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International audience; 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 for the optimization of SVM: kernel target alignment and kernel class separability. We demonstrate how these two measures, when fully expressed, can build efficient and simple methods, easily applicable to multiclass problems and iteratively computable with minimal memory requirements. An extensive experimental study is conducted both on artificial and real-world datasets to compare the proposed methods to state-of-the-art feature selection algorithms. The results demonstrate the relevance of the proposed methods both in terms of performance and computational cost.

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 improve the Audio-Print system, with evaluations both on a free corpus, made publicly available, and a real-world corpus of broadcast radio streams.