EasyChair Preprint № 4916 Signal Processing and Machine Learning Approaches and Evaluation for Indian Classical Music Rajashekhar Shastry and Anita Bai EasyChair preprints are intended for rapid dissemination of research results and are integrated with the rest of EasyChair. January 19, 2021 Signal Processing and Machine Learning Approaches and Evaluation for Indian Classical Music Rajashekar Shastry1 and Dr Anita Bai2 1Research Scholar, KL University, Guntur, Andhra Pradesh, India, 2Associate Professor, KL University, Guntur, Andhra Pradesh, India,
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[email protected] Abstract. In the field of signal processing, a considerable amount of work has been done in the analysis and implementation of various techniques related to speech signal processing. When it comes to the Music signal processing, the techniques that are originally developed for speech signal processing have been applied to music signal with good results. In comparison with Indian classical music, lot of research work done in Western classical music on music audio analysis like information retrieval, singer/instrument identification, genre detection, etc.,. This paper provides an overview of some signal processing, and machine learning techniques that specifically applied to the Indian classical music for analyzing and identifying features such as tonic identification, genre classification, raga recognition, music transcription, rhythm, and timbre. We will look at how particular features of Indian Classical music signals impact and how these features are extracted. 1. Introduction Traditionally rich in Indian classical music, its roots date back to Vedic scriptures more than 6,000 years ago, when tunes formed a system of musical notes and rhythmic cycles[1][2]. Indian classical music is truly linked to nature; numerous ragas were produced based on the seasons and time of the day, influenced by nature.