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We compare in this paper diverse hierarchical and multi-class approaches for the speech/music segmentation task, based on Support Vector Machines, combined with a median filter post-processing. We show the effciency of kernel tuning through the novel Kernel Target Alignment criterion. Quantitative results provide an F-measure of 96.9%, that represents an error reduction of about 50% compared to the...
This paper presents the latest improvements on AudioPrint: the IRCAM audio fingerprint system. Cosine filters are introduced in the short-term spectral analysis, in order to compensate the effect of pitch shifting, and a simple solution is proposed for the determination of the frame positions, robust to audio degradations, with nearly no additional cost. We then show that both contributions significantly...
Feature selection has been an important issue in recent decades to determine the most relevant features according to a given classification problem. Numerous methods have emerged that take into account support vector machines (SVMs) in the selection process. Such approaches are powerful but often complex and costly. In this paper, we propose new feature selection methods based on two criteria designed...
In this paper, we present for the first time the fingerprint IRCAM system for audio identification in streams. The baseline system relies on a double-nested Short Time Fourier Transform. The first STFT computes the energies of a filter-bank, that are then modelled over 2 s, using a second STFT. We then present recent improvements of our system: first the inclusion of perceptual scales for amplitude...
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