# DAFMSVC: One-Shot Singing Voice Conversion with Dual Attention Mechanism and Flow Matching

arXiv: https://arxiv.org/abs/2508.05978 · HTML: https://mlsanity.com/paper/2508.05978/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 31 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 28 of 28)

- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://mlsanity.com/paper/2301.00808/) (2023)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [Voicebox: Text-Guided Multilingual Universal Speech Generation at Scale](https://mlsanity.com/paper/2306.15687/) (2023)
- [CAM++: A Fast and Efficient Network for Speaker Verification Using Context-Aware Masking](https://mlsanity.com/paper/2303.00332/) (2023)
- [ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakers](https://mlsanity.com/paper/2204.09224/) (2022)
- [Multi-band MelGAN: Faster Waveform Generation for High-Quality Text-to-Speech](https://mlsanity.com/paper/2005.05106/) (2020)
- [Multi-Singer: Fast Multi-Singer Singing Voice Vocoder With A Large-Scale Corpus](https://mlsanity.com/paper/2112.10358/) (2021)
- [On the Utility of Self-supervised Models for Prosody-related Tasks](https://mlsanity.com/paper/2210.07185/) (2022)
- [DiffSVC: A Diffusion Probabilistic Model for Singing Voice Conversion](https://mlsanity.com/paper/2105.13871/) (2021)
- [Why does Self-Supervised Learning for Speech Recognition Benefit Speaker Recognition?](https://mlsanity.com/paper/2204.12765/) (2022)
- [The Singing Voice Conversion Challenge 2023](https://mlsanity.com/paper/2306.14422/) (2023)
- [FastSVC: Fast Cross-Domain Singing Voice Conversion with Feature-wise Linear Modulation](https://mlsanity.com/paper/2011.05731/) (2020)
- [Singing Voice Conversion with Disentangled Representations of Singer and Vocal Technique Using Variational Autoencoders](https://mlsanity.com/paper/1912.02613/) (2019)
- [DurIAN-SC: Duration Informed Attention Network based Singing Voice Conversion System](https://mlsanity.com/paper/2008.03009/) (2020)
- [StableVC: Style Controllable Zero-Shot Voice Conversion with Conditional Flow Matching](https://mlsanity.com/paper/2412.04724/) (2024)
- [PPG-based singing voice conversion with adversarial representation learning](https://mlsanity.com/paper/2010.14804/) (2020)
- [HYBRIDFORMER: improving SqueezeFormer with hybrid attention and NSR mechanism](https://mlsanity.com/paper/2303.08636/) (2023)
- [Content-Dependent Fine-Grained Speaker Embedding for Zero-Shot Speaker Adaptation in Text-to-Speech Synthesis](https://mlsanity.com/paper/2204.00990/) (2022)
- [Hierarchical disentangled representation learning for singing voice conversion](https://mlsanity.com/paper/2101.06842/) (2021)
- [RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction](https://mlsanity.com/paper/2403.05010/) (2024)
- [Neural Concatenative Singing Voice Conversion: Rethinking Concatenation-Based Approach for One-Shot Singing Voice Conversion](https://mlsanity.com/paper/2312.04919/) (2023)
- [A Hierarchical Speaker Representation Framework for One-shot Singing Voice Conversion](https://mlsanity.com/paper/2206.13762/) (2022)
- [SPA-SVC: Self-supervised Pitch Augmentation for Singing Voice Conversion](https://mlsanity.com/paper/2406.05692/) (2024)

## Built on by (most cited first, 2 of 2)

- [TVTSyn: Content-Synchronous Time-Varying Timbre for Streaming Voice Conversion and Anonymization](https://mlsanity.com/paper/2602.09389/) (2026)
- [Vibrato Expression Control for Singing Voice Conversion with Improving Independent Control](https://mlsanity.com/paper/2606.17126/) (2026)

## Abstract (arXiv)

Singing Voice Conversion (SVC) transfers a source singer's timbre to a target while keeping melody and lyrics. The key challenge in any-to-any SVC is adapting unseen speaker timbres to source audio without quality degradation. Existing methods either face timbre leakage or fail to achieve satisfactory timbre similarity and quality in the generated audio. To address these challenges, we propose DAFMSVC, where the self-supervised learning (SSL) features from the source audio are replaced with the most similar SSL features from the target audio to prevent timbre leakage. It also incorporates a dual cross-attention mechanism for the adaptive fusion of speaker embeddings, melody, and linguistic content. Additionally, we introduce a flow matching module for high quality audio generation from the fused features. Experimental results show that DAFMSVC significantly enhances timbre similarity and naturalness, outperforming state-of-the-art methods in both subjective and objective evaluations.