Environmental Sound Classification: A descriptive review of the literature

Abstract

Automatic environmental sound classification (ESC) is one of the upcoming areas of research as most of the traditional studies are focused on speech and music signals. Classifying environmental sounds such as glass breaking, helicopter, baby crying and many more can aid in surveillance systems as well as criminal investigations. In this paper, a vast range of literature in the field of ESC is elucidated from various facets like preprocessing, feature extraction, and classification techniques. Researchers have used various noise removal and signal enhancement techniques to preprocess the signals. This paper explicates multitude of datasets used in recent studies along with the year of publication and maximum accuracy achieved with the dataset. Deep Neural Networks surpass the traditional machine learning classifiers. The future challenges and prospective research in this field are proposed. Since no recent review on ESC has been published, this study will open up novel ways for certain business applications and security systems.

Keywords

• Environmental Sound Classification
• Feature extraction
• Feature selection
• Machine learning classifiers
• Deep neural networks