ROBUST AUDIO FINGERPRINTING FOR NOISE-LIKE SIGNALS Name of author First Address Line

Tangible Acoustic Interfaces (TAIs) are interaction devices that are able to localize the interaction point on a solid sur-face. Their advantages over traditional interaction devices (touch screens, touch pads, etc.) is in the fact that actual acoustic (vibrational signals) are acquired by contact sen-sors. This opens the way to interaction classification and recognition. With this application in mind, this paper approaches the problem of classifying the interaction type from the acquired sounds. We focus on continuous interaction noise, which we classify through a “fingerprinting ” approach: features are extracted from the acquired signals and matched against pre-computed features. More sophisticated solutions can be de-vised for the problem of the classification of noise-like sounds but our approach has the advantage of being computation-ally simple and can be profitably implemented in real-time. 1