標題: A new approach for classification of generic audio data
作者: Lin, RS
Chen, LH
資訊工程學系
Department of Computer Science
關鍵字: audio classification;spectrogram;Bayesian decision function;multivariable Gaussian distribution
公開日期: 1-二月-2005
摘要: The existing audio retrieval systems fall into one of two categories: single-domain systems that can accept data of only a single type (e.g. speech) or multiple-domain systems that offer content-based retrieval for multiple types of audio data. Since a single-domain system has limited applications, a multiple-domain system will be more useful. However, different types of audio data will have different properties, this will make a multiple-domain system harder to be developed. If we can classify audio information in advance, the above problems can be solved. In this paper, we will propose a real-time classification method to classify audio signals into several basic audio types such as pure speech, music, song, speech with music background, and speech with environmental noise background. In order to make the proposed method robust for a variety of audio sources, we use Bayesian decision function for multivariable Gaussian distribution instead of manually adjusting a threshold for each discriminator. The proposed approach can be applied to content-based audio/video retrieval. In the experiment, the efficiency and effectiveness of this method are shown by an accuracy rate of more than 96% for general audio data classification.
URI: http://dx.doi.org/10.1142/S0218001405003958
http://hdl.handle.net/11536/23777
ISSN: 0218-0014
DOI: 10.1142/S0218001405003958
期刊: INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Volume: 19
Issue: 1
起始頁: 63
結束頁: 78
顯示於類別:期刊論文


文件中的檔案:

  1. 000228135600004.pdf