Graduate School and Research Center in Digital Sciences

Audio-based video genre identification

Rouvier, Mickael; Oger, Stanislas; Linarès, Georges; Matrouf, Driss; Merialdo, Bernard; Li, Yingbo

IEEE/ACM Transactions on Audio, Speech, and Language Processing, Volume 23, N°6, June 2015

This paper presents investigations about the automatic identification of video genre by audio channel analysis. Genre refers to editorial styles such commercials, movies, sports... We propose and evaluate some methods based on both low and high level descriptors, in cepstral or time domains, but also by analyzing the global structure of the document and the linguistic contents. Then, the proposed features are combined and their complementarity is evaluated. On a database composed of single-stories web-videos, the best audio-only based system performs 9% of Classification Error Rate (CER). Finally, we evaluate the complementarity of the proposed audio features and video features that are classically used for Video Genre Identification (VGI). Results demonstrate the complementarity of the modalities for genre recognition, the final audio-video system reaching 6% CER.

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Title:Audio-based video genre identification
Keywords:Automatic classification, linguistic feature extraction, video genre classification
Department:Data Science
Eurecom ref:4589
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Bibtex: @article{EURECOM+4589, doi = {}, year = {2015}, month = {01}, title = {{A}udio-based video genre identification}, author = {{R}ouvier, {M}ickael and {O}ger, {S}tanislas and {L}inar{\`e}s, {G}eorges and {M}atrouf, {D}riss and {M}erialdo, {B}ernard and {L}i, {Y}ingbo }, journal = {{IEEE}/{ACM} {T}ransactions on {A}udio, {S}peech, and {L}anguage {P}rocessing, {V}olume 23, {N}°6, {J}une 2015}, url = {} }
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