Environmental sounds recognition using TESPAR

Environmental sounds depict the sound content of varieties of creatures´ survival and activities, and also closely related with the human living environment. Current conventional approaches for recognition of environmental sounds required important computational resources and employing complex signal processing methods in the frequency domain. This work proposes a low-complexity method named Time Encoded Signal Processing and Recognition (TESPAR for short). The computational requirements for this method are two orders of magnitude less than that required by other usual methods. We used the TESPAR coding method to produce simple data structures, and then used the archetypes technique for classification. Our method was tested on two databases, database 1 consisted of 10 classes of bird sounds to test the interspecific recognition, database 2 consisted of 10 classes of different environmental sounds to test intraspecific recognition. We also did the experiments on the same databases using MFCC and SVM to make a comparison. Results showed that TESPAR has lower training time complexity than SVM, and the recognition rate of intraspecific recognition was better than interspecific recognition.