Abstract
Given a piece of acoustic musical signal, various automatic music transcription (AMT) processing methods have been proposed to generate the corresponding music notations without human intervention. However, the existing AMT methods based on signal processing or machine learning cannot perfectly restore the original music signal and have significant distortion. In this paper, we propose a novel processing method which integrates various AMT methods so as to achieve better performance on music transcription. This integrated method is based on the entropic regularized Wasserstein Barycenter algorithm to speed up the computation of the Wasserstein distance and minimize the distance between two discrete distributions. Moreover, we introduce the proportional transportation distance (PTD) to evaluate the performance of different methods. Experimental results show that the precision and accuracy of the proposed method increase by approximately 48% and 67% respectively compared with the existing methods.
Supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 61631016, National Key Research and Development Plan of Ministry of Science and Technology No. 2018YFB1403903 and the Fundamental Research Funds for the Central Universities No. CUC2019E002, CUC19ZD003.
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Jin, C. et al. (2020). An Integrated Processing Method Based on Wasserstein Barycenter Algorithm for Automatic Music Transcription. In: Gao, H., Feng, Z., Yu, J., Wu, J. (eds) Communications and Networking. ChinaCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-030-41117-6_19
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