# Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network

arXiv: https://arxiv.org/abs/2007.12955 · HTML: https://mlsanity.com/paper/2007.12955/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [The Ethical Implications of Generative Audio Models: A Systematic Literature Review](https://mlsanity.com/paper/2307.05527/) (2023)
- [Source-Filter HiFi-GAN: Fast and Pitch Controllable High-Fidelity Neural Vocoder](https://mlsanity.com/paper/2210.15533/) (2022)
- [PeriodNet: A non-autoregressive waveform generation model with a structure separating periodic and aperiodic components](https://mlsanity.com/paper/2102.07786/) (2021)
- [Unified Source-Filter GAN with Harmonic-plus-Noise Source Excitation Generation](https://mlsanity.com/paper/2205.06053/) (2022)
- [Unified Source-Filter GAN: Unified Source-filter Network Based On Factorization of Quasi-Periodic Parallel WaveGAN](https://mlsanity.com/paper/2104.04668/) (2021)
- [MSR-NV: Neural Vocoder Using Multiple Sampling Rates](https://mlsanity.com/paper/2109.13714/) (2021)
- [DHAuDS: A Dynamic and Heterogeneous Audio Benchmark for Test-Time Adaptation](https://mlsanity.com/paper/2511.18421/) (2025)
- [Pseudo-Cepstrum: Pitch Modification for Mel-Based Neural Vocoders](https://mlsanity.com/paper/2512.16519/) (2025)

## Abstract (arXiv)

In this paper, we propose a quasi-periodic parallel WaveGAN (QPPWG) waveform generative model, which applies a quasi-periodic (QP) structure to a parallel WaveGAN (PWG) model using pitch-dependent dilated convolution networks (PDCNNs). PWG is a small-footprint GAN-based raw waveform generative model, whose generation time is much faster than real time because of its compact model and non-autoregressive (non-AR) and non-causal mechanisms. Although PWG achieves high-fidelity speech generation, the generic and simple network architecture lacks pitch controllability for an unseen auxiliary fundamental frequency (F0) feature such as a scaled F0. To improve the pitch controllability and speech modeling capability, we apply a QP structure with PDCNNs to PWG, which introduces pitch information to the network by dynamically changing the network architecture corresponding to the auxiliary F0 feature. Both objective and subjective experimental results show that QPPWG outperforms PWG when the auxiliary F0 feature is scaled. Moreover, analyses of the intermediate outputs of QPPWG also show better tractability and interpretability of QPPWG, which respectively models spectral and excitation-like signals using the cascaded fixed and adaptive blocks of the QP structure.