# CREPE: A Convolutional Representation for Pitch Estimation

arXiv: https://arxiv.org/abs/1802.06182 · HTML: https://mlsanity.com/paper/1802.06182/

## Key facts

- Cited by 110 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2018.

## Built on by (most cited first, 30 of 110)

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- [textless-lib: a Library for Textless Spoken Language Processing](https://mlsanity.com/paper/2202.07359/) (2022)
- [A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation](https://mlsanity.com/paper/2203.09893/) (2022)
- [Coding Speech through Vocal Tract Kinematics](https://mlsanity.com/paper/2406.12998/) (2024)
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- [DDX7: Differentiable FM Synthesis of Musical Instrument Sounds](https://mlsanity.com/paper/2208.06169/) (2022)
- [YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation](https://mlsanity.com/paper/2407.04822/) (2024)
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- [BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping](https://mlsanity.com/paper/2206.12038/) (2022)
- [Towards Interactive Intelligence for Digital Humans](https://mlsanity.com/paper/2512.13674/) (2025)
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- [PhaseAug: A Differentiable Augmentation for Speech Synthesis to Simulate One-to-Many Mapping](https://mlsanity.com/paper/2211.04610/) (2022)
- [ATFNet: Adaptive Time-Frequency Ensembled Network for Long-term Time Series Forecasting](https://mlsanity.com/paper/2404.05192/) (2024)
- [DEEPF0: End-To-End Fundamental Frequency Estimation for Music and Speech Signals](https://mlsanity.com/paper/2102.06306/) (2021)
- [DiffMoog: a Differentiable Modular Synthesizer for Sound Matching](https://mlsanity.com/paper/2401.12570/) (2024)
- [Learning Interpretable Features in Audio Latent Spaces via Sparse Autoencoders](https://mlsanity.com/paper/2510.23802/) (2025)
- [Towards Robust Unsupervised Disentanglement of Sequential Data -- A Case Study Using Music Audio](https://mlsanity.com/paper/2205.05871/) (2022)
- [The Efficacy of Self-Supervised Speech Models for Audio Representations](https://mlsanity.com/paper/2209.12900/) (2022)
- [A multimodal dynamical variational autoencoder for audiovisual speech representation learning](https://mlsanity.com/paper/2305.03582/) (2023)
- [LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any Singing Voice Conversion with Singer Guidance](https://mlsanity.com/paper/2406.05325/) (2024)
- [Audio synthesizer inversion in symmetric parameter spaces with approximately equivariant flow matching](https://mlsanity.com/paper/2506.07199/) (2025)
- [RefineGAN: Universally Generating Waveform Better than Ground Truth with Highly Accurate Pitch and Intensity Responses](https://mlsanity.com/paper/2111.00962/) (2021)
- [Audio Barlow Twins: Self-Supervised Audio Representation Learning](https://mlsanity.com/paper/2209.14345/) (2022)
- [FretNet: Continuous-Valued Pitch Contour Streaming for Polyphonic Guitar Tablature Transcription](https://mlsanity.com/paper/2212.03023/) (2022)

## Abstract (arXiv)

The task of estimating the fundamental frequency of a monophonic sound recording, also known as pitch tracking, is fundamental to audio processing with multiple applications in speech processing and music information retrieval. To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics. While such techniques perform very well on average, there remain many cases in which they fail to correctly estimate the pitch. In this paper, we propose a data-driven pitch tracking algorithm, CREPE, which is based on a deep convolutional neural network that operates directly on the time-domain waveform. We show that the proposed model produces state-of-the-art results, performing equally or better than pYIN. Furthermore, we evaluate the model's generalizability in terms of noise robustness. A pre-trained version of CREPE is made freely available as an open-source Python module for easy application.