Evidence map›Paper›PMID 35720906›Full record

ArticleComputational intelligence and neuroscience2022

Optimization Simulation of Dance Technical Movements and Music Matching Based on Multifeature Fusion.

Liusha Dong

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Liusha DongYangtze University, JingZhou 434020, China.ORCID https://orcid.org/0000-0003-0280-9965

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Music and dance videos have been popular among researchers in recent years. Music is one of the most important forms of human communication; it carries a wealth of emotional information, and it is studied using computer tools. In the feature engineering process, most present machine learning approaches suffer from information loss or insufficient extracted features despite the relevance of computer interface and multimedia technologies in sound and music matching tasks. Multifeature fusion is widely utilized in education, aerospace, intelligent transportation, biomedicine, and other fields, and it plays a critical part in how humans get information. In this research, we offer an effective simulation method for matching dance technique movements with music based on multifeature fusion. The initial step is to use music beat extraction theory to segment the synchronized dance movements and music data, then locate mutation points in the music, and dynamically update the pheromones based on the merits of the dance motions. The audio feature sequence is obtained by extracting audio features from the dancing video's accompanying music. Then, we combine the two sequences to create an entropy value sequence based on audio variations. By comparing the consistency of several approaches for optimizing dance movement simulation trials, the optimized simulation method described in this research has an average consistency of 87%, indicating a high consistency. As a result, even though the background and the subject are readily confused, the algorithm in this research can keep a consistent recognition rate for more complicated dance background music, and the approach in this study can still guarantee a certain accuracy rate.

Indexed as

MusicComputer SimulationEmotionsHumansMovementRecognition, Psychology

Identifiers

PMID35720906
PMCPMC9200528

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.