Application of Elman Back Propagation Neural Network for Automatic Identification of Tabla Strokes in North Indian Classical Music
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 3 | Views: 353 | Weekly Hits: ⮙1 | Monthly Hits: ⮙1

Research Paper | Computer Science & Engineering | India | Volume 11 Issue 4, April 2022 | Popularity: 5.2 / 10


     

Application of Elman Back Propagation Neural Network for Automatic Identification of Tabla Strokes in North Indian Classical Music

Shambhavi Shete, Saurabh Deshmukh


Abstract: Tabla is the most useful accompanying percussion instrument used in North Indian Classical Music. The homophonic sound produced from Tabla instruments generates multiple harmonics. Therefore, Tabla stroke identification is a challenging task. Tabla stroke identification has various applications such as Tempo Estimation, Beat Tracking, Rhythm Identification, Tala Recognition, Automatic Music Transcription to name a few. This research aims to compare Elman Neural Network (ENN) with various neural network architectures useful for automatic Tabla stroke identification. This comparison would be useful to appropriately select the ENN for the applications of Tabla stroke identification. Studio recordings of 640 Tabla audio excerpts, sampled at 44, 100 Hz sampling frequency are used to train and test the neural networks namely, Feed Forward Back Propagation Neural Network (FFBPNN), Pattern Recognition Neural Network (PRNN), Elman Neural Network (ENN), Cascade Forward Neural Network (CFNN), and Recurrent Neural Network (RNN). The audio features are extracted using traditional Mel Frequency Cepstral Coefficient (MFCC) and Timbral audio descriptors along with MFCC. The Tabla strokes are categorized into two major categories namely, Open and Closed Tabla strokes. The result shows that Tabla stroke identification accuracy is obtained higher for open strokes due to the difference of Attack, Decay, Sustain, and Release values of the strokes. The Tabla stroke identification accuracy of 94.1% is achieved using ENN, for Timbral audio features.


Keywords: Music Information Retrieval, Timbre, MFCC, Elman Neural Network, North Indian Classical Music


Edition: Volume 11 Issue 4, April 2022


Pages: 1155 - 1159


DOI: https://www.doi.org/10.21275/SR22419123931



Make Sure to Disable the Pop-Up Blocker of Web Browser


Text copied to Clipboard!
Shambhavi Shete, Saurabh Deshmukh, "Application of Elman Back Propagation Neural Network for Automatic Identification of Tabla Strokes in North Indian Classical Music", International Journal of Science and Research (IJSR), Volume 11 Issue 4, April 2022, pp. 1155-1159, https://www.ijsr.net/getabstract.php?paperid=SR22419123931, DOI: https://www.doi.org/10.21275/SR22419123931

Similar Articles

Downloads: 103

Review Papers, Computer Science & Engineering, India, Volume 4 Issue 4, April 2015

Pages: 1769 - 1771

A Review of Comparatively Study of Different Speaker Recognition Techniques

Umer Malik

Share this Article

Downloads: 106

Survey Paper, Computer Science & Engineering, India, Volume 4 Issue 7, July 2015

Pages: 2039 - 2041

Age Detection of Singer using KNN Algorithm

Sumeet S. Andhalkar

Share this Article

Downloads: 110

Research Paper, Computer Science & Engineering, India, Volume 3 Issue 7, July 2014

Pages: 1373 - 1377

Symbolic Representation of Speech for Text Independent Speaker Recognition

Akshay S, Apoorva P

Share this Article

Downloads: 112

Survey Paper, Computer Science & Engineering, India, Volume 3 Issue 12, December 2014

Pages: 1545 - 1550

Survey of Audio Feature Representation using Encoding Techniques

Shabnam R. Makandar, V. L. Kolhe

Share this Article

Downloads: 113

Review Papers, Computer Science & Engineering, Indonesia, Volume 6 Issue 11, November 2017

Pages: 1247 - 1251

Trends and Challenges in Developing Context-Aware Music Recommendation Systems

G. A. Vida Mastrika Giri

Share this Article
Top