NDLI: Audio classification using braided convolutional neural networks

Content Provider	IET Digital Library
Author	Sinha, Harsh Awasthi, Vinayak Ajmera, Pawan K.
Abstract	Convolutional neural networks (CNNs) work surprisingly well and have helped drastically enhance the state-of-the-art techniques in the domain of image classification. The unprecedented success motivated the application of CNNs to the domain of auditory data. Recent publications suggest hidden Markov models and deep neural networks for audio classification. This study aims to achieve audio classification by representing audio as spectrogram images and then use a CNN-based architecture for classification. This study presents an innovative strategy for a CNN-based neural architecture that learns a sparse representation imitating the receptive neurons in the primary auditory cortex in mammals. The feasibility of the proposed CNN-based neural architecture is assessed for audio classification tasks on standard benchmark datasets such as Google Speech Commands datasets (GSCv1 and GSCv2) and the UrbanSound8K dataset (US8K). The proposed CNN architecture, referred to as braided convolutional neural network, achieves 97.15, 95 and 91.9% average recognition accuracy on GSCv1, GSCv2 and US8 K datasets, respectively, outperforming other deep learning architectures.
Starting Page	448
Ending Page	454
Page Count	7
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 7, Sep (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0381
Journal	IET Signal Processing
Publisher Date	2020-06-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Audio Classification Tasks Braided Convolutional Neural Network CNN Architecture CNN-based Neural Architecture Computer Vision And Image Processing Technique Convolutional Neural Nets Deep Learning Architectures Deep Neural Network Google Speech Command Datasets Hidden Markov Model Image Classification Image Recognition Image Representation Learning in AI Markov Processes Neural Computing Technique Neurophysiology Primary Auditory Cortex Receptive Neurons Sparse Representation Spectrogram Image UrbanSound8K Dataset
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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