Automatic Classification of Digital Music by Genre

Presented at the Grace Hopper Celebration of Women in Computing (GHC’12) Research Poster, Baltimore, MD, USA and also presented at the Women in Machine Learning Workshop (WiML ’12), Research Poster, Lake Tahoe, Nevada, USA.Over the past two decades, advances in the digital music industry have resulted in an exponential growth in music data sets. This exponential growth has in turn spurred great interest in music information retrieval (MIR) problems, organizing large music collections, and content-based search methods for digital music libraries. Equally important are the related problems in music classification such as genre classification, music mood analysis, and artist identification. Music genre classification is a well-studied problem in the music information retrieval community and has a wide range of applications. In this project we address the problem of genre classification by representing the MFCC feature vectors in an extended semantic space. We combine this audio representation with machine learning techniques to perform genre classification with the goal of obtaining higher classification accuracy

Content-Based Music Genre Classification Using Sparse Approximation Techniques

Presented at the Drexel IEEE Graduate Forum’s Fifth Annual Research SymposiumIn this study we evaluated the performance of genre classification systems using various feature vectors and learning methods. Using a fixed classifier, i.e., the Gaussian mixture models we were able to create a suboptimal feature vector to characterize the audio signals in a low dimensional feature space. We then utilized this modified feature representation to solve the problem of music genre classification. We evaluated the performance of the recent sparsity-eager support vector machines classifier using the proposed feature vector and compared the results to the classic support vector machines and Gaussian mixture models as the baseline classifiers

Music genre classification using On-line Dictionary Learning

In this paper, an approach for music genre classification based on sparse representation using MARSYAS features is proposed. The MARSYAS feature descriptor consisting of timbral texture, pitch and beat related features is used for the classification of music genre. On-line Dictionary Learning (ODL) is used to achieve sparse representation of the features for developing dictionaries for each musical genre. We demonstrate the efficacy of the proposed framework on the Latin Music Database (LMD) consisting of over 3000 tracks spanning 10 genres namely Axé, Bachata, Bolero, Forró, Gaúcha, Merengue, Pagode, Salsa, Sertaneja and Tango

Music genre classification using On-line Dictionary Learning

In this paper, an approach for music genre classification based on sparse representation using MARSYAS features is proposed. The MARSYAS feature descriptor consisting of timbral texture, pitch and beat related features is used for the classification of music genre. On-line Dictionary Learning (ODL) is used to achieve sparse representation of the features for developing dictionaries for each musical genre. We demonstrate the efficacy of the proposed framework on the Latin Music Database (LMD) consisting of over 3000 tracks spanning 10 genres namely Axé, Bachata, Bolero, Forró, Gaúcha, Merengue, Pagode, Salsa, Sertaneja and Tango

Analyzing and improving genre and style classification in music through experiments

Music classification is a core task in the field of Music Information Retrieval (MIR). Classification refers to recognizing patterns in data. Music classification assigns genre, style, mood and etc. to each piece of music, to facilitate managing music data. It is an interesting topic in MIR with potential applications. There has been a considerable deal of attention focused on variety issues of music classification, such as selection appropriate feature sets, feature selection techniques, classification algorithm, etc. In this thesis, a series of empirical experiments are conducted to investigate and evaluate the genre and style classification in music. To validate our investigations and evaluations, several methods are proposed to analyze and interpret the results. In addition, we also design and implement an effective classification approach that obtains higher classification accuracy