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2. Hearing Iterative and Recursive Behavior

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   Adams, Joel C; Anderson, Hayworth (March 2024, ACM)

   Abstract topics such as recursion are challenging for many computer science students to understand. In this experience report, we explore function sonification—the addition of sound to a function to communicate information about the function’s behavior in real-time as it runs—as a pedagogical approach for improving students’ understanding of recursion. We present several example iterative and recursive function sonifications, plus spectrograms that illustrate their different sonic behaviors. We also present experimental evidence that using these sonifications significantly improved the understanding of recursion for students who used them, compared to students who used silent (i.e., traditional) versions of the same functions. Based on these experiences, we believe sonification has under-appreciated potential for teaching abstract computing topics. 

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3. Noise schemas aid hearing in noise

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   Human hearing is robust to noise, but the basis of this robustness is poorly understood. Several lines of evidence are consistent with the idea that the auditory system adapts to sound components that are stable over time, potentially achieving noise robustness by suppressing noise-like signals. Yet background noise often provides behaviorally relevant information about the environment and thus seems unlikely to be completely discarded by the auditory system. Motivated by this observation, we explored whether noise robustness might instead be mediated by internal models of noise structure that could facilitate the separation of background noise from other sounds. We found that detection, recognition, and localization in real-world background noise were better for foreground sounds positioned later in a noise excerpt, with performance improving over the initial second of exposure to a noise. These results are consistent with both adaptation-based and model-based accounts (adaptation increases over time and online noise estimation should benefit from acquiring more samples). However, performance was also robust to interruptions in the background noise and was enhanced for intermittently recurring backgrounds, neither of which would be expected from known forms of adaptation. Additionally, the performance benefit observed for foreground sounds occurring later within a noise excerpt was reduced for recurring noises, suggesting that a noise representation is built up during exposure to a new background noise and then maintained in memory. These findings suggest that noise robustness is supported by internal models—“noise schemas”—that are rapidly estimated, stored over time, and used to estimate other concurrent sounds. 

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4. Open Speech Platform: Democratizing Hearing Aid Research

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   Staples, Anne E.; Mikel-Stites, Maxwell R. (January 2021, 25th International Congress of Theoretical and Applied Mechanics)

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