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

^*此作品的通讯作者

信息与电子学院

科研成果: 期刊稿件 › 文章 › 同行评审

4 引用（Scopus）

摘要

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%.

源语言	英语
页（从-至）	351-355
页数	5
期刊	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
卷	29
期	4
出版状态	已出版 - 4月 2009

其它文件与链接

链接到 Scopus 的出版物

引用此

Niu, B., Kong, L. Z., Luo, S. L., Pan, L. M., & Guo, L. (2009). Individuality music recommendation model based on MFCC and GMM. Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology, 29(4), 351-355.

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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 - Pan, Li Min

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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

摘要

其它文件与链接

指纹

引用此