Pattern Recognition Approaches in Music Analytics

Volume 5 Issue 2 June - August 2018

Review Paper

Pattern Recognition Approaches in Music Analytics

Makarand Velankar*, Parag Arun Kulkarni**

* Assistant Professor, Department of Information Technology, MKSSS's Cummins College of Engineering and PhD Research Scholar PICT, SPPU Pune, Maharashtra, India.

** Founder, Chief Scientist and CEO, iknowlation Research Labs Pvt. Ltd., Pune, Maharashtra, India.

Velankar, M., and Kulkarni, P. A (2018). Pattern recognition approaches in music analytics. i-manager’s Journal on Pattern Recognition, 5(2), 37-46. https://doi.org/10.26634/jpr.5.2.14784

Abstract

Content based Music Information Retrieval (MIR) has been a study matter for MIR research group since the inception of the group. Different pattern recognition paradigms are used for the diverse application for content-based music information retrieval. Music is a multidimensional phenomenon posing severe investigation tasks. Diverse tasks such as automatic music transcription, music recommendation, style identification, music classification, emotion modeling etc. requires quantitative and qualitative analysis. In spite of noteworthy efforts, the conclusions revealed shows latency over correctness achieved in different tasks. This paper covers different feature learning techniques used for music data in conventional audio pattern in different digital signal processing domains. Considering the remarkable improvements in results for applications related to speech and image processing using deep learning approach, similar efforts are attempted in the domain of music data analytics. Deep learning applied for music analytics applications are covered along with music adversaries reported. Future directions in conventional and deep learning approach with evaluation criteria for pattern recognition approaches in music analytics are explored.