Evaluation of short range depth sonifications for visual-to-auditory sensory substitution

April 11, 2023 Β· Declared Dead Β· πŸ› IEEE Transactions on Human-Machine Systems

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Authors Louis Commère, Jean Rouat arXiv ID 2304.05462 Category cs.HC: Human-Computer Interaction Citations 9 Venue IEEE Transactions on Human-Machine Systems Last Checked 4 months ago
Abstract
Visual to auditory sensory substitution devices convert visual information into sound and can provide valuable assistance for blind people. Recent iterations of these devices rely on depth sensors. Rules for converting depth into sound (i.e. the sonifications) are often designed arbitrarily, with no strong evidence for choosing one over another. The purpose of this work is to compare and understand the effectiveness of five depth sonifications in order to assist the design process of future visual to auditory systems for blind people which rely on depth sensors. The frequency, amplitude and reverberation of the sound as well as the repetition rate of short high-pitched sounds and the signal-to-noise ratio of a mixture between pure sound and noise are studied. We conducted positioning experiments with twenty-eight sighted blindfolded participants. Stage 1 incorporates learning phases followed by depth estimation tasks. Stage 2 adds the additional challenge of azimuth estimation to the first stage's protocol. Stage 3 tests learning retention by incorporating a 10-minute break before re-testing depth estimation. The best depth estimates in stage 1 were obtained with the sound frequency and the repetition rate of beeps. In stage 2, the beep repetition rate yielded the best depth estimation and no significant difference was observed for the azimuth estimation. Results of stage 3 showed that the beep repetition rate was the easiest sonification to memorize. Based on statistical analysis of the results, we discuss the effectiveness of each sonification and compare with other studies that encode depth into sounds. Finally we provide recommendations for the design of depth encoding.
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