Abstract

Anomaly detection is a crucial problem that has garnered attention across various research areas and application domains. Numerous anomaly detection techniques have been developed, with specific focus on domains such as sound or speech recognition. Sound recognition applications are commonly employed in health system monitoring and control, making them particularly relevant in this study. The objective of this research is to detect anomalies in sound, enabling informed decision-making regarding the handling of such sounds. For this study, the TUT Rare Sound Events 2017 dataset is utilized, comprising 2987 audio files. These files encompass isolated sound events for each target class with the noise background noise from everyday acoustic scenes. The dataset is divided into a training and testing set with the split of 80:20. The Extreme Learning Machine (ELM) method is employed for the learning process. The accuracy and performance of the ELM method are evaluated through calculations. The results reveal that the ELM method demonstrates promising capabilities in detecting anomalies within raw audio data, achieving an accuracy of 93.98%. Notably, the highest accuracy of 92.13% is achieved when detecting the anomaly of baby cry. These findings highlight the effectiveness of the ELM method in anomaly detection within sound data.

Original languageEnglish
Title of host publication2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages238-242
Number of pages5
ISBN (Electronic)9798350312164
DOIs
Publication statusPublished - 2023
Event14th International Conference on Information and Communication Technology and System, ICTS 2023 - Surabaya, Indonesia
Duration: 4 Oct 20235 Oct 2023

Publication series

Name2023 14th International Conference on Information and Communication Technology and System, ICTS 2023

Conference

Conference14th International Conference on Information and Communication Technology and System, ICTS 2023
Country/TerritoryIndonesia
CitySurabaya
Period4/10/235/10/23

Keywords

  • Anomaly Detection
  • Deep Learning
  • Extreme Learning Machine
  • Sound Recognition
  • TUT Rare Sound Events 2017

Fingerprint

Dive into the research topics of 'Anomaly Detection in Raw Audio Using Extreme Learning Machine'. Together they form a unique fingerprint.

Cite this