Auditory Scene Analysis Principles Improve Image Reconstruction Abilities of Novice Vision-to-Audio Sensory Substitution Users

2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)(2021)

引用 1|浏览1
暂无评分
摘要
Sensory substitution devices (SSDs) such as the ‘vOICe’ preserve visual information in sound by turning visual height, brightness, and laterality into auditory pitch, volume, and panning/time respectively. However, users have difficulty identifying or tracking multiple simultaneously presented tones – a skill necessary to discriminate the upper and lower edges of object shapes. We explore how these deficits can be addressed by using image-sonifications inspired by auditory scene analysis (ASA). Here, sighted subjects (N=25) of varying musical experience listened to, and then reconstructed, complex shapes consisting of simultaneously presented upper and lower lines. Complex shapes were sonified using the vOICe, with either the upper and lower lines varying only in pitch (i.e. the vOICe’s ‘unaltered’ default settings), or with one line degraded to alter its auditory timbre or volume. Results found that overall performance increased with subjects’ years of prior musical experience. ANOVAs revealed that both sonification style and musical experience significantly affected performance, but with no interaction effect between them. Compared to the vOICe’s ‘unaltered’ pitch-height mapping, subjects had significantly better image-reconstruction abilities when the lower line was altered via timbre or volume-modulation. By contrast, altering the upper line only helped users identify the unaltered lower line. In conclusion, adding ASA principles to vision-to-audio SSDs boosts subjects’ image-reconstruction abilities, even if this also reduces total task-relevant information. Future SSDs should seek to exploit these findings to enhance both novice user abilities and the use of SSDs as visual rehabilitation tools.
更多
查看译文
关键词
auditory scene analysis principles,image reconstruction abilities,vision-to-audio
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要