**Automatic Scene Recognition through Acoustic Classification for Behavioral Robotics**

#### **Sumair Aziz 1, Muhammad Awais 2,\*, Tallha Akram 2, Umar Khan 1, Musaed Alhussein 3 and Khursheed Aurangzeb 3,\***


#### Received: 27 March 2019; Accepted: 23 April 2019; Published: 30 April 2019

**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.

**Keywords:** feature extraction; sound classification; support vector machine; sound processing; robotics; MFCC
