Name of the Speaker : Gowriprasad R (EE19D702)
Name of the Guide: Dr. Aravind R
Co-Guide: Dr. Hema Murthy A
Venue: ESB 244 (Seminar Hall)
Date/Time : 27th September 2022, 4.00pm
Rhythm is a fundamental dimension of music. Music Information Retrieval (MIR) on rhythm has been primarily focused on Western music (WM). The rhythm analysis of Indian art music Rhythm analysis in IAM has received attention in recent years. Unlike WM, the rhythm accompaniments in IAM are tonic based and also extempore or improvisational similar to that melody in IAM. IAM has an advanced rhythmic framework based on the tala (rhythm cycle), which is quite different from the western notions of rhythm. With a complex and sophisticated rhythmic structure and framework, IAM poses a significant challenge to state-of-the-art rhythm modeling tools MIR models developed for Western music cannot be directly applied to IA. While MIR-based analyses have received some attention for the analysis of mridangam (a percussion instrument) used in Carnatic music (CM), MIR-based analysis of tabla in Hindustan music (HM) has hardly received any attention. In this work, we aspire to primarily analyze the MIR rhythm aspects of tabla in HM and also compare the same with that of mridangam and other percussion instruments used in CM.
The proposed work consists of two parts. The first part addresses rhythm-related selective analysis using a large amount of music data combined with domain knowledge and expertise. The initial analysis deals with the rhythm-based structural segmentation and labeling of tabla solo audios. Later sections deal with the rhythm analyses by comparing and contrasting the percussion technique of tabla and mridangam in both accompaniment and solo performances. The second part analyzes various results from the first part from the human perception and cognition point of view. This part of the research intends to study the effect of various rhythmic and melodic variations of Indian art music on the human brain.
The focus of the talk will be first to review the research efforts on the analysis of percussion instruments in music and then focus our attention on efforts in Indian art music. Finally, we present our preliminary studies on tabla gharana recognition from solo tabla recordings. Teaching practices and performances of tabla are based on stylistic schools called gharana-s. Gharana-s are characterized by their unique playing technique, finger posture, improvisations, and compositional patterns (signature patterns). Recognizing the gharana information from a tabla performance is hence helpful in characterizing the performance. We present two different approaches to the task. The first approach consists of a transcription of tabla audio followed by a signature pattern search. The second approach consists of deep learning models that combine convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The LSTM networks are trained to classify the gharana-s by processing the sequence of extracted features from CNNs.