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Open AccessArticle

Efficient Melody Extraction Based on Extreme Learning Machine

1
Information Science and Technology College, Dalian Maritime University, Dalian 116023, China
2
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
3
Sinwt Technology Company Limited, Beijing 100029, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(7), 2213; https://doi.org/10.3390/app10072213
Received: 21 January 2020 / Revised: 21 February 2020 / Accepted: 17 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Sound and Music Computing -- Music and Interaction)
Melody extraction is an important task in music information retrieval community and it is unresolved due to the complex nature of real-world recordings. In this paper, the melody extraction problem is addressed in the extreme learning machine (ELM) framework. More specifically, the input musical signal is first pre-processed to mimic the human auditory system. The music features are then constructed by constant-Q transform (CQT), and the concentration strategy is introduced to make use of contextual information. Afterwards, the rough melody pitches are determined by ELM network, according to its pre-trained parameters. Finally, the rough melody pitches are fine-tuned by the spectral peaks around the frame-wise rough pitches. The proposed method can extract melody from polyphonic music efficiently and effectively, where pitch estimation and voicing detection are conducted jointly. Some experiments have been conducted based on three publicly available datasets. The experimental results reveal that the proposed method achieves higher overall accuracies with very fast speed. View Full-Text
Keywords: melody extraction; efficient melody extraction; extreme learning machine; constant-Q transform melody extraction; efficient melody extraction; extreme learning machine; constant-Q transform
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MDPI and ACS Style

Zhang, W.; Zhang, Q.; Bi, S.; Fang, S.; Dai, J. Efficient Melody Extraction Based on Extreme Learning Machine. Appl. Sci. 2020, 10, 2213.

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