An Enhanced Musical Instrument Classification using Deep Convolutional Neural Network
S. Prabavathy1, V. Rathikarani2, P. Dhanalakshmi3
1S. Prabavathy, Department of Computer and Information Science, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamilnadu, India.
2V. Rathikarani, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamilnadu, India.
3P. Dhanalakshmi, Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamilnadu, India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on 30 November, 2019. | PP: 8772-8774 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9271118419/2019©BEIESP | DOI: 10.35940/ijrte.D9271.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Retrieval of musical information from musical databases is a major challenging issue in a digital world. Therefore, it is necessary to develop an efficient tool for retrieving the musical information. Musical instrument classification plays a major role for retrieving the information from musical database. In order to retrieve the musical instrument efficiently, an enhanced musical instrument classification algorithm using deep Convolutional Neural Network is proposed in this paper. The proposed algorithm consists of convolutional layers interleaved with two pooling functions followed by two fully interconnected layers. There are sixteen instruments from different instrument families are taken for evaluating the performance of proposed algorithm. The experimental result shows that the proposed algorithm recognizes the instruments significantly and achieves the greater accuracy than existing algorithm.
Keywords: Deep Convolutional Neural Network, Musical Instrument Classification.
Scope of the Article: Classification.