Name
Title | ||
|---|---|---|
On the Applicability of Speaker Diarization to Audio Concept Detection for Multimedia Retrieval |
Abstract | ||
|---|---|---|
Recently, audio concepts emerged as a useful building block in multimodal video retrieval systems. Information like "this file contains laughter", "this file contains engine sounds" or "this file contains slow music" can significantly improve purely visual based retrieval. The weak point of current approaches to audio concept detection is that they heavily rely on human annotators. In most approaches, audio material is manually inspected to identify relevant concepts. Then instances that contain examples of relevant concepts are selected--again manually--and used to train concept detectors. This approach comes with two major disadvantages: (1) it leads to rather abstract audio concepts that hardly cover the audio domain at hand and (2) the way human annotators identify audio concepts likely differs from the way a computer algorithm clusters audio data--introducing additional noise in training data. This paper explores whether unsupervized audio segementation systems can be used to identify useful audio concepts by analyzing training data automatically and whether these audio concepts can be used for multimedia document classification and retrieval. A modified version of the ICSI (International Computer Science Institute) speaker diarization system finds segments in an audio track that have similar perceptual properties and groups these segments. This article provides an in-depth analysis on the statistic properties of similar acoustic segments identified by the diarization system in a predefined document set and the theoretical fitness of this approach to discern one document class from another. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ISM.2011.79 | ISM |
Keywords | Field | DocType |
multimedia retrieval,audio material,audio data,training data,audio domain,useful audio concept,unsupervized audio segementation system,audio concept,audio concept detection,audio track,speaker diarization,abstract audio concept,engines,image classification,speaker recognition,mathematical model,audio signal processing,acoustics,clustering algorithms | Statistic,Information retrieval,Computer science,Audio mining,Speaker recognition,Speaker diarisation,Audio signal processing,Contextual image classification,Cluster analysis,Multimedia,Perception | Conference |
ISBN | Citations | PageRank |
978-1-4577-2015-4 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Robert Mertens | 1 | 6 | 2.90 |
| Po-Sen Huang | 2 | 926 | 44.01 |
| Luke Gottlieb | 3 | 61 | 5.79 |
| Gerald Friedland | 4 | 1127 | 96.23 |
| Ajay Divakaran | 5 | 598 | 50.83 |