A new pyramidal concatenated CNN approach for environmental sound classification

Abstract

Recently, there has been an incremental interest on Environmental Sound Classification (ESC), which is an important topic of the non-speech audio classification task. A novel approach, which is based on deep Convolutional Neural Networks (CNN), is proposed in this study. The proposed approach covers a bunch of stages such as pre-processing, deep learning based feature extraction, feature concatenation, feature reduction and classification, respectively. In the first stage, the input sound signals are denoised and are converted into sound images by using the Sort Time Fourier Transform (STFT) method. After sound images are formed, pre-trained CNN models are used for deep feature extraction. In this stage, VGG16, VGG19 and DenseNet201 models are considered. The feature extraction is performed in a pyramidal fashion which makes the dimension of the feature vector quite large. For both dimension reduction and the determination of the most efficient features, a feature selection mechanism is considered after feature concatenation stage. In the last stage of the proposed method, a Support Vector Machines (SVM) classifier is used. The efficiency of the proposed method is calculated on various ESC datasets such as ESC 10, ESC 50 and UrbanSound8K, respectively. The experimental works show that the proposed method produced 94.8%, 81.4% and 78.14% accuracy scores for ESC-10, ESC-50 and UrbanSound8K datasets. The obtained results are also compared with the state-of-the art methods achievements. © 2020 Elsevier Ltd