# SiFiSinger: A High-Fidelity End-to-End Singing Voice Synthesizer based on Source-filter Model

arXiv: https://arxiv.org/abs/2410.12536 · HTML: https://mlsanity.com/paper/2410.12536/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 16 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech](https://mlsanity.com/paper/2106.06103/) (2021)
- [DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism](https://mlsanity.com/paper/2105.02446/) (2021)
- [CREPE: A Convolutional Representation for Pitch Estimation](https://mlsanity.com/paper/1802.06182/) (2018)
- [Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis](https://mlsanity.com/paper/2201.07429/) (2022)
- [Neural source-filter waveform models for statistical parametric speech synthesis](https://mlsanity.com/paper/1904.12088/) (2019)
- [VISinger: Variational Inference with Adversarial Learning for End-to-End Singing Voice Synthesis](https://mlsanity.com/paper/2110.08813/) (2021)
- [DeepSinger: Singing Voice Synthesis with Data Mined From the Web](https://mlsanity.com/paper/2007.04590/) (2020)
- [ByteSing: A Chinese Singing Voice Synthesis System Using Duration Allocated Encoder-Decoder Acoustic Models and WaveRNN Vocoders](https://mlsanity.com/paper/2004.11012/) (2020)
- [Sequence-to-sequence Singing Synthesis Using the Feed-forward Transformer](https://mlsanity.com/paper/1910.09989/) (2019)
- [Sinsy: A Deep Neural Network-Based Singing Voice Synthesis System](https://mlsanity.com/paper/2108.02776/) (2021)
- [VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis Enhanced by Digital Signal Processing Synthesizer](https://mlsanity.com/paper/2211.02903/) (2022)
- [Source-Filter HiFi-GAN: Fast and Pitch Controllable High-Fidelity Neural Vocoder](https://mlsanity.com/paper/2210.15533/) (2022)
- [SUSing: SU-net for Singing Voice Synthesis](https://mlsanity.com/paper/2205.11841/) (2022)

## Built on by (most cited first, 3 of 3)

- [TCSinger 2: Customizable Multilingual Zero-shot Singing Voice Synthesis](https://mlsanity.com/paper/2505.14910/) (2025)
- [Controllable Singing Voice Synthesis using Phoneme-Level Energy Sequence](https://mlsanity.com/paper/2509.07038/) (2025)
- [Generative Multi-modal Feedback for Singing Voice Synthesis Evaluation](https://mlsanity.com/paper/2512.02523/) (2025)

## Abstract (arXiv)

This paper presents an advanced end-to-end singing voice synthesis (SVS) system based on the source-filter mechanism that directly translates lyrical and melodic cues into expressive and high-fidelity human-like singing. Similarly to VISinger 2, the proposed system also utilizes training paradigms evolved from VITS and incorporates elements like the fundamental pitch (F0) predictor and waveform generation decoder. To address the issue that the coupling of mel-spectrogram features with F0 information may introduce errors during F0 prediction, we consider two strategies. Firstly, we leverage mel-cepstrum (mcep) features to decouple the intertwined mel-spectrogram and F0 characteristics. Secondly, inspired by the neural source-filter models, we introduce source excitation signals as the representation of F0 in the SVS system, aiming to capture pitch nuances more accurately. Meanwhile, differentiable mcep and F0 losses are employed as the waveform decoder supervision to fortify the prediction accuracy of speech envelope and pitch in the generated speech. Experiments on the Opencpop dataset demonstrate efficacy of the proposed model in synthesis quality and intonation accuracy.