend on phoneme and note boundary annotations, limiting their robustness and producing poor transitions between phonemes and notes. |
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MoE-SLU: Towards ASR-Robust Spoken Language Understanding via Mixture-of-Experts (2024.findings-acl)
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| Challenge: | Spoken language understanding (SLU) is a crucial task in task-oriented dialogue systems. |
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CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency Models (2024.findings-naacl)
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| Challenge: | Neural Text-to-Speech systems are a promising approach for high-fidelity speech synthesis . but the efficiency of multi-step sampling in Diffusion Models presents challenges . |
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