Ambient Sound Recognition using Convolutional Neural Networks

Abstract

Due to its many uses in areas including voice recognition, music analysis, and security systems, sound recognition has attracted a lot of attention. Convolutional neural networks (CNNs) have become a potent tool for sound recognition, producing cutting-edge outcomes in a variety of challenges. In this study, we will look at the architecture of CNNs, several training methods used to enhance their performance, and accuracy testing. The performance of the proposed sound recognition technique has been tested using 1000 audio files from the UrbanSounds8K dataset. The accuracy results obtained by using a CNN and Support Vector Machine (SVM) models were 95.6% and 93% respectively. These results portray the efficiency of using an advanced CNN architecture with five convolution layers and a versatile dataset like Urbansoundsd8K.