While current approaches for audiovisual data segmentation and classification are mostly focused on visual cues, audio signals may actually play a more important role in content parsing for many applications. An approach to automatic segmentation and classification of audiovisual data based on audio content analysis is proposed. The audio signal from movies or TV programs is segmented and class...

The MPEG-4 Audio Standard provides audio and speech coding for natural and synthetic content at bitrates ranging from 2 to 64 kbit/s and above. While the first version of MPEG-4 Audio was finalised in 1998, work continues for Version 2, complementing MPEG-4 Audio by the following new tools: Error Resilience, Low-Delay Audio Coding, Small Step Scalability, Parametric Audio Coding, and Environmen...

Audio recordings of the environment are an increasingly important technique to monitor biodiversity and ecosystem function. While the acquisition of long-duration recordings is becoming easier and cheaper, the analysis and interpretation of that audio remains a significant research area. The issue addressed in this paper is the automated reduction of environmental audio data to facilitate ecolo...

A hierarchical system for audio classiication and retrieval based on audio content analysis is presented in this paper. The system consists of three stages. The audio recordings are rst classiied and segmented into speech, music, several types of environmental sounds, and silence, based on morphological and statistical analysis of temporal curves of the energy function, the average zero-crossin...

A hierarchical system for audio classi cation and retrieval based on audio content analysis is presented in this paper. The system consists of three stages. The audio recordings are rst classi ed and segmented into speech, music, several types of environmental sounds, and silence, based on morphological and statistical analysis of temporal curves of the energy function, the average zero-crossin...

Content Based Audio Retrieval system is very helpful to facilitate users to find the target audio materials. Audio signals are classified into speech, music, several types of environmental sounds and silence based on audio content analysis. The extracted audio features include temporal curves of the average zero-crossing rate, the spectral Centroid, the spectral flux, as well as spectral roll-o...

A hierarchical system for audio classi cation and retrieval based on audio content analysis is presented in this paper. The system consists of three stages. The rst stage is called the coarse-level audio classi cation and segmentation, where audio recordings are classi ed and segmented into speech, music, several types of environmental sounds, and silence, based on morphological and statistical...