Using Cyclic Noise as the Source Signal for Neural Source-Filter-based Speech Waveform Model

Wang, Xin; Yamagishi, Junichi

doi:10.48550/arxiv.2004.02191

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…We compared the following variants in the experiments 4 : N-BL: The harmonic-plus-noise NSF model [31] was included as a reference model without the reverberation module. The number of model parameters was around 1.2 × 10 6 .…”

Section: Experimental Modelsmentioning

confidence: 99%

Reverberation Modeling for Source-Filter-Based Neural Vocoder

Ai¹,

Wang²,

Yamagishi³

et al. 2020

Interspeech 2020

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This paper presents a reverberation module for source-filterbased neural vocoders that improves the performance of reverberant effect modeling. This module uses the output waveform of neural vocoders as an input and produces a reverberant waveform by convolving the input with a room impulse response (RIR). We propose two approaches to parameterizing and estimating the RIR. The first approach assumes a global time-invariant (GTI) RIR and directly learns the values of the RIR on a training dataset. The second approach assumes an utterance-level time-variant (UTV) RIR, which is invariant within one utterance but varies across utterances, and uses another neural network to predict the RIR values. We add the proposed reverberation module to the phase spectrum predictor (PSP) of a HiNet vocoder and jointly train the model. Experimental results demonstrate that the proposed module was helpful for modeling the reverberation effect and improving the perceived quality of generated reverberant speech. The UTV-RIR was shown to be more robust than the GTI-RIR to unknown reverberation conditions and achieved a perceptually better reverberation effect.

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Section: Experimental Modelsmentioning

confidence: 99%

Reverberation Modeling for Source-Filter-Based Neural Vocoder

Ai¹,

Wang²,

Yamagishi³

et al. 2020

Interspeech 2020

Self Cite

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DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism

Liu¹,

Li²,

Ren

et al. 2022

AAAI

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Singing voice synthesis (SVS) systems are built to synthesize high-quality and expressive singing voice, in which the acoustic model generates the acoustic features (e.g., mel-spectrogram) given a music score. Previous singing acoustic models adopt a simple loss (e.g., L1 and L2) or generative adversarial network (GAN) to reconstruct the acoustic features, while they suffer from over-smoothing and unstable training issues respectively, which hinder the naturalness of synthesized singing. In this work, we propose DiffSinger, an acoustic model for SVS based on the diffusion probabilistic model. DiffSinger is a parameterized Markov chain that iteratively converts the noise into mel-spectrogram conditioned on the music score. By implicitly optimizing variational bound, DiffSinger can be stably trained and generate realistic outputs. To further improve the voice quality and speed up inference, we introduce a shallow diffusion mechanism to make better use of the prior knowledge learned by the simple loss. Specifically, DiffSinger starts generation at a shallow step smaller than the total number of diffusion steps, according to the intersection of the diffusion trajectories of the ground-truth mel-spectrogram and the one predicted by a simple mel-spectrogram decoder. Besides, we propose boundary prediction methods to locate the intersection and determine the shallow step adaptively. The evaluations conducted on a Chinese singing dataset demonstrate that DiffSinger outperforms state-of-the-art SVS work. Extensional experiments also prove the generalization of our methods on text-to-speech task (DiffSpeech). Audio samples: https://diffsinger.github.io. Codes: https://github.com/MoonInTheRiver/DiffSinger.

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Reverberation Modeling for Source-Filter-based Neural Vocoder

Wang

Yamagishi

et al. 2020

Preprint

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Using Cyclic Noise as the Source Signal for Neural Source-Filter-based Speech Waveform Model

Cited by 3 publications

References 26 publications

Reverberation Modeling for Source-Filter-Based Neural Vocoder

Reverberation Modeling for Source-Filter-Based Neural Vocoder

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism

Reverberation Modeling for Source-Filter-based Neural Vocoder

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