Individuality music recommendation model based on MFCC and GMM

Bin Niu; Ling Zhi Kong; Sen Lin Luo; Li Min Pan; Liang Guo

Individuality music recommendation model based on MFCC and GMM

Bin Niu^*, Ling Zhi Kong, Sen Lin Luo, Li Min Pan, Liang Guo

^*Corresponding author for this work

School of Information and Electronics

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

A personality music recommendation algorithm model based on Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture model (GMM) is provided. This method extracts MFCC from a certain song as feature parameters, and generates a template of the song using the GMM algorithm. It then gains similar songs from the music library by comparing their templates' through similarity. From the experimental result, the correct rate of the song recommendation is 90%.

Original language	English
Pages (from-to)	351-355
Number of pages	5
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	29
Issue number	4
Publication status	Published - Apr 2009

Keywords

Gaussian mixture model
Mel-frequency cepstrum coefficient
Music recommendation

Cite this

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title = "Individuality music recommendation model based on MFCC and GMM",

abstract = "A personality music recommendation algorithm model based on Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture model (GMM) is provided. This method extracts MFCC from a certain song as feature parameters, and generates a template of the song using the GMM algorithm. It then gains similar songs from the music library by comparing their templates' through similarity. From the experimental result, the correct rate of the song recommendation is 90%.",

keywords = "Gaussian mixture model, Mel-frequency cepstrum coefficient, Music recommendation",

author = "Bin Niu and Kong, {Ling Zhi} and Luo, {Sen Lin} and Pan, {Li Min} and Liang Guo",

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AU - Niu, Bin

AU - Kong, Ling Zhi

AU - Luo, Sen Lin

AU - Pan, Li Min

AU - Guo, Liang

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AB - A personality music recommendation algorithm model based on Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture model (GMM) is provided. This method extracts MFCC from a certain song as feature parameters, and generates a template of the song using the GMM algorithm. It then gains similar songs from the music library by comparing their templates' through similarity. From the experimental result, the correct rate of the song recommendation is 90%.

KW - Gaussian mixture model

KW - Mel-frequency cepstrum coefficient

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Individuality music recommendation model based on MFCC and GMM

Abstract

Keywords

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