Musical information retrieval. Signal Analysis and Feature Extraction using Python

Research Paper (postgraduate) from the year 2021 in the subject Musicology - Miscellaneous, grade: 8.0, , course: IMSc Mathematics and Computing, language: English, abstract: This work gives a comprehensive overview of research on the multidisciplinary field of Music Information Retrieval (MIR). MIR uses knowledge from areas as diverse as signal processing, machine learning, information and music theory. The Main Feature of this work is to explore how this knowledge can be used for the development of novel methodologies for browsing and retrieval on large music collections, a hot topic given recent advances in online music distribution and searching. Emphasis would be given to audio signal processing techniques. Music information retrieval (MIR) is the interdisciplinary science of retrieving information from music. MIR is a small but growing field of research with many realworld applications. Those involved in MIR may have a background in musicology, sychoacoustics, psychology, academic music study, signal processing, informatics, machine learning, optical music recognition, computational intelligence or some combination of these. MIR is being used by businesses and academics to categorize, manipulate and even create music. One of the classical MIR research topics is genre classification, which is categorizing music items into one of pre-defined genres such as classical, jazz, rock, etc. Mood classification, artist classification, and music tagging are also popular topics.

Dr. Soubhik Chakraborty, an M.Sc. (Statistics), PhD (Science) and NET (UGC/CSIR) in Mathematical Sciences, is currently serving as the Professor & Head, Department of Mathematics at Birla Institute of Technology, Mesra, Ranchi, India. His research interests are algorithm analysis, music analysis and statistical computing. He has published several books, research monograms and research papers in peer reviewed journals of international and national repute in these areas apart from guiding several research scholars leading to PhD. He is also an acknowledged reviewer associated with ACM, AMS and IEEE. He has been a visiting scientist twice to Indian Statistical Institute (Bangalore Centre in 2002, Kolkata Centre in 2004, the latter under INSA fellowship). He is a leading figure in computational musicology and has written the first book on the topic (in the context of Hindustani music; see ref [1] in relevant publications). He has been the principal investigator of a UGC major research project titled Analyzing the structure and performance of Hindustani classical music through statistics in his institute. He has received several awards in both teaching and research including the National Award for Teaching Excellence (Mathematics) given by Indus Foundation (2013) and the Best Academic Researcher Award 2013 given by Association of Scientists, Developers and Faculties (2013). Prior to joining this institute in 2006 (Nov 30), he served as a lecturer in Statistics at T.M. Bhagalpur university where he taught Statistics at both undergraduate and postgraduate levels for about ten years. Relevant Publications (books):- 1.Soubhik Chakraborty, Guerino Mazzola, Swarima Tewari and Moujhuri Patra, Computational Musicology in Hindustani Music, Springer, 2014 2.Asoke Kumar Datta, Sandeep Singh Solanki, Ranjan Sengupta, Soubhik Chakraborty, Kartik Mahto, Anirban Patranabis, Signal Analysis of Hindustani Classical Music, Springer, 2017 3.Shashi Bhushan Singh, Soubhik Chakraborty, Keashav Mohan Jha, Satish Chandra, Shanti Prakash and Swarima Tewari, Music and Medicine: Healing Brain Injury through Ragas, CBH publications, 2016