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Akademický článek

A supervised term ranking model for diversity enhanced biomedical information retrieval

Autor: Bo Xu, Hongfei Lin, Liang Yang, Kan Xu, Yijia Zhang, Dongyu Zhang, Zhihao Yang, Jian Wang, Yuan Lin, Fuliang Yin

Publikováno v: BMC Bioinformatics, Vol 20, Iss S16, Pp 1-11 (2019)

Abstract Background The number of biomedical research articles have increased exponentially with the advancement of biomedicine in recent years. These articles have thus brought a great difficulty in obtaining the needed information of researchers. I

Externí odkaz: https://doaj.org/article/7a04f26f35ab4502a926e04f44d8247b

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Akademický článek

Differential Evolution and Local Search based Monarch Butterfly Optimization Algorithm with Applications

Autor: Xingyue Cui, Zhe Chen, Fuliang Yin

Publikováno v: International Journal of Computational Intelligence Systems, Vol 12, Iss 1 (2018)

Global optimization for nonlinear function is a challenging issue. In this paper, an improved monarch butterfly algorithm based on local search and differential evolution is proposed. Local search strategy is first embedded into original monarch butt

Externí odkaz: https://doaj.org/article/29071fb6d9f944989d319c6db836357d

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Akademický článek

Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping

Autor: Weiwei Zhang, Zhe Chen, Fuliang Yin

Publikováno v: Applied Sciences, Vol 8, Iss 9, p 1618 (2018)

A new architecture for melody extraction from polyphonic music is explored in this paper. Specifically, chromagrams are first constructed through the harmonic pitch class profile (HPCP) to measure the salience of melody, and chroma-level notes are tr

Externí odkaz: https://doaj.org/article/4871fe03a5fc44b6a5d3ec1f39b8b3ca

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Akademický článek

Combining Superdirective Beamforming and Frequency-Domain Blind Source Separation for Highly Reverberant Signals

Autor: Lin Wang, Heping Ding, Fuliang Yin

Publikováno v: EURASIP Journal on Audio, Speech, and Music Processing, Vol 2010 (2010)

Frequency-domain blind source separation (BSS) performs poorly in high reverberation because the independence assumption collapses at each frequency bins when the number of bins increases. To improve the separation result, this paper proposes a metho

Externí odkaz: https://doaj.org/article/2d7ae00a82864db3ae601f91879f2f54

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