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

• URL: http://arxiv.org/abs/2410.12536v1
• Date: Wed, 16 Oct 2024 13:18:45 GMT
• Title: SiFiSinger: A High-Fidelity End-to-End Singing Voice Synthesizer based on Source-filter Model
• Authors: Jianwei Cui, Yu Gu, Chao Weng, Jie Zhang, Liping Chen, Lirong Dai,
• Abstract summary: This paper presents an advanced end-to-end singing voice synthesis (SVS) system based on the source-filter mechanism. The proposed system also incorporates elements like the fundamental pitch (F0) predictor and waveform generation decoder. Experiments on the Opencpop dataset demonstrate efficacy of the proposed model in intonation quality and accuracy.
• Score: 31.280358048556444
• License:
• Abstract: 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.

Related papers

• Comparative Analysis of the wav2vec 2.0 Feature Extractor [42.18541127866435]
  We study the capability to replace the standard feature extraction methods in a connectionist temporal classification (CTC) ASR model. We show that both are competitive with traditional FEs on the LibriSpeech benchmark and analyze the effect of the individual components.
  arXiv  Detail & Related papers  (2023-08-08T14:29:35Z)
• Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis [19.422230767803246]
  We propose Period VITS, a novel end-to-end text-to-speech model that incorporates an explicit periodicity generator. In the proposed method, we introduce a frame pitch predictor that predicts prosodic features, such as pitch and voicing flags, from the input text. From these features, the proposed periodicity generator produces a sample-level sinusoidal source that enables the waveform decoder to accurately reproduce the pitch.
  arXiv  Detail & Related papers  (2022-10-28T07:52:30Z)
• Adaptive re-calibration of channel-wise features for Adversarial Audio Classification [0.0]
  We propose a recalibration of features using attention feature fusion for synthetic speech detection. We compare its performance against different detection methods including End2End models and Resnet-based models. We also demonstrate that the combination of Linear frequency cepstral coefficients (LFCC) and Mel Frequency cepstral coefficients (MFCC) using the attentional feature fusion technique creates better input features representations.
  arXiv  Detail & Related papers  (2022-10-21T04:21:56Z)
• Fully Automated End-to-End Fake Audio Detection [57.78459588263812]
  This paper proposes a fully automated end-toend fake audio detection method. We first use wav2vec pre-trained model to obtain a high-level representation of the speech. For the network structure, we use a modified version of the differentiable architecture search (DARTS) named light-DARTS.
  arXiv  Detail & Related papers  (2022-08-20T06:46:55Z)
• Audio Deepfake Detection Based on a Combination of F0 Information and Real Plus Imaginary Spectrogram Features [51.924340387119415]
  Experimental results on the ASVspoof 2019 LA dataset show that our proposed system is very effective for the audio deepfake detection task. Our proposed system is very effective for the audio deepfake detection task, achieving an equivalent error rate (EER) of 0.43%, which surpasses almost all systems.
  arXiv  Detail & Related papers  (2022-08-02T02:46:16Z)
• NeuralDPS: Neural Deterministic Plus Stochastic Model with Multiband Excitation for Noise-Controllable Waveform Generation [67.96138567288197]
  We propose a novel neural vocoder named NeuralDPS which can retain high speech quality and acquire high synthesis efficiency and noise controllability. It generates waveforms at least 280 times faster than the WaveNet vocoder. It is also 28% faster than WaveGAN's synthesis efficiency on a single core.
  arXiv  Detail & Related papers  (2022-03-05T08:15:29Z)
• DiffSinger: Diffusion Acoustic Model for Singing Voice Synthesis [53.19363127760314]
  DiffSinger is a parameterized Markov chain which iteratively converts the noise into mel-spectrogram conditioned on the music score. The evaluations conducted on the Chinese singing dataset demonstrate that DiffSinger outperforms state-of-the-art SVS work with a notable margin.
  arXiv  Detail & Related papers  (2021-05-06T05:21:42Z)
• End-to-End Video-To-Speech Synthesis using Generative Adversarial Networks [54.43697805589634]
  We propose a new end-to-end video-to-speech model based on Generative Adversarial Networks (GANs) Our model consists of an encoder-decoder architecture that receives raw video as input and generates speech. We show that this model is able to reconstruct speech with remarkable realism for constrained datasets such as GRID.
  arXiv  Detail & Related papers  (2021-04-27T17:12:30Z)
• Wave-Tacotron: Spectrogram-free end-to-end text-to-speech synthesis [25.234945748885348]
  We describe a sequence-to-sequence neural network which directly generates speech waveforms from text inputs. The architecture extends the Tacotron model by incorporating a normalizing flow into the autoregressive decoder loop. Experiments show that the proposed model generates speech with quality approaching a state-of-the-art neural TTS system.
  arXiv  Detail & Related papers  (2020-11-06T19:30:07Z)
• VaPar Synth -- A Variational Parametric Model for Audio Synthesis [78.3405844354125]
  We present VaPar Synth - a Variational Parametric Synthesizer which utilizes a conditional variational autoencoder (CVAE) trained on a suitable parametric representation. We demonstrate our proposed model's capabilities via the reconstruction and generation of instrumental tones with flexible control over their pitch.
  arXiv  Detail & Related papers  (2020-03-30T16:05:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.