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Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics

1
Department of Electronic Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan
2
Department of Electrical and Computer Engineering, COMSATS University Islamabad—Wah Campus, Wah Cantt 47040, Pakistan
3
Computer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Electronics 2019, 8(5), 483; https://doi.org/10.3390/electronics8050483
Received: 27 March 2019 / Revised: 15 April 2019 / Accepted: 23 April 2019 / Published: 30 April 2019
(This article belongs to the Special Issue Machine Learning Techniques for Assistive Robotics)
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Abstract

Classification of complex acoustic scenes under real time scenarios is an active domain which has engaged several researchers lately form the machine learning community. A variety of techniques have been proposed for acoustic patterns or scene classification including natural soundscapes such as rain/thunder, and urban soundscapes such as restaurants/streets, etc. In this work, we present a framework for automatic acoustic classification for behavioral robotics. Motivated by several texture classification algorithms used in computer vision, a modified feature descriptor for sound is proposed which incorporates a combination of 1-D local ternary patterns (1D-LTP) and baseline method Mel-frequency cepstral coefficients (MFCC). The extracted feature vector is later classified using a multi-class support vector machine (SVM), which is selected as a base classifier. The proposed method is validated on two standard benchmark datasets i.e., DCASE and RWCP and achieves accuracies of 97.38 % and 94.10 % , respectively. A comparative analysis demonstrates that the proposed scheme performs exceptionally well compared to other feature descriptors. View Full-Text
Keywords: feature extraction; sound classification; support vector machine; sound processing; robotics; MFCC feature extraction; sound classification; support vector machine; sound processing; robotics; MFCC
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Aziz, S.; Awais, M.; Akram, T.; Khan, U.; Alhussein, M.; Aurangzeb, K. Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics. Electronics 2019, 8, 483.

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