Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics

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 <mml:semantics>97.38%</mml:semantics> and <mml:semantics>94.10%</mml:semantics>, respectively. A comparative analysis demonstrates that the proposed scheme performs exceptionally well compared to other feature descriptors.