• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Automatic melody extraction algorithm using a convolutional neural network

Vol. 11, No.12, December 31, 2017
10.3837/tiis.2017.12.019, Download Paper (Free):

Abstract

In this study, we propose an automatic melody extraction algorithm using deep learning. In this algorithm, feature images, generated using the energy of frequency band, are extracted from polyphonic audio files and a deep learning technique, a convolutional neural network (CNN), is applied on the feature images. In the training data, a short frame of polyphonic music is labeled as a musical note and a classifier based on CNN is learned in order to determine a pitch value of a short frame of audio signal. We want to build a novel structure of melody extraction, thus the proposed algorithm has a simple structure and instead of using various signal processing techniques for melody extraction, we use only a CNN to find a melody from a polyphonic audio. Despite of simple structure, the promising results are obtained in the experiments. Compared with state-of-the-art algorithms, the proposed algorithm did not give the best result, but comparable results were obtained and we believe they could be improved with the appropriate training data. In this paper, melody extraction and the proposed algorithm are introduced first, and the proposed algorithm is then further explained in detail. Finally, we present our experiment and the comparison of results follows.


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Cite this article

[IEEE Style]
Jongseol Lee, Dalwon Jang and Kyoungro Yoon, "Automatic melody extraction algorithm using a convolutional neural network," KSII Transactions on Internet and Information Systems, vol. 11, no. 12, pp. 6038-6053, 2017. DOI: 10.3837/tiis.2017.12.019

[ACM Style]
Lee, J., Jang, D., and Yoon, K. 2017. Automatic melody extraction algorithm using a convolutional neural network. KSII Transactions on Internet and Information Systems, 11, 12, (2017), 6038-6053. DOI: 10.3837/tiis.2017.12.019