Real Time Audio-Based Search in Media Files Using Machine Learning  
  Authors : Swati Krishnan; Sahil Raina; Neha Aher

 

This paper explores the various audio processing and matching methodologies available to extrapolate an algorithm which can be applied in real-time for effective audio extraction from audio-visual files and then searching for certain user defined audio patterns in said media file. With the exponential rise in multimedia content, the need to search and find information contained in these assets is a must. We propose to build tool which will enable the user to search across the spoken content of any audiovisual file chosen locally on his/her machine.

 

Published In : IJCAT Journal Volume 1, Issue 10

Date of Publication : 30 November 2014

Pages : 507 - 511

Figures :05

Tables : --

Publication Link :Real Time Audio-Based Search in Media Files Using Machine Learning

 

 

 

Swati Krishnan : Currently pursuing B.E. from the Computer Science Department of Maharashtra Institute of Technology College of Engineering, Pune (2014-2015 batch)

Sahil Raina : Currently pursuing B.E. from the Computer Science Department of Maharashtra Institute of Technology College of Engineering, Pune (2014-2015 batch)

Neha Aher : Currently pursuing B.E. from the Computer Science Department of Maharashtra Institute of Technology College of Engineering, Pune (2014-2015 batch)

 

 

 

 

 

 

 

Phonetic Search

Audio Indexing

Audio Retrieval

Machine Learning

Audio Acquisition

Fourier Transforms

Mel-frequency cepstral coefficients

Hidden Markov Model

In this study, we have explored the hitherto unexplored area of audio matching on a local machine, in real time. Our method will seek the media based on the spoken input from the user. For a general user, this study will thus help move media-seeking to its next logical conclusion. Also, with the increase in its usage, the proposed method will get better at correctly matching the recorded and the extracted audio, using machine learning, thereby increasing its accuracy in cases where background noise might hamper the results.

 

 

 

 

 

 

 

 

 

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