Audio Cartography: Visual Encoding of Acoustic Parameters

Our sonic environment is the matter of subject in multiple domains which developed individual means of its description. As a result, it lacks an established visual language through which knowledge can be connected and insights shared. We provide a visual communication framework for the systematic and coherent documentation of sound in large-scale environments. This consists of visual encodings and mappings of acoustic parameters into distinct graphic variables that present plausible solutions for the visualization of sound. These candidate encodings are assembled into an application-independent, multifunctional, and extensible design guide. We apply the guidelines and show example maps that acts as a basis for the exploration of audio cartography

About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN

Enlightened Romanticism: Mary Gartside’s colour theory in the age of Moses Harris, Goethe and George Field

The aim of this paper is to evaluate the work of Mary Gartside, a British female colour theorist, active in London between 1781 and 1808. She published three books between 1805 and 1808. In chronological and intellectual terms Gartside can cautiously be regarded an exemplary link between Moses Harris, who published a short but important theory of colour in the second half of the eighteenth century, and J.W. von Goethe’s highly influential Zur Farbenlehre, published in Germany in 1810. Gartside’s colour theory was published privately under the disguise of a traditional water colouring manual, illustrated with stunning abstract colour blots (see example above). Until well into the twentieth century, she remained the only woman known to have published a theory of colour. In contrast to Goethe and other colour theorists in the late 18th and early 19th century Gartside was less inclined to follow the anti-Newtonian attitudes of the Romantic movement

Multi-task CNN Model for Attribute Prediction

This paper proposes a joint multi-task learning algorithm to better predict attributes in images using deep convolutional neural networks (CNN). We consider learning binary semantic attributes through a multi-task CNN model, where each CNN will predict one binary attribute. The multi-task learning allows CNN models to simultaneously share visual knowledge among different attribute categories. Each CNN will generate attribute-specific feature representations, and then we apply multi-task learning on the features to predict their attributes. In our multi-task framework, we propose a method to decompose the overall model's parameters into a latent task matrix and combination matrix. Furthermore, under-sampled classifiers can leverage shared statistics from other classifiers to improve their performance. Natural grouping of attributes is applied such that attributes in the same group are encouraged to share more knowledge. Meanwhile, attributes in different groups will generally compete with each other, and consequently share less knowledge. We show the effectiveness of our method on two popular attribute datasets.Comment: 11 pages, 3 figures, ieee transaction pape