Automatic Extraction and Identification of Bol from Tabla Signal

In Indian classical music, tabla is the most widely used rhythmic instrument. The instrument has two drums. By striking either of the drums, a bol is produced and it forms the basic component of tala (rhythm). In this work, bols are automatically extracted from tabla signal. Subsequently, features are extracted and used for bol identification. Ideally, a bol follows attack-decay-sustain-release (ADSR) model. A bol has a characteristic rise in the initial attack stage, after which it decays to reach a steady energy level. It sustains that level and, finally, releases the energy. Proposed segmentation methodology exploits this phenomenon to extract the bols. Once the bol segments are extracted, low-level spectral features are computed and used for classification. Multilayer perceptron network is used for bol identification. Experiment is successfully carried out with the signals of recitals by different players and also at different tempo. The result shows that proposed methodology performs quite well on diverse collection. Segmentation and identification of bols can act as the foundation for the applications like transcript generation, tala identification.