Audio Event Identification and Classification for Cricket Sports using LSTM
Pooja N.K1, John Sahaya Rani Alex.2
1Pooja N.K , School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
2John Sahaya Rani Alex*, School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9924-9927 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9462118419/2019©BEIESP | DOI: 10.35940/ijrte.D9462.118419
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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: Audio event identification is an emerging research topic to augment the automation of audio tagging, context-based audio event retrieval, audio surveillance and much more. In this research work, audio event classification for cricket commentary is done by using long short term memory (LSTM) neural network. Mel-frequency cepstral coefficients (MFCC) features are extracted from the audio commentary and trained with LSTM neural network. The trained LSTM network is validated and attained an accuracy of 95%.
Keywords: Acoustic Event, LSTM, MFCC,NN.
Scope of the Article: Building and Environmental Acoustics.