Wiwiet Herulambang, Rifki Fahrial Zainal


Music genre is one of the digital music data that is determined to classify music based on all the character equations of each type. The characteristics in question are usually seen from the frequency of music, rhythmic structure, instrumentation structure, and harmony content that the music has. Classification of music genres in realtime (automatic / not manual), giving effect to the classification is no longer relative / subjective, because it is done based on predetermined parameters. In this study Raspberry Pi microcomputer is used, which is quite concisely used as a portable media and is quite powerful for realtime data processing. Raspberry Pi is used as a sound processing unit, music genreidentifier, and information on the results of the introduction of the music genre. This system input is in the form of music sound (realtime), while the system output is information (text) about the music genre. Whereas for the process of recognizing the music genre, the Self Organizing Maps (SOM) type Neural Neural Network (JOM) method is also used, or also known as the Kohonen ANN Network. The feature extraction stage uses the Music Genre Recognition by Analysis of Text (MUGRAT) method, with nine features related to the spectral surface of music, and six features related to beat / rhythm of music. Mel Frequency Cepstral Coefficients (MFCCs) feature extraction process was carried out as input from the classification process using the Self Organizing Map (SOM) method. The classification results using the SOM method give an accuracy value of 74.75%. Accuracy of classification results using training data as many as 400 pieces which are divided into 4 musical genres amounting to 74.75%.

Full Text:



Anan, Yoko, Kohei Hatano, Hideo Bannai, Masayuki Takeda, 2011, Music Genre Classification Using

Similarity Functions, Proceedings of the 12th International Society for Music Information

Retrieval Conference, ISMIR 2011, October 24-28 2011, ISBN 978-0-615-54865-4, Miami,

Florida, USA

Arjannikov, Tom, John Z. Zhang, 2014, An Association-Based Approach To Genre Classification In

Music, Proceedings of the 15th International Society for Music Information Retrieval

Conference, ISMIR 2014, October 27-31 2014, Taipei, Taiwan

Herulambang, Wiwiet, Retantyo Wardoyo, 2017, 3D Simulation of Plant Growth Modeling Using

Neuro-Fuzzy, Lindenmayer System, and Turtle Geometry, JEECS (Journal Of Electrical

Engineering And Computer Sciences) Vol 2 No 2 2017, Surabaya, Indonesia

Li, Tom LH., Antoni B. Chan, Andy HW. Chun, 2010, Automatic Musical Pattern Feature Extraction

Using Convolutional Neural Network, Proceedings of The International MultiConference of

Engineers and Computer Scientists (IMECS) Vol 1, March 17-19 2010, pp546-550, Hongkong

Norris, Donald J., 2017, Beginning Artificial Intelligence with the Raspberry Pi, ISBN-13 (pbk): 978-

-4842-2742-8, DOI 10.1007/978-1-4842-2743-5, Apress Publishing, Barrington, New

Hampshire, USA

Schmadecke, Ingo, Holger Blume, 2013, High Performance Hardware Architectures for Automated

Music Classification Algorithms from and for Nature and Life, Studies in Classification, Data

Analysis, and Knowledge Organization, DOI 10.1007/978-3-319-00035-0 55, Springer

International Publishing, Switzerland

Schreiber, Hendrik, 2015, Improving Genre Annotations For The Million Song Dataset, Proceedings of

the 16th International Society for Music Information Retrieval Conference, ISMIR

,October 26-30 2015, ISBN 978-84-606-8853-2, Málaga, Spain

Silla Jr, Carlos N., Celso A. A. Kaestner, Alessandro L. Koerich, 2010, Improving automatic music

genre classification with hybrid contentbased feature vectors, Proceedings of the 2010 ACM

Symposium on Applied Computing (SAC’10), pages 1702–1707, New York, USA


  • There are currently no refbacks.