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1. Learning acoustic responses from experiments: A multiscale-informed transfer learning approach

   https://doi.org/10.1121/10.0010187  

   Trinh, Van Hai; Guilleminot, Johann; Perrot, Camille; Vu, Viet Dung (April 2022, The Journal of the Acoustical Society of America)

   A methodology to learn acoustical responses based on limited experimental datasets is presented. From a methodological standpoint, the approach involves a multiscale-informed encoder used to cast the learning task in a finite-dimensional setting. A neural network model mapping parameters of interest to the latent variables is then constructed and calibrated using transfer learning and knowledge gained from the multiscale surrogate. The relevance of the approach is assessed by considering the prediction of the sound absorption coefficient for randomly-packed rigid spherical beads of equal diameter. A two-microphone method is used in this context to measure the absorption coefficient on a set of configurations with various monodisperse particle diameters and sample thicknesses, and a hybrid numerical approach relying on the Johnson-Champoux-Allard-Pride-Lafarge model is deployed as the multiscale-based predictor. It is shown that the strategy allows for the relationship between the micro-/structural parameters and the experimental acoustic response to be well approximated, even if a small physical dataset (comprised of ten samples) is used for training. The methodology, therefore, enables the identification and validation of acoustical models under constraints related to data limitation and parametric dependence. It also paves the way for an efficient exploration of the parameter space for acoustical materials design. 

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2. Walkable auralizations for experiential learning in an immersive classroom

   https://doi.org/10.1121/10.0012985  

   Chabot, Samuel; Braasch, Jonas (August 2022, The Journal of the Acoustical Society of America)

   This paper proposes an experiential method for learning acoustics and consequences of room design through the rapid creation of audio-visual congruent walkable auralizations. An efficient method produces auralizations of acoustical landmarks using a two-dimensional ray-tracing algorithm and publicly available floor plans for a 128-channel wave-field synthesis system. Late reverberation parameters are calculated using additional volumetric data. Congruent visuals are produced using a web-based interface accessible via personal devices, which automatically formats for and transmits to the immersive display. Massive user-contributed online databases are harnessed through application programming interfaces, such as those offered by the Google Maps Platform, to provide near-instant access to innumerable locations. The approach allows the rapid sonic recreation of historical concert venues with adequate sound sources. Listeners can walk through these recreations over an extended user area (12 m × 10 m). 

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3. Estimating atmospheric wind speeds from Gemini planet imager AO telemetry

   https://doi.org/10.1117/12.3020607  

   Du, Zhenxi; Perera, Saavidra; Levinstein, Daniel; Konopacky, Quinn M; Madurowicz, Alex; Macintosh, Bruce; Poyneer, Lisa; Wilson, Richard W; Farley, Ollie_J D (August 2024, SPIE)

   Schmidt, Dirk; Vernet, Elise; Jackson, Kathryn J (Ed.)

   The Earth’s atmosphere is comprised of turbulent layers that result in speckled and blurry images from ground- based visible and infrared observations. Adaptive Optics (AO) systems are employed to measure the perturbed wavefront with a wavefront sensor (WFS) and correct for these distortions with a deformable mirror. Therefore, understanding and characterising the atmosphere is crucial for the design and functionality of AO systems. One parameter for characterizing the atmosphere is the atmospheric coherence time, which is a function of the effec- tive wind velocity of the atmosphere. This parameter dictates how fast the AO system needs to correct for the atmosphere. If not fast enough, phenomena such as the wind butterfly effect can occur, hindering high-contrast coronographic imaging. This effect is a result of fast, strong, high-altitude turbulent layers. This paper presents two methods for estimating the effective wind velocity, using pseudo-open loop WFS slopes. The first method uses a spatial-temporal covariance map and the second uses the power spectral density of the defocus term. We show both simulated results and preliminary results from the Gemini Planet Imager AO telemetry. 

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4. Predicting room acoustical parameters from running signals using a precedence effect model and deep neural networks

   Tyler, J.; Si, M.; Braasch, J. (January 2022, Proceedings of the 24th International Congress on Acoustics (ICA))

   Choi, Jee Woong; Cho Wan-Ho (Ed.)

   J. Tyler, M. Si, J. Braasch (2022) Predicting room acoustical parameters from running signals using a precedence effect model and deep neural networks, In: Proceedings of the 24th International Congress on Acoustics (ICA), October 24–28, Gyeongju, Korea, Paper No. ABS-0627, p. 283–290, https://www.ica2022korea.org/data/Proceedings_A11.pdf https://www.ica2022korea.org/sub_proceedings.php 

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5. Nonlinear techniques for few-mode wavefront sensors

   https://doi.org/10.1364/AO.537925  

   Lin, Jonathan; Fitzgerald, Michael_P (November 2024, Applied Optics)

   We present several nonlinear wavefront sensing techniques for few-mode sensors, all of which are empirically calibrated and agnostic to the choice of wavefront sensor. The first class of techniques involves a straightforward extension of the linear phase retrieval scheme to higher order; the resulting Taylor polynomial can then be solved using the method of successive approximations, though we discuss alternate methods such as homotopy continuation. In the second class of techniques, a model of the WFS intensity response is created using radial basis function interpolation. We consider both forward models, which map phase to intensity and can be solved with nonlinear least-squares methods such as the Levenberg-Marquardt algorithm, as well as backwards models, which directly map intensity to phase and do not require a solver. We provide demonstrations for both types of techniques in simulation using a quad-cell sensor and a photonic lantern wavefront sensor as examples. Next, we demonstrate how the nonlinearity of an arbitrary sensor may be studied using the method of numerical continuation, and apply this technique both to the quad-cell sensor and a photonic lantern sensor. Finally, we briefly consider the extension of nonlinear techniques to polychromatic sensors. 

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