Multi Genre Music Classification and Conversion System

Full Text (PDF, 613KB), PP.30-36

Views: 0 Downloads: 0

Author(s)

Irfan Siddavatam 1,* Ashwini Dalvi 1 Dipen Gupta 1 Zaid Farooqui 1 Mihir Chouhan 1

1. Department of Information Technology, K.J.Somaiya College of Engineering, Mumbai-400077

* Corresponding author.

DOI: https://doi.org/10.5815/ijieeb.2020.01.04

Received: 29 Aug. 2019 / Revised: 10 Sep. 2019 / Accepted: 25 Oct. 2019 / Published: 8 Feb. 2020

Index Terms

Music, Artificial Intelligence, Genre, Music Classification, Music Conversion, Convolution Neural Network (CNN)

Abstract

Artificial Intelligence (AI) has a huge scope in automating, stream- lining, and increasing productivity of Music Industry. Here, we look upon AI based techniques for classifying a piece of music into multiple genres and then later converting it into another user-specified genre. Plenty of work has been done in classification, but using traditional machine learning models which are limited in term of accuracy and rely heavily on features to train the model. The novelty of this work lies in its attempt to covert genre of music from one type to another. This paper focuses on classification achieved by using a model trained via Convolutional Neural Networks. Conversion of music genre, a relatively less worked upon field has been discussed in this paper along with details of implementation. For Conversion, we initially convert the input file to spectrogram. A database of all genre is maintained at all times and a random file from user selected genre is also converted to spectrogram. Later, these spectrograms are processed and converted back to signals. Finally the user can listen to the converted audio file. Validation of the conversion was performed via a survey with the help of end users. Thus, a novel idea of doing Music Genre Conversion was put forth and was validated with positive outcomes.

Cite This Paper

Irfan Siddavatam, Ashwini Dalvi, Dipen Gupta, Zaid Farooqui, Mihir Chouhan, "Multi Genre Music Classification and Conversion System", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.12, No.1, pp. 30-36, 2020. DOI:10.5815/ijieeb.2020.01.04

Reference

[1]Pacha, Alexander, and Horst Eidenberger.( 2017) “Towards Self-Learning Optical Mu- sic Recognition.” Proceedings of the 16th IEEE International Conference On Machine Learning and Applications.
[2]Reed, Jeremy, and Chin-Hui Lee. (2011) “Preference music ratings prediction using tokenization and minimum classification error training.” IEEE Transactions on Audio, Speech, and Language Processing 19.8: 2294-2303.
[3]Tsunoo, Emiru, et al. (2011) “Beyond timbral statistics: Improving music classification using percussive patterns and bass lines.” IEEE Transactions on Audio, Speech, and Language Processing 19.4: 1003-1014.
[4]Lu, Jing, et al. (2009) “Music style classification using support vector machine.”, 452-455.
[5]Ren, Jia-Min, Ming-Ju Wu, and Jyh-Shing Roger Jang. (2015) “Automatic music mood classification based on timbre and modulation features.” IEEE Transactions on Affective Computing 6.3: 236-246.
[6]Ridoean, Johanes Andre, et al. (2017) “Music mood classification using audio power and audio harmonicity based on MPEG-7 audio features and Support Vector Machine.” Science in Information Technology (ICSITech), 2017 3rd International Conference on. IEEE.
[7]Quinto, Rene Josiah M., Rowel O. Atienza, and Nestor Michael C. Tiglao. (2017) “Jazz music sub-genre classification using deep learning.” Region 10 Conference, TENCON 2017-2017 IEEE.
[8]Srinivas, M., Debaditya Roy, and C. Krishna Mohan. (2014) “Music genre classification using On-line Dictionary Learning” Neural Networks (IJCNN), 2014 International Joint Conference on. IEEE.
[9]Ren, Jia-Min, Ming-Ju Wu, and Jyh-Shing Roger Jang. (2015) “Automatic music mood classification based on timbre and modulation features.” IEEE Transactions on Affective Computing 6.3: 236-246.
[10]Xue, Angela, and Nick Dupoux. ”Predicting A Songs Commercial Success Based on Lyrics and Other Metrics.”
[11]Fu, Zhouyu, et al. (2011) “A survey of audio-based music classification and annota- tion.” IEEE transactions on multimedia 13.2: 303-319.
[12]Karatana, Ali, and Oktay Yildiz. (2017) “Music genre classification with machine learning techniques” Signal Processing and Communications Applications Confer- ence (SIU), 2017 25th. IEEE.
[13]Panagakis, Yannis, Constantine L. Kotropoulos, and Gonzalo R. Arce. (2014) “Music genre classification via joint sparse low-rank representation of audio features.” IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP) 22.12: 1905-1917.
[14]Kravitz, Aaron, Eliza Lupone, and Ryan Diaz. ”From Classical To Hip- Hop: Can Machines Learn Genres?.” folk 13.192: 22-13.
[15]Benetos, Emmanouil, and Constantine Kotropoulos. (2010) “Non-negative tensor fac- torization applied to music genre classification.” IEEE Transactions on Audio, Speech, and Language Processing 18.8: 1955-1967.
[16]Su, Shih-Yang, et al. (2017) “Automatic conversion of Pop music into chiptunes for 8- bit pixel art.” Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on. IEEE.
[17]Musical Taste Differences Between Men And Women - https://joyruffen.com/musical-taste-differences-men-women/
[18]Audio Texture and style transfer - https://dmitryulyanov.github.io/audio-texture-synthesis-and-style-transfer/
[19]http://marsyas.info/downloads/datasets.html