Data Augmentation and Deep Learning Methods in Sound Classification: A Systematic Review

The aim of this systematic literature review (SLR) is to identify and critically evaluate current research advancements with respect to small data and the use of data augmentation methods to increase the amount of data available for deep learning classifiers for sound (including voice, speech, and r...

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Bibliographic Details
Published in:Electronics (Basel) 2022-11, Vol.11 (22), p.3795
Main Authors: Abayomi-Alli, Olusola O., Damaševičius, Robertas, Qazi, Atika, Adedoyin-Olowe, Mariam, Misra, Sanjay
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Audio signals
Automatic classification
Classification
Classifiers
Data augmentation
Datasets
Deep learning
Electronic data processing
Feature extraction
Keywords
Literature reviews
Machine learning
Methods
Natural language processing
Neural networks
Signal classification
Signal processing
Sound
Systematic review
Trends
Voice recognition
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Description
Summary:The aim of this systematic literature review (SLR) is to identify and critically evaluate current research advancements with respect to small data and the use of data augmentation methods to increase the amount of data available for deep learning classifiers for sound (including voice, speech, and related audio signals) classification. Methodology: This SLR was carried out based on the standard SLR guidelines based on PRISMA, and three bibliographic databases were examined, namely, Web of Science, SCOPUS, and IEEE Xplore. Findings. The initial search findings using the variety of keyword combinations in the last five years (2017–2021) resulted in a total of 131 papers. To select relevant articles that are within the scope of this study, we adopted some screening exclusion criteria and snowballing (forward and backward snowballing) which resulted in 56 selected articles. Originality: Shortcomings of previous research studies include the lack of sufficient data, weakly labelled data, unbalanced datasets, noisy datasets, poor representations of sound features, and the lack of effective augmentation approach affecting the overall performance of classifiers, which we discuss in this article. Following the analysis of identified articles, we overview the sound datasets, feature extraction methods, data augmentation techniques, and its applications in different areas in the sound classification research problem. Finally, we conclude with the summary of SLR, answers to research questions, and recommendations for the sound classification task.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11223795