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C. Vegiris, C. Dimoulas, and G. Papanikolaou, "Audio Content Annotation, Description, and Management Using Joint Audio Detection, Segmentation, and Classification Techniques," Paper 7661, (2009 May.). doi: C. Vegiris, C. Dimoulas, and G. Papanikolaou, "Audio Content Annotation, Description, and Management Using Joint Audio Detection, Segmentation, and Classification Techniques," Paper 7661, (2009 May.). doi:
Abstract: The current paper focuses on audio content management by means of joint audio segmentation and classification. We concentrate on the separation of typical audio classes, such as silence / background noise, speech, music and their combinations. A compact feature-vector subset is selected by a Correlation feature selection subset evaluation algorithm after the use of EM clustering algorithm on an initial audio data set. Time and spectral parameters are extracted using filter-banks and wavelets in combination with sliding windows and exponential moving averaging techniques. Features are extracted on a point-to-point basis, using the finest possible time resolution, so that each sample can be individually classified to one of the available groups. Clustering algorithms like EM or Simple K-means are tested to evaluate the final point-to-point classification result, therefore the joint audio detection-classification indexes. The extracted audio detection, segmentation and classification results can be incorporated into appropriate description schemes that would annotate audio events / segments for content description and management purposes.

@article{vegiris2009audio,
author={vegiris, christos and dimoulas, charalampos and papanikolaou, george},
journal={journal of the audio engineering society},
title={audio content annotation, description, and management using joint audio detection, segmentation, and classification techniques},
year={2009},
volume={},
number={},
pages={},
doi={},
month={may},} @article{vegiris2009audio,
author={vegiris, christos and dimoulas, charalampos and papanikolaou, george},
journal={journal of the audio engineering society},
title={audio content annotation, description, and management using joint audio detection, segmentation, and classification techniques},
year={2009},
volume={},
number={},
pages={},
doi={},
month={may},
abstract={the current paper focuses on audio content management by means of joint audio segmentation and classification. we concentrate on the separation of typical audio classes, such as silence / background noise, speech, music and their combinations. a compact feature-vector subset is selected by a correlation feature selection subset evaluation algorithm after the use of em clustering algorithm on an initial audio data set. time and spectral parameters are extracted using filter-banks and wavelets in combination with sliding windows and exponential moving averaging techniques. features are extracted on a point-to-point basis, using the finest possible time resolution, so that each sample can be individually classified to one of the available groups. clustering algorithms like em or simple k-means are tested to evaluate the final point-to-point classification result, therefore the joint audio detection-classification indexes. the extracted audio detection, segmentation and classification results can be incorporated into appropriate description schemes that would annotate audio events / segments for content description and management purposes.},}

TY - paper
TI - Audio Content Annotation, Description, and Management Using Joint Audio Detection, Segmentation, and Classification Techniques
SP - EP -
AU - Vegiris, Christos
AU - Dimoulas, Charalampos
AU - Papanikolaou, George
PY - 2009
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2009 TY - paper
TI - Audio Content Annotation, Description, and Management Using Joint Audio Detection, Segmentation, and Classification Techniques
SP - EP -
AU - Vegiris, Christos
AU - Dimoulas, Charalampos
AU - Papanikolaou, George
PY - 2009
JO - Journal of the Audio Engineering Society
IS -
VO -
VL -
Y1 - May 2009
AB - The current paper focuses on audio content management by means of joint audio segmentation and classification. We concentrate on the separation of typical audio classes, such as silence / background noise, speech, music and their combinations. A compact feature-vector subset is selected by a Correlation feature selection subset evaluation algorithm after the use of EM clustering algorithm on an initial audio data set. Time and spectral parameters are extracted using filter-banks and wavelets in combination with sliding windows and exponential moving averaging techniques. Features are extracted on a point-to-point basis, using the finest possible time resolution, so that each sample can be individually classified to one of the available groups. Clustering algorithms like EM or Simple K-means are tested to evaluate the final point-to-point classification result, therefore the joint audio detection-classification indexes. The extracted audio detection, segmentation and classification results can be incorporated into appropriate description schemes that would annotate audio events / segments for content description and management purposes.