Identifying Similarities Between Musical Files Using Association Rules

The number of music in digital format increases years after years. The amount of data available allows streaming services to offer wide variety of music. This makes these services attractive. Automatic classification process is required to manage and structure all files available. Automatic music classification is an active field of research. Researches rely on machine learning techniques such as deep neural network. These techniques used features extracted from raw data to generate classification. Features have an important impact on results. Selecting the right descriptors is one of the main difficulties associated with automatic music classification. Opacity of current methods makes it difficult to evaluate the contribution of descriptors in the classification process. In this paper, we propose to use association rules to add more transparency and interpretability.

[1]  Man-Kwan Shan,et al.  Relevance feedback for category search in music retrieval based on semantic concept learning , 2008, Multimedia Tools and Applications.

[2]  Mark Sandler,et al.  Convolutional recurrent neural networks for music classification , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  Daniel P. W. Ellis,et al.  Song-Level Features and Support Vector Machines for Music Classification , 2005, ISMIR.

[4]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[5]  Xiaoqing Yu,et al.  Research on fast music classification based on SVM in compressed domain , 2008, 2008 International Conference on Audio, Language and Image Processing.

[6]  Ismaïl Biskri,et al.  Using Association Rules Mining for Retrieving Genre-Specific Music Files , 2017, FLAIRS.

[7]  Jyh-Shing Roger Jang,et al.  Music Genre Classification via Compressive Sampling , 2010, ISMIR.

[8]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[9]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[10]  Andreas Rauber,et al.  Capturing the Temporal Domain in Echonest Features for Improved Classification Effectiveness , 2012, Adaptive Multimedia Retrieval.

[11]  Peter Grosche,et al.  Audio Content-Based Music Retrieval , 2012, Multimodal Music Processing.

[12]  Ichiro Fujinaga,et al.  Automatic Genre Classification Using Large High-Level Musical Feature Sets , 2004, ISMIR.

[13]  Bob L. Sturm.  A Survey of Evaluation in Music Genre Recognition , 2012, Adaptive Multimedia Retrieval.

[14]  Muhammad Asim Ali,et al.  Automatic Music Genres Classification using Machine Learning , 2017 .

[15]  Hema A. Murthy,et al.  Music genre classification by fusion of Modified Group Delay and Melodic Features , 2017, 2017 Twenty-third National Conference on Communications (NCC).

[16]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[17]  Bob L. Sturm.  An analysis of the GTZAN music genre dataset , 2012, MIRUM '12.

[18]  John Z. Zhang,et al.  An Association-based Approach to Genre Classification in Music , 2014, ISMIR.