The optimization of musician influence model and the trait analysis of music in different genres

Qimin Tian

doi:10.1117/12.2639283

15 July 2022 The optimization of musician influence model and the trait analysis of music in different genres

Qimin Tian

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 1225811 (2022) https://doi.org/10.1117/12.2639283
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

For the purpose of better measuring the influence of musicians and exploring the similarities and characteristics of various genres, we need to optimize the Musician Influence Model properly and represent musical features in an appropriate way. The previous model was established based on the correction of followers’ and influencers’ number of each musician and the correction of total number of actual musicians according to genre proportion, which did not take the change of genre proportion over time and indirect influence of musician into consideration. To better optimize it, I innovatively introduced “direct number”, “indirect number” and “strong number” of followers and influencers, and all of them was corrected by proper functions. Through the standardization of data and the frequent use of entropy weight method, the relationship between the data became more accurate and reliable. The advantages a musician should have to be more influential can be assumed after obtaining the ranking table with Bob Dylan, The Beatles, Radiohead, Avril Lavigne and Alan Jackson being the top five. Besides, I selected six parameters - danceability, energy, valence, Tempo, acousticness and instrumentalness, to characterize the music. And I selected 6 representative genres based on the analysis of the proportion, musician number and average popularity of all genres. We draw some conclusions by analyzing the musical characteristics radar map of each genre.

Citation Download Citation

Qimin Tian "The optimization of musician influence model and the trait analysis of music in different genres", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 1225811 (15 July 2022); https://doi.org/10.1117/12.2639283

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Optimization (mathematics)

Radar

Data modeling

Data processing

Analytical research

Affine motion model

Data analysis

Show All Keywords

Keywords/Phrases

Search In:

Publication Years