Autoencoding sensory substitution

Tóth, Viktor

doi:10.13140/rg.2.2.10576.87048

Cited by 3 publications

(1 citation statement)

References 138 publications

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“…Recently, an implicit method for visual-auditory sensory substitution technology, called the autoencoded V2A (AEV2A) model [35], has been proposed. This approach converts visual information of an image into an auditory signal by applying a deep learning algorithm without predefined rules.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution

Kim

Park

Moon

et al. 2021

IJERPH

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Visual-auditory sensory substitution has demonstrated great potential to help visually impaired and blind groups to recognize objects and to perform basic navigational tasks. However, the high latency between visual information acquisition and auditory transduction may contribute to the lack of the successful adoption of such aid technologies in the blind community; thus far, substitution methods have remained only laboratory-scale research or pilot demonstrations. This high latency for data conversion leads to challenges in perceiving fast-moving objects or rapid environmental changes. To reduce this latency, prior analysis of auditory sensitivity is necessary. However, existing auditory sensitivity analyses are subjective because they were conducted using human behavioral analysis. Therefore, in this study, we propose a cross-modal generative adversarial network-based evaluation method to find an optimal auditory sensitivity to reduce transmission latency in visual-auditory sensory substitution, which is related to the perception of visual information. We further conducted a human-based assessment to evaluate the effectiveness of the proposed model-based analysis in human behavioral experiments. We conducted experiments with three participant groups, including sighted users (SU), congenitally blind (CB) and late-blind (LB) individuals. Experimental results from the proposed model showed that the temporal length of the auditory signal for sensory substitution could be reduced by 50%. This result indicates the possibility of improving the performance of the conventional vOICe method by up to two times. We confirmed that our experimental results are consistent with human assessment through behavioral experiments. Analyzing auditory sensitivity with deep learning models has the potential to improve the efficiency of sensory substitution.

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Section: Related Workmentioning

confidence: 99%

Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution

Kim

Park

Moon

et al. 2021

IJERPH

View full text Add to dashboard Cite

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Phenomenology: What’s AI got to do with it?

Buccella

Springle

2022

Phenom Cogn Sci

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“AI for all” is a matter of social justice

Buccella

2022

AI Ethics

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Artificial intelligence (AI) is a radically transformative technology (or system of technologies) that created new existential possibilities and new standards of well-being in human societies. In this article, I argue that to properly understand the increasingly important role AI plays in our society, we must consider its impacts on social justice. For this reason, I propose to conceptualize AI’s transformative role and its socio-political implications through the lens of the theory of social justice known as the Capability Approach. According to the approach, a just society must put its members in a position to acquire and exercise a series of basic capabilities and provide them with the necessary means for these capabilities to be actively realized. Because AI is re-shaping the very definition of some of these basic capabilities, I conclude that AI itself should be considered among the conditions of possession and realization of the capabilities it transforms. In other words, access to AI—in the many forms this access can take—is necessary for social justice.

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Autoencoding sensory substitution

Cited by 3 publications

References 138 publications

Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution

Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution

Phenomenology: What’s AI got to do with it?

“AI for all” is a matter of social justice

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