back icon
close icon

Capture phrases in quotes for more specific queries (e.g. "rocket ship" or "Fred Lynn")

Article  April 1, 2013

On the Applicability of Speaker Diarization to Audio Indexing of Non-Speech and Mixed Non-Speech/Speech Video

SRI Authors Ajay Divakaran

Citation

COPY
Mertens, R., Huang, P., Gottlieb, L., Friedland, G., Divakaran, A., & Hasegawa-Johnson, M. (2012). On the Applicability of Speaker Diarization to Audio Indexing of Non-Speech and Mixed Non-Speech/Speech Video Soundtracks. International Journal of Multimedia Data Engineering and Management (IJMDEM), 3(3), 1-19. doi:10.4018/jmdem.2012070101

Abstract

A video’s soundtrack is usually highly correlated to its content. Hence, audio-based techniques have recently emerged as a means for video concept detection complementary to visual analysis. Most state-of-the-art approaches rely on manual definition of predefined sound concepts such as “engine sounds,” “outdoor/indoor sounds.” These approaches come with three major drawbacks: manual definitions do not scale as they are highly domain-dependent, manual definitions are highly subjective with respect to annotators and a large part of the audio content is omitted since the predefined concepts are usually found only in a fraction of the soundtrack. This paper explores how unsupervised audio segmentation systems like speaker diarization can be adapted to automatically identify low-level sound concepts similar to annotator defined concepts and how these concepts can be used for audio indexing. Speaker diarization systems are designed to answer the question “who spoke when?” by finding segments in an audio stream that exhibit similar properties in feature space, i.e., sound similar. Using a diarization system, all the content of an audio file is analyzed and similar sounds are clustered. 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. It also discusses how diarization can be tuned in order to better reflect the acoustic properties of general sounds as opposed to speech and introduces a proof-of-concept system for multimedia event classification working with diarization-based indexing.

How can we help?

Once you hit send…

We’ll match your inquiry to the person who can best help you. Expect a response within 48 hours.

Our Privacy Policy