NDLI: Improved audio features for large-scale multimedia event detection

Content Provider	IEEE Xplore Digital Library
Author	Metze, F. Rawat, S. Yipei Wang
Copyright Year	2014
Description	Author affiliation: Language Technol. Inst., Carnegie Mellon Univ. Pittsburgh, Pittsburgh, PA, USA (Metze, F.; Rawat, S.; Yipei Wang)
Abstract	In this paper, we present recent experiments on using Artificial Neural Networks (ANNs), a new “delayed” approach to speech vs. non-speech segmentation, and extraction of large-scale pooling feature (LSPF) for detecting “events” within consumer videos, using the audio channel only. A “event” is defined to be a sequence of observations in a video, that can be directly observed or inferred. Ground truth is given by a semantic description of the event, and by a number of example videos. We describe and compare several algorithmic approaches, and report results on the 2013 TRECVID Multimedia Event Detection (MED) task, using arguably the largest such research set currently available. The presented system achieved the best results in most audio-only conditions. While the overall finding is that MFCC features perform best, we find that ANN as well as LSP features provide complementary information at various levels of temporal resolution. This paper provides analysis of both low-level and high-level features, investigating their relative contributions to overall system performance.
Starting Page	1
Ending Page	6
File Size	75332
Page Count	6
File Format	PDF
ISBN	9781479947614
DOI	10.1109/ICME.2014.6890234
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Videos Mel frequency cepstral coefficient Feature extraction Speech Semantics Context multimedia retrieval acoustic event detection computational acoustic scene analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Large-scale audio feature extraction and SVM for acoustic scene classification

Audio-based affect detection in web videos

Audio analysis for surveillance applications

Gradient-based acoustic features for speech recognition

Effective preprocessing of speech and acoustic features extraction for spoken language identification

Tree-based context clustering using speech recognition features for acoustic model training of speech synthesis

Parallel audio features extraction for sound indexing and retrieval systems

A study of acoustic features for depression detection

An Empirical Study of Feature Extraction Methods for Audio Classification

Improved audio features for large-scale multimedia event detection

Similar Documents

Large-scale audio feature extraction and SVM for acoustic scene classification

Audio-based affect detection in web videos

Audio analysis for surveillance applications

Gradient-based acoustic features for speech recognition

Effective preprocessing of speech and acoustic features extraction for spoken language identification

Tree-based context clustering using speech recognition features for acoustic model training of speech synthesis

Parallel audio features extraction for sound indexing and retrieval systems

A study of acoustic features for depression detection

An Empirical Study of Feature Extraction Methods for Audio Classification

Improved audio features for large-scale multimedia event detection