Traditional Machine Learning For Pitch Detection

Abstract

Pitch detection is a fundamental problem in speech processing as F0 is used in a large number of applications. Recent articles have proposed deep learning for robust pitch tracking. In this paper, we consider voicing detection as a classification problem and F0 contour estimation as a regression problem. For both tasks, acoustic features from multiple domains and traditional machine learning methods are used. The discrimination power of existing and proposed features is assessed through mutual information. Multiple supervised and unsupervised approaches are compared. A significant relative reduction of voicing errors over the best baseline is obtained: 20% with the best clustering method (K-means) and 45% with a Multi-Layer Perceptron. For F0 contour estimation, the benefits of regression techniques are limited though. We investigate whether those objective gains translate in a parametric synthesis task. Clear perceptual preferences are observed for the proposed approach over two widel

Stats

• citations27
• S2 citations—
• github stars0
• HF likes0
• heat score10.85
• arxiv keydrugman2019traditional

Related papers

• Hf0: A Hybrid Pitch Extraction Method For Multimodal Voice (2019)0.00
• Real-time Pitch/f0 Detection Using Spectrogram Images And Convolutional Neural Networks (2025)0.00
• DEEPF0: End-to-end Fundamental Frequency Estimation For Music And Speech Signals (2021)10.35
• Unsupervised Classification Of Voiced Speech And Pitch Tracking Using Forward-backward Kalman Filtering (2021)0.00
• Self-supervised Learning Of Context-aware Pitch Prosody Representations (2020)0.00
• Waveform To Single Sinusoid Regression To Estimate The F0 Contour From Noisy Speech Using Recurrent Deep Neural Networks (2018)6.77
• Human Voice Pitch Estimation: A Convolutional Network With Auto-labeled And Synthetic Data (2023)0.00
• A Regression Model Of Recurrent Deep Neural Networks For Noise Robust Estimation Of The Fundamental Frequency Contour Of Speech (2018)4.52