©2017, IJCERT All Rights Reserved Page |43 Volume 4, Issue 2, February-2017, pp. 43-47 ISSN (O): 2349-7084 International Journal of Computer Engineering In Research Trends Music Genre Classification Using MFCC, K-NN and SVM Classifier Nilesh M. Patil 1 , Dr. Milind U. Nemade 2 , Ph.D Research Scholar 1 , Pacific Academy of Higher Education and Research University, Udaipur, India. Email ID: nileshdeep@gmail.com. Professor & Head 2 , Department of Electronics Engineering, K J Somaiya Institute of Engineering & Information Technology, Mumbai. Email ID: munemade@gmail.com -------------------------------------------------------------------------------------------------------------------------------- Abstract:-The audio corpus available today on Internet and Digital Libraries is increasing rapidly in huge volume. We need to properly index them if we want to have access to these audio data. The search engines available in market also find it challenging to classify and retrieve the audio files relevant to the user’s interest. In this paper, we describe an automated classification system model for music genres. We firstly found good feature for each music genre. To obtain feature vectors for the classifiers from the GTZAN genre dataset, features like MFCC vector, chroma frequencies, spectral roll-off, spectral centroid, zero-crossing rate were used. Different classifiers were trained and used to classify, each yielding varying degrees of accuracy in prediction. Keywords: Music, MFCC, K-NN, SVM, GTZAN dataset. ----------------------------------------------------------------------------------------------------------------------------------------------- 1. Introduction: Music classification is an interesting problem with varying applications from Drinkify to Pandora. Music classification is still considered to be one of the research area due to the challenge in selection and extraction of optimal audio features. Music genre classification has been a challenging task in the field of Music Information Retrieval (MIR). Music genres are inherently subjective due to which they are hard to systematically and consistently describe. Genre classification, till now, had been done manually by concatenating it to metadata repository of audio files. This paper however aims at content-based classification, focusing on information within the audio. We used traditional machine learning approach for classification by finding suitable features of audio signals, training classifier on feature data and make predictions.. 2. Related Works Tzanetakis and Cook [1] pioneered the work on music genre classification using machine learning technique. They created the GTZAN dataset and is to date considered as a standard for genre classification. Changsheng Xu et al. [2] have shown how to use support vector machines (SVM) for this task. Authors used supervised learning approaches for music genre classification. Scaringella et al. [3] gives a comprehensive survey of both features and classification techniques used in the music genre classification. Riedmiller [4] used unsupervised learning creating a dictionary of features. 3. Description An open source software framework called MARSYAS (Music Analysis, Retrieval, and Synthesis for Audio Signals) is available for audio processing with specific emphasis on Music Information Retrieval Applications [6]. MARSYAS website gives access to GTZAN dataset which is a collection of 900 audio tracks each 30 seconds long. There are 9 genres represented, each containing 100 tracks. All the tracks are 22050Hz Mono Available online at: www.ijcert.org