Environmental Sound Classification Using Deep Learning

Perhaps hearing impairment individuals cannot identify the environmental sounds due to noise around them. However, very little research has been conducted in this domain. Hence, the aim of this study is to categorize sounds generated in the environment so that the impairment individuals can distinguish the sound categories. To that end first we define nine sound classes--air conditioner, car horn, children playing, dog bark, drilling, engine idling, jackhammer, siren, and street music-- typically exist in the environment. Then we record 100 sound samples from each category and extract features of each sound category using Mel-Frequency Cepstral Coefficients (MFCC). The training dataset is developed using this set of features together with the class variable; sound category. Sound classification is a complex task and hence, we use two Deep Learning techniques; Multi Layer Perceptron (MLP) and Convolution Neural Network (CNN) to train classification models. The models are tested using a separate test set and the performances of the models are evaluated using precision, recall and F1-score. The results show that the CNN model outperforms the MLP. However, the MLP also provided a decent accuracy in classifying unknown environmental sounds.