Classification of audio signals using generalized spatial fuzzy clustering

Huynh Van Luong, Cheol Hong Kim, Jong-Myon Kim

Research output: Contribution to journalConference abstract in journalResearchpeer-review


With the increasing use of multimedia data, the need for automatic classification and retrieval of certain kinds of audio data has become an important issue. In this paper, we propose an efficient method of audio signal segmentation and classification from audiovisual database. While conventional methods apply thresholding to audio features such as energy and zero‐crossing rate to detect the boundaries, causing misclassification for audio signals which contain certain audio effects such as fade‐in, fade‐out, and cross‐fade, the proposed algorithm, called general spatial fuzzy c‐means algorithm (GSFCM), solves the problem by taking into account the local spatial information which is weighted correspondingly to neighbor elements based on their distance attributes. GSFCM detects the boundaries between two different audio signals, classifies segments, and then extracts unique feature vectors. This results in the accurate detection and classification. Experiment results for the audio signal from TV news program at 44.1 kHz with 30‐min long confirm that the proposed method outperforms conventional methods in terms of accuracy of the audio signal classification. These results demonstrate that the proposed method is a suitable candidate for audio‐video indexing which is compressed by MPEG.
Original languageEnglish
JournalJournal of the Acoustical Society of America
Issue number4
Pages (from-to)2699
Number of pages1
Publication statusPublished - 2009
Externally publishedYes
Event157th Meeting of the Acoustical Society of America - Portland, Oregon, United States
Duration: 18 May 200922 May 2009
Conference number: 157


Conference157th Meeting of the Acoustical Society of America
Country/TerritoryUnited States
CityPortland, Oregon


Dive into the research topics of 'Classification of audio signals using generalized spatial fuzzy clustering'. Together they form a unique fingerprint.

Cite this