In this paper, we introduce a mathematical method to extract similarities between paintings and musical tracks. Our approach is based on the digitalization of both paintings and musical tracks by means of finite expansions in terms of orthogonal basis functions (with both Fourier and wavelet bases). The best fit between a specific painting and a sample of musical tracks from a given composer is achieved via an $L^2$ projection upon a finite-dimensional subspace. Several examples are provided for the analysis of a collection of works of art by the Italian artist Marcello Morandini. Finally, we have developed an original applet that implements the process above and which can be freely downloaded from the site https://github.com/pgerva/playing-paintings.git
翻译:在本文中,我们引入了一种数学方法来提取绘画与音乐曲目之间的相似之处,我们的方法是,通过在正弦基础功能(包括Fourier和波子底)方面有限扩展,将绘画和音乐曲目数字化,在特定绘画与某个作曲家的音乐曲目样本之间最合适的做法是对一个有限维度的亚空间进行2美元预测,为分析意大利艺术家Marcello Morandini的艺术作品集提供了几个例子。最后,我们开发了一个执行上述进程的原始小苹果,可以从网站https://github.com/pgerva/playing-paintings.git自由下载。