Assessing the tonal qualities of an acoustic musical instrument is a challenge that has long been pursued by researchers in musical acoustics. This is a topic of particular interest when it comes to discussing the case of violins. Historical violins are often believed to owe a great deal of their celebrated timbral qualities to the choice and the make of their finishings, particularly the ground coat and the varnish. The impact of such finishings on the instrument's tonal qualities, however, is not so well understood. In this paper we investigate the impact of the finishing process on the instrument's timbre through a joint analysis of the characteristics of the materials involved for this process (ground coat and varnish) and audio features extracted from the sound produced by such violins at various stages of their finishing. Some of the results are compared with those found in the literature for validation purposes. The characterization of the impact of ground coat and varnish has been conducted during the finishing process of a new violin.
The widespread diffusion of portable devices capable of capturing high-quality multimedia data, together with the rapid proliferation of media sharing platforms, has determined an incredible growth of user-generated content available online. Since it is hard to strictly regulate this trend, illegal diffusion of copyrighted material is often likely to occur. This is the case of audio bootlegs, i.e., concerts illegally recorded and redistributed by fans. In this paper, we propose a bootleg detector, with the aim of disambiguating between: i) bootlegs unofficially recorded; ii) live concerts officially published; iii) studio recordings from officially released albums. The proposed method is based on audio feature analysis and machine learning techniques. We exploit a deep learning paradigm to extract highly characterizing features from audio excerpts, and a supervised classifier for detection. The method is validated against a dataset of nearly 500 songs, and results are compared to a state-of-the-art detector. The conducted experiments confirm the capability of deep learning techniques to outperform classic feature extraction approaches
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