# SPICE: Self-supervised Pitch Estimation

arXiv: https://arxiv.org/abs/1910.11664 · HTML: https://mlsanity.com/paper/1910.11664/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 27 of 27)

- [Multi-Task Self-Supervised Pre-Training for Music Classification](https://mlsanity.com/paper/2102.03229/) (2021)
- [Self-Supervised Learning of Audio Representations from Permutations with Differentiable Ranking](https://mlsanity.com/paper/2103.09879/) (2021)
- [Self-Adaptive Forecasting for Improved Deep Learning on Non-Stationary Time-Series](https://mlsanity.com/paper/2202.02403/) (2022)
- [The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling](https://mlsanity.com/paper/2209.14458/) (2022)
- [Equivariant Self-Supervision for Musical Tempo Estimation](https://mlsanity.com/paper/2209.01478/) (2022)
- [Interpretability for Multimodal Emotion Recognition using Concept Activation Vectors](https://mlsanity.com/paper/2202.01072/) (2022)
- [Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and Spectral Optimal Transport](https://mlsanity.com/paper/2312.14507/) (2023)
- [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)
- [Disentangling speech from surroundings with neural embeddings](https://mlsanity.com/paper/2203.15578/) (2022)
- [Toward Fully Self-Supervised Multi-Pitch Estimation](https://mlsanity.com/paper/2402.15569/) (2024)
- [Coherent, super resolved radar beamforming using self-supervised learning](https://mlsanity.com/paper/2106.13085/) (2021)
- [HarmoF0: Logarithmic Scale Dilated Convolution For Pitch Estimation](https://mlsanity.com/paper/2205.01019/) (2022)
- [PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objective](https://mlsanity.com/paper/2508.01488/) (2025)
- [Tempo vs. Pitch: understanding self-supervised tempo estimation](https://mlsanity.com/paper/2304.06868/) (2023)
- [Tempo estimation as fully self-supervised binary classification](https://mlsanity.com/paper/2401.08891/) (2024)
- [When Humans Growl and Birds Speak: High-Fidelity Voice Conversion from Human to Animal and Designed Sounds](https://mlsanity.com/paper/2505.24336/) (2025)
- [Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning](https://mlsanity.com/paper/2201.01771/) (2022)
- [MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation](https://mlsanity.com/paper/2501.03689/) (2025)
- [Real-Time Pitch/F0 Detection Using Spectrogram Images and Convolutional Neural Networks](https://mlsanity.com/paper/2504.06165/) (2025)
- [Investigating an Overfitting and Degeneration Phenomenon in Self-Supervised Multi-Pitch Estimation](https://mlsanity.com/paper/2506.23371/) (2025)
- [Translation-Equivariant Self-Supervised Learning for Pitch Estimation with Optimal Transport](https://mlsanity.com/paper/2508.01493/) (2025)
- [Twenty-Five Years of MIR Research: Achievements, Practices, Evaluations, and Future Challenges](https://mlsanity.com/paper/2511.07205/) (2025)
- [Lightweight Self-Supervised Detection of Fundamental Frequency and Accurate Probability of Voicing in Monophonic Music](https://mlsanity.com/paper/2601.11768/) (2026)
- [PitchBench: Measuring Pitch Hearing in Audio-Language Models](https://mlsanity.com/paper/2605.26176/) (2026)
- [Semi-Supervised Speech Confidence Detection using Pseudo-Labelling and Whisper Embeddings](https://mlsanity.com/paper/2606.16505/) (2026)
- [CoJEPA: Combining Contrastive Learning and JEPA for Global-Local Music Representations](https://mlsanity.com/paper/2608.30974/) (2026)

## Abstract (arXiv)

We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. We acknowledge the fact that obtaining ground truth annotations at the required temporal and frequency resolution is a particularly daunting task. Therefore, we propose to adopt a self-supervised learning technique, which is able to estimate pitch without any form of supervision. The key observation is that pitch shift maps to a simple translation when the audio signal is analysed through the lens of the constant-Q transform (CQT). We design a self-supervised task by feeding two shifted slices of the CQT to the same convolutional encoder, and require that the difference in the outputs is proportional to the corresponding difference in pitch. In addition, we introduce a small model head on top of the encoder, which is able to determine the confidence of the pitch estimate, so as to distinguish between voiced and unvoiced audio. Our results show that the proposed method is able to estimate pitch at a level of accuracy comparable to fully supervised models, both on clean and noisy audio samples, although it does not require access to large labeled datasets.