SwiftF0: Fast and Accurate Monophonic Pitch Detection

TL;DR

SwiftF0 is a lightweight neural network that achieves state-of-the-art monophonic pitch estimation in noisy conditions with high speed and accuracy, suitable for real-time applications on resource-limited devices.

Contribution

The paper introduces SwiftF0, a novel efficient neural model for pitch detection, and a synthetic speech dataset for better training and evaluation.

Findings

Achieves 91.80% harmonic mean at 10 dB SNR, outperforming baselines.

Runs 42x faster than CREPE on CPU with only 95,842 parameters.

Provides a new synthetic dataset and comprehensive evaluation metrics.

Abstract

Accurate and real-time monophonic pitch estimation in noisy conditions, particularly on resource-constrained devices, remains an open challenge in audio processing. We present \emph{SwiftF0}, a novel, lightweight neural model that sets a new state-of-the-art for monophonic pitch estimation. Through training on diverse speech, music, and synthetic datasets with extensive data augmentation, SwiftF0 achieves robust generalization across acoustic domains while maintaining computational efficiency. SwiftF0 achieves a 91.80\% harmonic mean (HM) at 10 dB SNR, outperforming baselines like CREPE by over 12 percentage points and degrading by only 2.3 points from clean audio. SwiftF0 requires only 95,842 parameters and runs approximately 42x faster than CREPE on CPU, making it ideal for efficient, real-time deployment. To address the critical lack of perfectly accurate ground truth pitch in speech corpora (which typically rely on algorithmic estimators or laryngograph signals), we introduce \emph{SpeechSynth}. This synthetic speech dataset, generated by a phoneme-level TTS model, provides exact, on-demand ground-truth pitch curves, enabling more robust model training and evaluation. Furthermore, we propose a unified metric, combining six complementary performance measures for comprehensive and reliable pitch evaluation, and release an open-source pitch benchmark suite. A live demo of SwiftF0 is available at https://swift-f0.github.io/, the source code at https://github.com/lars76/swift-f0, and the benchmark framework at https://github.com/lars76/pitch-benchmark.