Automatic Content based Classification of Speech Audio using Multiple Instance Learning
Vivek P1, Lajish V L2
1Vivek P., Department of computer science, University of Calicut, Kerala, India.
2Lajish V. L., Department of computer science, University of Calicut, Kerala, India.
Manuscript received on February 12, 2020. | Revised Manuscript received on February 21, 2020. | Manuscript published on March 30, 2020. | PP: 410-414 | Volume-8 Issue-6, March 2020. | Retrieval Number: E5616018520/2020©BEIESP | DOI: 10.35940/ijrte.E5616.038620
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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 content understanding is an active research problem in the area of speech analytics. A novel approach for content-based news audio classification using Multiple Instance Learning (MIL) approach is introduced in this paper. Content-based analysis provides useful information for audio classification as well as segmentation. A key step taken in this direction is to propose a classifier that can predict the category of the input audio sample. There are two types of features used for audio content detection, namely, Perceptual Linear Prediction (PLP) coefficients and Mel-Frequency Cepstral Coefficients (MFCC). Two MIL techniques viz. mi-Graph and mi-SVM are used for classification purpose. The results obtained using these methods are evaluated using different performance matrices. From the experimental results, it is marked that the MIL demonstrates excellent audio classification capability.
Keywords: Audio classification, Multiple Instance Learning (MIL); Feature extraction; mi-Graph; mi-SVM.
Scope of the Article: Artificial Intelligence and machine learning