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MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation by Wei, Haojie; Yuan, Jun; Zhang, Rui; Dai, Quanyu; Chen, Yueguo is a scholarly article available to read on EtoBox.

What is MAJL: A Model-Agnostic Joint Learning Framework for Music Source Separation and Pitch Estimation about?

Music source separation and pitch estimation are two vital tasks in music information retrieval. Typically, the input of pitch estimation is obtained from the output of music source separation. Therefore, existing methods have tried to perform these two tasks simultaneously, so as to leverage the mutually beneficial relationship between both tasks. However, these methods still face two critical challenges that limit the improvement of both tasks: the lack of labeled data and joint learning optimization. To address these challenges, we propose a Model-Agnostic Joint Learning (MAJL) framework for both tasks. MAJL is a generic framework and can use variant models for each task. It includes a two-stage training method and a dynamic weighting method named Dynamic Weights on Hard Samples (DWHS), which addresses the lack of labeled data and joint learning optimization, respectively. Experimental results on public music datasets show that MAJL outperforms state-of-the-art methods on both tasks, with significant improvements of 0.92 in Signal-to-Distortion Ratio (SDR) for music source separation and 2.71% in Raw Pitch Accuracy (RPA) for pitch estimation. Furthermore, comprehensive studies n

Author

Wei, Haojie; Yuan, Jun; Zhang, Rui; Dai, Quanyu; Chen, Yueguo

Published

2025

Language

EN