Autoencoding sensory substitution
Open accessAbstract
Tens of millions of people live blind, and their number is ever increasing. Visual-to-auditory sensory substitution (SS) encompasses a family of cheap, generic solutions to assist the visually impaired by conveying visual information through sound. The required SS training is lengthy: months of effort is necessary to reach a practical level of adaptation. There are two reasons for the tedious training process: the elongated substituting audio signal, and the disregard for the compressive characteristics of the human hearing system. To overcome these obstacles, we developed a novel class of SS methods, by training deep recurrent autoencoders for image-to-sound conversion. We successfully trained deep learning models on different datasets to execute visual-to-auditory stimulus conversion. By constraining the visual space, we demonstrated the viability of shortened substituting audio signals, while proposing mechanisms, such as the integration of computational hearing models, to optimally convey visual features in the substituting stimulus as perceptually discernible auditory components. We tested our approach in two separate cases. In the first experiment, the author went blindfolded for 5 days, while performing SS training on hand posture discrimination. The second experiment assessed the accuracy of reaching movements towards objects on a table. In both test cases, above-chance-level accuracy was attained after a few hours of training. Our novel SS architecture broadens the horizon of rehabilitation methods engineered for the visually impaired. Further improvements on the proposed model shall yield hastened rehabilitation of the blind and a wider adaptation of SS devices as a consequence.
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AEV2A makes it difficult for a participant to understand the conversion scheme because there is no consistent correlation between changes in input image and resulting sound.
“However, this method makes it difficult for a participant to understand the conversion scheme because there is no consistent correlation between changes in the input image and the resulting sound obtained from AEV2A.”
From Deep Learning-Based Optimization of Visual–Auditory Sensory Substitution (2023) · cites this paper as [23]
Section 2.5.1 (page unavailable) says implicit conversion acts as a black box and that explicit methods are easier to grasp, supporting the difficulty of understanding AEV2A’s conversion scheme. It does not support the asserted absence of a consistent image–sound correlation: Section 4.1 reports measured correlations between visual and audio features, including r = .97 and r = .99. (LLM, conf=0.87)
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“cross-modal connections and the receiving modality, we can specifically tailor the soundscapes so they are easily mapped onto the visual cortex, reducing the amount of necessary cross-modal plasticity [27]. The “Holy Grail” [28] of V2A SS is the synaesthetic experience: visual perception consistently and involuntarily…”
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“AI for all” is a matter of social justice
Buccella, A. (2022) · AI and Ethics · doi:10.1007/s43681-022-00222-z · Cited by 34 · see citing works
Molecular Sonification for Molecule to Music Information Transfer
Mahjour, B. A., Bench, J., Zhang, R., Frazier, J., & Cernak, T. (2022) · Digital Discovery · doi:10.1039/d3dd00008g · Cited by 16 · see citing works
Deep Learning-Based Optimization of Visual–Auditory Sensory Substitution
Kim, M., Park, Y., Moon, K., & Jeong, C. Y. (2023) · IEEE Access · doi:10.1109/access.2023.3243641 · Cited by 12 · see citing works
“However, in visual–auditory substitution, various methods that can code visual information [15], [16], [17], [18], [23], [24], [25] are available.”
MethodPhenomenology: What’s AI got to do with it?
Buccella, A., & Springle, A. A. (2022) · Phenomenology and the Cognitive Sciences · doi:10.1007/s11097-022-09833-7 · Cited by 9 · see citing works
Analysis and Validation of Cross-Modal Generative Adversarial Network for Sensory Substitution
Kim, M., Park, Y., Moon, K., & Jeong, C. Y. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18126216 · Cited by 8 · see citing works
Butorovа, A. S., & Sergeev, A. (2025) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-025-01596-x · Cited by 3 · see citing works
Port, A., Kim, C., & Patel, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.13291 · Cited by 4 · see citing works
- Authors
- Tóth, V., & Parkkonen, L.
- Corresponding author
- Tóth, Viktor
- Venue
- Unpublished
- Publisher
- Unpublished
- Published
- 2019
- ISSN
- 2331-8422 (ISSN-L)
- OpenAlex
- W2951582018
- Cited by
- 7
- References
- 162
- Citations by year
- 2025: 1 · 2023: 1 · 2022: 3 · 2021: 1 · 2020: 1
- Type
- posted content
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- arXiv (Cornell University) (submittedVersion)
- Aaltodoc (Aalto University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Multisensory perception and integration
- Keywords
- sensory substitution, recurrent autoencoder, blind rehabilitation, auditory display, reaching movements, audio compression
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