# HarmoF0: Logarithmic Scale Dilated Convolution For Pitch Estimation

arXiv: https://arxiv.org/abs/2205.01019 · HTML: https://mlsanity.com/paper/2205.01019/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 2 May 2022.

## Builds on (most cited first, 6 of 6)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [CREPE: A Convolutional Representation for Pitch Estimation](https://mlsanity.com/paper/1802.06182/) (2018)
- [SPICE: Self-supervised Pitch Estimation](https://mlsanity.com/paper/1910.11664/) (2019)
- [Audio Spectrogram Representations for Processing with Convolutional Neural Networks](https://mlsanity.com/paper/1706.09559/) (2017)
- [DEEPF0: End-To-End Fundamental Frequency Estimation for Music and Speech Signals](https://mlsanity.com/paper/2102.06306/) (2021)

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

- [Toward Fully Self-Supervised Multi-Pitch Estimation](https://mlsanity.com/paper/2402.15569/) (2024)
- [FCPE: A Fast Context-based Pitch Estimation Model](https://mlsanity.com/paper/2509.15140/) (2025)
- [MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation](https://mlsanity.com/paper/2501.03689/) (2025)
- [Audio-to-Score Transcription using Pre-trained Features, Data Augmentation, and the New SheetSage-A2S Dataset](https://mlsanity.com/paper/2608.06165/) (2026)

## Abstract (arXiv)

Sounds, especially music, contain various harmonic components scattered in the frequency dimension. It is difficult for normal convolutional neural networks to observe these overtones. This paper introduces a multiple rates dilated causal convolution (MRDC-Conv) method to capture the harmonic structure in logarithmic scale spectrograms efficiently. The harmonic is helpful for pitch estimation, which is important for many sound processing applications. We propose HarmoF0, a fully convolutional network, to evaluate the MRDC-Conv and other dilated convolutions in pitch estimation. The results show that this model outperforms the DeepF0, yields state-of-the-art performance in three datasets, and simultaneously reduces more than 90% parameters. We also find that it has stronger noise resistance and fewer octave errors. The code and pre-trained model are available at https://github.com/WX-Wei/HarmoF0.