Tabla Gharana Recognition from Audio music recordings of Tabla Solo performances

Tabla is a percussion instrument in Hindustani music tradition. Tabla learning and performance in the Indian subcontinent is based on stylistic schools called gharana-s. Each gharana is characterized by its unique style of playing technique, dynamics of tabla strokes, repertoire, compositions, and improvisations. Identifying the gharana from a tabla performance is hence helpful to characterize the performance. This paper addresses the task of automatic gharana recognition from solo tabla recordings. We motivate the problem and present different facets and challenges in the task. We present a comprehensive and diverse collection of over 16 hours of tabla solo recordings for the task. We propose an approach using deep learning models that use a combination of convolutional neural networks (CNN) and long short-term memory (LSTM) networks. The CNNs are used to extract gharana discriminative features from the raw audio data. The LSTM networks are trained to classify the gharana-s by processing the sequence of extracted features from CNNs. Our experiments on gharana recognition include different lengths of audio data and comparison between various aspects of the task. An evaluation demonstrates promising results with the highest recognition accuracy of 93%.