Ambient Sound Recognition using Convolutional Neural Networks

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 potent tool for producing cutting-edge outcomes variety challenges. In this study, we will look at the architecture CNNs, several training methods used enhance their performance, accuracy testing. The performance proposed technique been tested using 1000 audio files from UrbanSounds8K dataset. results obtained by CNN Support Vector Machine (SVM) models were 95.6% 93% respectively. These portray efficiency an advanced with five convolution layers versatile dataset like Urbansoundsd8K.