Horror film genre typing and scene labeling via audio analysis
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Video classification using spatial-temporal features and PCA
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Explicit modelling of session variability for speaker verification
Computer Speech and Language
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EURASIP Journal on Applied Signal Processing
Comparison of scoring methods used in speaker recognition with Joint Factor Analysis
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
A Study of Interspeaker Variability in Speaker Verification
IEEE Transactions on Audio, Speech, and Language Processing
Automatic Video Classification: A Survey of the Literature
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Abstract: Audio pattern classification represents a particular statistical classification task and includes, for example, speaker recognition, language recognition, emotion recognition, speech recognition and, recently, video genre classification. The feature being used in all these tasks is generally based on a short-term cepstral representation. The cepstral vectors contain at the same time useful information and nuisance variability, which are difficult to separate in this domain. Recently, in the context of GMM-based recognizers, a novel approach using a Factor Analysis (FA) paradigm has been proposed for decomposing the target model into a useful information component and a session variability component. This approach is called Joint Factor Analysis (JFA), since it models jointly the nuisance variability and the useful information, using the FA statistical method. The JFA approach has even been combined with Support Vector Machines, known for their discriminative power. In this article, we successfully apply this paradigm to three automatic audio processing applications: speaker verification, language recognition and video genre classification. This is done by applying the same process and using the same free software toolkit. We will show that this approach allows for a relative error reduction of over 50% in all the aforementioned audio processing tasks.