Earticle

초록

영어

Automatic extraction of the index of broadcast streams from radio and television has become a challenging research topic over the last years. The automatic classification of audio types, such as speech, music, noises/atypical events etc, has found numerous applications. In this paper we study the evaluation of different machine learning algorithms, which have successfully been used in other classification tasks, on the task of classification of audio broadcast news. The audio classification scheme consists of pre-processing, audio parameterization with established audio features, and classification to acoustic events. The experimental evaluation was carried out using the Voice of America broadcast recordings database for the Greek language. The experimental results indicated that the best performance, approximately 92% of accuracy, was achieved by the classification scheme using the boosting technique with decision trees.

목차

Abstract
 1. Introduction
 2. Scheme Architecture Description
 3. Experimental Setup
  3.1. Broadcast News Database
  3.2. Audio Parameterization
  3.3. Classification Algorithms
 4. Experimental Results
 5. Conclusions
 References

키워드

저자

• Theodoros Theodorou [ Artificial Intelligence Group, Wire Communications Laboratory, Dept. of Electrical and Computer Engineering, University of Patras ]
• Iosif Mporas [ Artificial Intelligence Group, Wire Communications Laboratory, Dept. of Electrical and Computer Engineering, University of Patras, Dept. of Informatics and Means of Mass Communication, Technological Educational Institute of Patras ]
• Nikos Fakotakis [ Artificial Intelligence Group, Wire Communications Laboratory, Dept. of Electrical and Computer Engineering, University of Patras ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605