Year: 2026
Type: conference-paper
Abstract: Estimating the fundamental frequency, or melody, is a core task in Music Information Retrieval (MIR). Various studies have explored signal processing, machine learning, and deep-learning-based approaches, with a very recent focus on utilizing uncertainty in active learning settings for melody estimation. However, these approaches do not investigate the relative effectiveness of different uncertainties. In this work, we follow a framework that disentangles aleatoric and epistemic uncertainties to guide active learning for melody estimation. Trained on a source dataset, our model adapts to new domains using only a small number of labeled samples. Experimental results demonstrate that epistemic uncertainty is more reliable for domain adaptation with reduced labeling effort as compared to aleatoric uncertainty.
Authors: Aayush Jaiswal, Parampreet Singh, Vipul Arora
Institution: Indian Institute of Technology Kanpur
Language: English
Topic: Music and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415254233
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