Sound Classification Using Convolutional Neural Network and Tensor Deep Stacking Network

In every aspect of human life, sound plays an important role. From personal security to critical surveillance, sound is a key element to develop the automated systems for these fields. Few systems are already in the market, but their efficiency is a point of concern for their implementation in real-...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.7717-7727
Main Authors: Khamparia, Aditya, Gupta, Deepak, Nguyen, Nhu Gia, Khanna, Ashish, Pandey, Babita, Tiwari, Prayag
Format: Article
Language:English
Subjects:
Acoustics
Artificial neural networks
Background noise
Classification
Computer architecture
convolutional neural network
Convolutional neural networks
Datasets
Deep learning
Feature extraction
Image classification
Machine learning
Mathematical analysis
Neural networks
Sound
Spectrogram
Spectrograms
Stacking
tensor deep stacking networks
Tensors
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Summary:In every aspect of human life, sound plays an important role. From personal security to critical surveillance, sound is a key element to develop the automated systems for these fields. Few systems are already in the market, but their efficiency is a point of concern for their implementation in real-life scenarios. The learning capabilities of the deep learning architectures can be used to develop the sound classification systems to overcome efficiency issues of the traditional systems. Our aim, in this paper, is to use the deep learning networks for classifying the environmental sounds based on the generated spectrograms of these sounds. We used the spectrogram images of environmental sounds to train the convolutional neural network (CNN) and the tensor deep stacking network (TDSN). We used two datasets for our experiment: ESC-10 and ESC-50. Both systems were trained on these datasets, and the achieved accuracy was 77% and 49% in CNN and 56% in TDSN trained on the ESC-10. From this experiment, it is concluded that the proposed approach for sound classification using the spectrogram images of sounds can be efficiently used to develop the sound classification and recognition systems.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2888882