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.
Visual-auditory sensory substitution systems can aid blind people in traveling to various places and recognizing their own environments without help from others. Although several such systems have been developed, they are either not widely used or are limited to laboratory-scale research. Among various factors that hinder the widespread use of these systems, one of the most important issues to consider is the optimization of the algorithms for sensory substitution. This study is the first attempt at exploring the possibility of using deep learning for the objective quantification of sensory substitution. To this end, we used generative adversarial networks to investigate the possibility of optimizing the vOICe algorithm, a representative visual-auditory sensory substitution method, by controlling the parameters of the method for converting an image to sound. Furthermore, we explored the effect of the parameters on the conversion scheme for the vOICe system and performed frequency-range and frequency-mappingfunction experiments. The process of sensory substitution in humans was modeled to use generative models to assess the extent of visual perception from the substituted sensory signals. We verified the humanbased experimental results against the modeling results. The results suggested that deep learning could be used for evaluating the efficiency of algorithms for visual-auditory sensory substitutions without laborintensive human behavioral experiments. The introduction of deep learning for optimizing the visualauditory conversion method is expected to facilitate studies on various aspects of sensory substitution, such as generalization and estimation of algorithm efficiency.
This paper focuses on the recovery of the characters in Louise Erdrich’s Love Medicine through remembering and their own solidarity in the community. In this paper, I will argue that Louise Erdrich presents a positive vision to the characters in Love Medicine through remembering. Albertine and Lipsha change and heal with historical awareness through remembering June, and Lulu and Marie are healed through affective solidarity in the community. While remembering June, Albertine and Lipsha recall the history of the Chippewa tribe they had forgotten and understand their ancestors' historical trauma. The change of the two main characters presents a vision of hope that the second generation of Native Americans who have not suffered direct historical trauma, will not forget their painful history and continue to live with pride as Native Americans. Lulu and Marie have the trauma of not achieving complete love with Nector due to their love adversaries. However, Lulu and Marie do not give up their lives because of the wounds of love, but rather, they acknowledge and reconcile each other by converting the relationship between hatred and long-cherished desire into a relationship of empathy and love in Nector's death. The dynamic relationship between Lulu and Marie-Nector reveals that they become family to each other within the tribal community. Erdrich's story of the Chippewa tribe’s trauma and wounds in Love Medicine may not be apparent on the surface, but in the process of storytelling an individual's life, it makes the reader deeply empathize with their trauma and wounds.
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