Hybrid Computerized Method for Environmental Sound Classification

Classification of environmental sounds plays a key role in security, investigation, robotics since the study of the sounds present in a specific environment can allow to get significant insights. Lack of standardized methods for an automatic and effective environmental sound classification (ESC) cre...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.124055-124065
Main Authors: Ullo, Silvia Liberata, Khare, Smith K., Bajaj, Varun, Sinha, G. R.
Format: Article
Language:English
Subjects:
Acoustics
Background noise
Classification
classification techniques
Classifiers
convolutional neural network
Datasets
Decision analysis
Decision trees
Discriminant analysis
Environmental sound classification
Feature extraction
Fourier transforms
Hidden Markov models
Kernels
Microsoft Windows
optimal allocation sampling
Resource management
Robotics
Sound
Spectrogram
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Classification of environmental sounds plays a key role in security, investigation, robotics since the study of the sounds present in a specific environment can allow to get significant insights. Lack of standardized methods for an automatic and effective environmental sound classification (ESC) creates a need to be urgently satisfied. As a response to this limitation, in this paper, a hybrid model for automatic and accurate classification of environmental sounds is proposed. Optimum allocation sampling (OAS) is used to elicit the informative samples from each class. The representative samples obtained by OAS are turned into the spectrogram containing their time-frequency-amplitude representation by using a short-time Fourier transform (STFT). The spectrogram is then given as an input to pre-trained AlexNet and Visual Geometry Group (VGG)-16 networks. Multiple deep features are extracted using the pre-trained networks and classified by using multiple classification techniques namely decision tree (fine, medium, coarse kernel), k-nearest neighbor (fine, medium, cosine, cubic, coarse and weighted kernel), support vector machine, linear discriminant analysis, bagged tree and softmax classifiers. The ESC-10, a ten-class environmental sound dataset, is used for the evaluation of the methodology. An accuracy of 90.1%, 95.8%, 94.7%, 87.9%, 95.6%, and 92.4% is obtained with a decision tree, k-neared neighbor, support vector machine, linear discriminant analysis, bagged tree and softmax classifier respectively. The proposed method proved to be robust, effective, and promising in comparison with other existing state-of-the-art techniques, using the same dataset.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3006082