An audio-based vehicle classifier using convolutional neural network

Abstract

Audio-based event and scene classification are getting more attention in recent years. Many examples of environmental noise detection, vehicle classification, and soundscape analysis are developed using state of art deep learning techniques. The major noise source in urban and rural areas is traffic noise. Environmental noise parameters for urban and rural small roads have not been investigated due to some practical reasons. The purpose of this study is to develop an audio-based traffic classifier for rural and urban small roads which have limited or no traffic flow data to supply values for noise mapping and other noise metrics. To develop an audio-based vehicle classifier a convolutional neural network-based algorithm was proposed using Mel spectrogram of audio signals as an input feature. Different variations of the network were generated by changing the parameters of the convolutional layers and the length of the network. Filter size, number of filters were tested with a dataset prepared with various real-life traffic records and audio extracts from traffic videos. The precision of the networks was evaluated with the common performance metrics. Further assessments were conducted with longer audio files and predictions of the system compared with actual traffic flow. The results showed that convolutional neural networks can be used to classify road traffic noise sources and perform outstandingly for single or double-lane roads.