Abstract
In this paper, we present a content-adaptive audio texture based method to segment video into audio scenes. The audio scene is modeled as a semantically consistent chunk of audio data. Our algorithm is based on "semantic audio texture analysis." At first, we train GMM models for basic audio classes such as speech, music, etc. Then we define the semantic audio texture based on those classes. We study and present two types of scene changes, those corresponding to an overall audio texture change and those corresponding to a special "transition marker" used by the content creator, such as a short stretch of music in a sitcom or silence in dramatic content. Unlike prior work using genre specific heuristics, such as some methods presented for detecting commercials, we adaptively find out if such special transition markers are being used and if so, which of the base classes are being used as markers without any prior knowledge about the content. Our experimental results show that our proposed audio scene segmentation works well across a wide variety of broadcast content genres.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 6820 |
| DOIs | |
| State | Published - May 15 2008 |
| Event | Multimedia Content Access: Algorithms and Systems II - San Jose, CA, United States Duration: Jan 30 2008 → Jan 31 2008 |
Other
| Other | Multimedia Content Access: Algorithms and Systems II |
|---|---|
| Country | United States |
| City | San Jose, CA |
| Period | 1/30/08 → 1/31/08 |
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Keywords
- Audio scene
- Segmentation
- Semantic texture
- SVM
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics
Cite this
Audio scene segmentation for video with generic content. / Niu, Feng; Goela, Naveen; Divakaran, Ajay; Abdel-Mottaleb, Mohamed.
Proceedings of SPIE - The International Society for Optical Engineering. Vol. 6820 2008. 68200S.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Audio scene segmentation for video with generic content
AU - Niu, Feng
AU - Goela, Naveen
AU - Divakaran, Ajay
AU - Abdel-Mottaleb, Mohamed
PY - 2008/5/15
Y1 - 2008/5/15
N2 - In this paper, we present a content-adaptive audio texture based method to segment video into audio scenes. The audio scene is modeled as a semantically consistent chunk of audio data. Our algorithm is based on "semantic audio texture analysis." At first, we train GMM models for basic audio classes such as speech, music, etc. Then we define the semantic audio texture based on those classes. We study and present two types of scene changes, those corresponding to an overall audio texture change and those corresponding to a special "transition marker" used by the content creator, such as a short stretch of music in a sitcom or silence in dramatic content. Unlike prior work using genre specific heuristics, such as some methods presented for detecting commercials, we adaptively find out if such special transition markers are being used and if so, which of the base classes are being used as markers without any prior knowledge about the content. Our experimental results show that our proposed audio scene segmentation works well across a wide variety of broadcast content genres.
AB - In this paper, we present a content-adaptive audio texture based method to segment video into audio scenes. The audio scene is modeled as a semantically consistent chunk of audio data. Our algorithm is based on "semantic audio texture analysis." At first, we train GMM models for basic audio classes such as speech, music, etc. Then we define the semantic audio texture based on those classes. We study and present two types of scene changes, those corresponding to an overall audio texture change and those corresponding to a special "transition marker" used by the content creator, such as a short stretch of music in a sitcom or silence in dramatic content. Unlike prior work using genre specific heuristics, such as some methods presented for detecting commercials, we adaptively find out if such special transition markers are being used and if so, which of the base classes are being used as markers without any prior knowledge about the content. Our experimental results show that our proposed audio scene segmentation works well across a wide variety of broadcast content genres.
KW - Audio scene
KW - Segmentation
KW - Semantic texture
KW - SVM
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U2 - 10.1117/12.760267
DO - 10.1117/12.760267
M3 - Conference contribution
SN - 9780819469922
VL - 6820
BT - Proceedings of SPIE - The International Society for Optical Engineering
ER -