Audio Event Identification and Classification for Cricket Sports using LSTM

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%.