Skin Pattern Sonification Using NMF-based Visual Feature Extraction and Learning-based PMSon

This paper describes the use of sonification to represent the scanned image data of skin pattern of the human body. Skin Patterns have different characteristics and visual features depending on the positions and conditions of the skin on the human body. The visual features are extracted and analyzed for sonification in order to broaden the dimensions of data representation and to explore the diversity of sound in each human body. Non-negative matrix factorization (NMF) is employed to parameterize skin pattern images, and the represented visual parameters are connected to sound parameters through support vector regression (SVR). We compare the sound results with the data from the skin pattern analysis to examine how much each individual skin patterns are effectively mapped to create accurate sonification results. Thus, the use of sonification in this research suggests a novel approach to parameter mapping sonification by designing personal sonic instruments that use the entire human body as