See-Through-Sound: Transforming Images into Sonic Representations to Help the Blind

See-Through-Sound: Transforming Images into Sonic Representations to Help the Blind

J. Tomás Henriques, Sofia Cavaco, Nuno Correia
Copyright: © 2014 |Pages: 19
DOI: 10.4018/jitr.2014010105
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Abstract

See-Through-Sound is a research project that is aimed at creating an innovative solution for mapping visual information into the auditory realm, enabling a spatial environment and its unique features to be described as organized sonic events. Of particular interest to us has been the creation of a tool for people with vision disabilities to help them perceive and recognize objects and features of their environment through sonic representations of light, color and shapes. Applications for sighted people have also been explored as sonification methods for monitoring changes in color within a broad range of scenarios, as well as advanced motion detection. The benefits and promise of this technology are far reaching; it goes beyond mere medical and scientific applications. Ultimately the main goal of this research project is the attempt to systematize and create a universal vocabulary of sonic events that map visual data into auditory data, both for man and machine use.
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State-Of-The-Art

Other tools that convert image into sound have been developed and reported in the literature. The following is a list of a selected group of relevant projects in this field that contain interesting approaches to the image-to-sound conversion and deserve being mentioned. Further work does exist and new sonification projects to help the blind keep surfacing and gaining interest both from the scientific and entrepreneurial worlds.

The vOICe, a pioneering work in this area, focuses mainly on scanning shapes but it also provides color information(Auvray, Hanneton, & O'Regan, 2007; Bach-y-Rita & Kercel, 2003; Meijer, 1992).The vOICe uses gray level image analysis to convert color images and video into a 16-grey-scale picture, with a resolution of 176 × 64 pixels. Each of the 64 columns is listened to for about 15 ms, making one complete scan approximately 1-second long. The sounds produced vary as a function of the position and brightness of the pixels in the column of the image that is under the scan at each moment. Each pixel corresponds to a different frequency. The higher the position of the pixel, the higher the frequency. Brightness of pixels in the image is coded in terms of the amplitude of the sinusoidal waveform that is emitted for that pixel. Silence corresponds to the color black and a loud sound means white. Anything in-between is a shade of grey. This software is quite evolved in terms of its quality and the reach of its distribution. Applications for PC, iPhone and Android are available.

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