Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation

Association Rule Mining and Audio Signal Processing for Music Discovery and Recommendation

Md. Mahfuzur Rahman Siddiquee (Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh), Md. Saifur Rahman (Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh), Shahnewaz Ul Islam Chowdhury (Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh) and Rashedur M. Rahman (Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh)
Copyright: © 2016 |Pages: 17
DOI: 10.4018/IJSI.2016040105
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Abstract

In this research, the authors propose an intelligent system that can recommend songs to user according to his choice. They predict the next song a user might prefer to listen based on their previous listening patterns, currently played songs and similar music based on music data. To calculate music similarity the authors used a Matlab toolbox that considers audio signals. They used association rule mining to find users' listening patterns and predict the next song the user might prefer. As they propose a music discovery service as well, the authors use the information of music listening pattern and music data similarity to recommend a new song. Later in result section, they replaced the audio based similarity with last.fm api for similar song listing and analyzed the behaviour of their system with the new list of songs.
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The authors in (Kim, Lee, Yoon & Lee, 2008) designed a music recommendation system based on personal preference analysis. First they built a music model using Hidden Markov Models (HMM) with Mel Frequency Cepstral Coefficients (MFCC). They calculated HMM for each song, representing one model for each song. They check the similarity between models and calculate and store vectors for similarity. They also use an improved K-Means algorithm which limits the radius of a cluster. For analyzing preference and assign weights to clusters, they set higher weights to clusters which are further away from each other and fewer weights to clusters which are near. Their recommendation system uses these weights to model similarity to recommend music to users. Their system proves to provide very accurate recommendations for some particular generes.

Mandel and Ellis, in their paper (Mandel & Ellis, 2006), showed that using support vector machine (SVM) classifier with song level feature are better than using only SVM classifier. They also showed that use of Kullback-Leibler (KL) divergence for measuring distance is better than Mahalanobis distance over Gaussian model. They used 20 –coefficient MFCCs as suggested by Aucouturier and Pachet (Pachet & Aucouturier, 2004) and a “bag of frames” model for modeling the MFCCs. After extracting MFCCs, the mean and covariance of them are described in a Gaussian model. For distance measurement, they used both Mahalonobis and closed form of KL (Penny, 2001) divergence. They conducted experiments with the combinations of single Gaussian, GMM with KL divergence. They also compared artist-level versus song-level feature and SVM versus non-SVM classifiers. They showed that the use of single Gaussian is an effective way of capturing and comparing song-level feature (MFCC).

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