Comparative Analysis of Random Forests and Inception-v3 for Broadcast Audio Classification

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Paper Details

Paper Title

Comparative Analysis of Random Forests and Inception-v3 for Broadcast Audio Classification

Authors

Kamatchy B, P. Dhanalakshmi

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Abstract

This paper focuses on the comparative analysis of two different audio pattern classifiers for classifying TV broadcast audio data into one of five categories namely Advertisement, Cartoon, News, Songs and Sports. For Classifying TV broadcast audio data, machine learning algorithm Random Forests and deep learning pretrained model Inception -v3 are implemented and the results are compared. In this comparative analysis, the pretrained model Inception-v3 exhibits increased efficiency in classifying the broadcast audio data.

Keywords- Audio Segmentation, Mel Frequency Cepstral Coefficients (MFCC), Audio Classification, Inception-v3, Random Forests, Spectrogram, Transfer learning.

Publication Details

Unique Identification Number - IJEDR2004034
Page Number(s) - 206-210
Pubished in - Volume 8 | Issue 4 | December 2020
DOI (Digital Object Identifier) -
Publisher - IJEDR (ISSN - 2321-9939)

Cite this Article

Kamatchy B, P. Dhanalakshmi, "Comparative Analysis of Random Forests and Inception-v3 for Broadcast Audio Classification", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.8, Issue 4, pp.206-210, December 2020, Available at :http://www.ijedr.org/papers/IJEDR2004034.pdf

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