Audio Event Identification and Classification for Cricket Sports using LSTM | Zendy

Pooja N.K | Zendy; John Sahaya Rani Alex | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Audio Event Identification and Classification for Cricket Sports using LSTM

Author(s) -

Pooja N.K,

John Sahaya Rani Alex

Publication year - 2019

Publication title -

international journal of recent technology and engineering (ijrte)

Language(s) - English

Resource type - Journals

ISSN - 2277-3878

DOI - 10.35940/ijrte.d9462.118419

Subject(s) - mel frequency cepstrum , computer science , event (particle physics) , speech recognition , cricket , artificial neural network , context (archaeology) , identification (biology) , artificial intelligence , sound recording and reproduction , machine learning , pattern recognition (psychology) , feature extraction , ecology , paleontology , physics , botany , quantum mechanics , acoustics , biology

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%.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research