# Neural Pitch-Shifting and Time-Stretching with Controllable LPCNet

arXiv: https://arxiv.org/abs/2110.02360 · HTML: https://mlsanity.com/paper/2110.02360/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Learning and controlling the source-filter representation of speech with a variational autoencoder](https://mlsanity.com/paper/2204.07075/) (2022)
- [PRESENT: Zero-Shot Text-to-Prosody Control](https://mlsanity.com/paper/2408.06827/) (2024)
- [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)

Modifying the pitch and timing of an audio signal are fundamental audio editing operations with applications in speech manipulation, audio-visual synchronization, and singing voice editing and synthesis. Thus far, methods for pitch-shifting and time-stretching that use digital signal processing (DSP) have been favored over deep learning approaches due to their speed and relatively higher quality. However, even existing DSP-based methods for pitch-shifting and time-stretching induce artifacts that degrade audio quality. In this paper, we propose Controllable LPCNet (CLPCNet), an improved LPCNet vocoder capable of pitch-shifting and time-stretching of speech. For objective evaluation, we show that CLPCNet performs pitch-shifting of speech on unseen datasets with high accuracy relative to prior neural methods. For subjective evaluation, we demonstrate that the quality and naturalness of pitch-shifting and time-stretching with CLPCNet on unseen datasets meets or exceeds competitive neural- or DSP-based approaches.