Automatic Environmental Sound Recognition (AESR) Using Convolutional Neural Network

Automatic Environmental Sound Recognition (AESR) is an essential topic in modern research in the field of pattern recognition. We can convert a short audio file of a sound event into a spectrogram image and feed that image to the Convolutional Neural Network (CNN) for processing. Features generated...

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Bibliographic Details
Published inInternational journal of modern education and computer science Vol. 12; no. 5; pp. 41 - 54
Main Authors Ahmed, Md. Rayhan, Islam Robin, Towhidul, Ali Shafin, Ashfaq
Format Journal Article
LanguageEnglish
Published Hong Kong Modern Education and Computer Science Press 08.10.2020
Subjects
Accuracy
Artificial neural networks
Audio data
Background noise
Classification
Comparative Analysis
Datasets
Feature recognition
Model accuracy
Neural networks
Pattern recognition
Sound
Training
Waveforms
Wavelet transforms
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Summary:Automatic Environmental Sound Recognition (AESR) is an essential topic in modern research in the field of pattern recognition. We can convert a short audio file of a sound event into a spectrogram image and feed that image to the Convolutional Neural Network (CNN) for processing. Features generated from that image are used for the classification of various environmental sound events such as sea waves, fire cracking, dog barking, lightning, raining, and many more. We have used the log-mel spectrogram auditory feature for training our six-layer stack CNN model. We evaluated the accuracy of our model for classifying the environmental sounds in three publicly available datasets and achieved an accuracy of 92.9% in the urbansound8k dataset, 91.7% accuracy in the ESC-10 dataset, and 65.8% accuracy in the ESC-50 dataset. These results show remarkable improvement in precise environmental sound recognition using only stack CNN compared to multiple previous works, and also show the efficiency of the log-mel spectrogram feature in sound recognition compared to Mel Frequency Cepstral Coefficients (MFCC), Wavelet Transformation, and raw waveform. We have also experimented with the newly published Rectified Adam (RAdam) as the optimizer. Our study also shows a comparative analysis between the Adaptive Learning Rate Optimizer (Adam) and RAdam optimizer used in training the model to correctly classifying the environmental sounds from image recognition architecture.
ISSN:2075-0161
2075-017X
DOI:10.5815/ijmecs.2020.05.04