Next Article in Journal
Medical Instructed Real-Time Assistant for Patient with Glaucoma and Diabetic Conditions
Previous Article in Journal
Crab Bioturbation and Seasonality Control Nitrous Oxide Emissions in Semiarid Mangrove Forests (Ceará, Brazil)
Previous Article in Special Issue
Mining Characteristic Patterns for Comparative Music Corpus Analysis
Open AccessArticle

Efficient Melody Extraction Based on Extreme Learning Machine

Information Science and Technology College, Dalian Maritime University, Dalian 116023, China
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
Sinwt Technology Company Limited, Beijing 100029, China
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(7), 2213;
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop