基于MFCC和GMM的个性音乐推荐模型

Individuality Music Recommendation Model Based on MFCC and GMM

摘要: 提出一种基于Mel频率倒谱系数(MFCC)和高斯混合模型(GMM)的个性音乐推荐模型的建立方法. 该方法采用MFCC技术提取歌曲的语音特征,并利用GMM算法生成该歌曲的模板,然后利用音乐模板库对音乐文件进行相似度计算. 实验结果表明,利用该模型为用户推荐的歌曲平均准确率为90%.

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%.