Rare Sound Detection Using GUI Based Python Software, “echoNet”

EasyChair Preprint 12545

Abstract

The exponential growth of digital technologies has led to a massive increase in the volume of multimedia data generated from various smart devices such as smartphones, cameras, and audio recording devices. This massive volume of data has made it difficult to extract useful information from the multimedia data.

This study proposes an efficient technique for anomaly detection and classification of rare events in audio data. The proposed technique is based on a deep learning-based approach that uses a convolutional neural network (CNN) to extract high-level features from the audio data. The extracted features are then used to train a support vector machine (SVM) classifier that can accurately detect and classify rare events in the audio data. The proposed technique has several advantages over traditional anomaly detection techniques.

Keyphrases: CNN, HMM, SVM

@booklet{EasyChair:12545,
  author    = {Nitesh Kumar and Pratham Gandhi and Neeraj Kumar and Garvit Saini},
  title     = {Rare Sound Detection Using GUI Based Python Software, “echoNet”},
  howpublished = {EasyChair Preprint 12545},
  year      = {EasyChair, 2024}}