Earticle

초록

영어

The automatic classification of urban sounds is important for environmental monitoring. In this work we employ SAX-based Multiresolution Motif Discovery to generate features for Urban Sound Classification. Our approach consists in the discovery of relevant frequent motifs in the audio signals and use the frequency of discovered motifs as characterizing attributes. We explore and evaluate different configurations of motif discovery for defining attributes. In the automatic classification step we use a decision tree based algorithm, random forests and SVM. Results obtained are compared with the ones using Mel-Frequency Cepstral Coefficients (MFCC) as features. MFCCs are commonly used in environmental sound analysis, as well as in other sound classification tasks. Experiments were performed on the Urban Sound dataset, which is publicly available. Our results indicate that we can separate difficult pairs of classes (where MFCC fails) using the motif approach for feature construction.

목차

Abstract
 1. Introduction
 2. Background
  2.1 Feature Extraction using MFCC
  2.2 Multiresolution Motif Discovery
 3. Dataset
 4. MFCCs based Classification
  4.1 Methodology
 5. Motif based Classification
  5.1 Methodology
 6. Experiments
  6.1 Generating Features
 7. Conclusions and Future Work
 ACKNOWLEDGEMENTS
 References

키워드

저자

• Elsa Ferreira Gomes [ GECAD- Knowledge Engineering and Decision Support Research Center, ISEP-School of Engineering, Polytechnic of Porto, Portugal ]
• Fábio Batista [ ISEP-School of Engineering, Polytechnic of Porto, Portugal ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605