Author:
Avi Bleiweiss
Affiliation:
Intel Corporation, United States
Keyword(s):
MFCC, Vector Quantization, Bag of Words, Support Vector Machines, Ranked Information Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Design and Implementation of Signal Processing Systems
;
Multimedia
;
Multimedia Databases, Indexing, Recognition and Retrieval
;
Multimedia Systems and Applications
;
Semantic Analysis of Multimedia Data
;
Telecommunications
Abstract:
The sensory experience of watching a movie, links input from both sight and hearing modalities. Yet traditionally, the motion picture rating system largely relies on the visual content of the film, to make its informed decisions to parents. The current rating process is fairly elaborate. It requires a group of parents to attend a full screening, manually prepare and submit their opinions, and vote out the appropriate audience age for viewing. Rather, our work explores the feasibility of classifying age attendance of a movie automatically, resorting to solely analyzing the movie auditory data. Our high performance software records the audio content of the shorter movie trailer, and builds a labeled training set of original and artificially distorted clips. We use a bag of audio words to effectively represent the film sound track, and demonstrate robust and closely correlated classification accuracy, in exploiting boolean discrimination and ranked retrieval methods.