By Topic

Using the Fisher kernel method for Web audio classification

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
P. J. Moreno ; Cambridge Res. Lab., Compaq Comput. Corp., Cambridge, MA, USA ; R. Rifkin

As the multimedia content of the Web increases techniques to automatically classify this content become more important. We present a system to classify audio files collected from the Web. The system classifies any audio file as belonging to one of three categories: speech, music and other. To classify the audio files, we use the technique of Fisher kernels. The technique as proposed by Jaakkola (1998) assumes a probabilistic generative model for the data, in our case a Gaussian mixture model. Then a discriminative classifier uses the GMM as an intermediate step to produce appropriate feature vectors. Support vector machines are our choice of discriminative classifier. We present classification results on a collection of more than 173 hours of Web audio randomly collected. We believe our results represent one of the first realistic studies of audio classification performance on found data. Our final system yielded a classification rate of 81.8%

Published in:

Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on  (Volume:6 )

Date of Conference: