Abstract: The disclosure describes one or more embodiments of an impulse response system that generates accurate and realistic synthetic impulse responses. For example, given an acoustic impulse response, the impulse response system can generate one or more synthetic impulse responses that modify the direct-to-reverberant ratio (DRR) of the acoustic impulse response. As another example, the impulse response system can generate one or more synthetic impulse responses that modify the reverberation time (e.g., T60) of the acoustic impulse response. Further, utilizing the synthetic impulse responses, the impulse response system can perform a variety of functions to improve a digital audio recording or acoustic measurement or prediction model.

Abstract: The disclosure describes one or more embodiments of an impulse response system that generates accurate and realistic synthetic impulse responses. For example, given an acoustic impulse response, the impulse response system can generate one or more synthetic impulse responses that modify the direct-to-reverberant ratio (DRR) of the acoustic impulse response. As another example, the impulse response system can generate one or more synthetic impulse responses that modify the reverberation time (e.g., T60) of the acoustic impulse response. Further, utilizing the synthetic impulse responses, the impulse response system can perform a variety of functions to improve a digital audio recording or acoustic measurement or prediction model.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for rendering scene-aware audio based on acoustic properties of a user environment. For example, the disclosed system can use neural networks to analyze an audio recording to predict environment equalizations and reverberation decay times of the user environment without using a captured impulse response of the user environment. Additionally, the disclosed system can use the predicted reverberation decay times with an audio simulation of the user environment to optimize material parameters for the user environment. The disclosed system can then generate an audio sample that includes scene-aware acoustic properties based on the predicted environment equalizations, material parameters, and an environment geometry of the user environment.

Abstract: Methods and systems for obtaining a probabilistic diagnosis based on medical images of a patient are disclosed. In exemplary embodiments, such methods include the steps of scanning some or all of a patient to obtain a medical image; evaluating the medical image for one or more designated key features; assigning discrete values to the one or more designated key features to form a patient scan key feature pattern; and transmitting the values of the one or more designated key features to a processor programmed to match the patient scan key feature pattern to one or more known disease-specific key feature patterns to create a probabilistic diagnosis and transmit the probabilistic diagnosis to a user. Associated systems include a medical imaging device that is capable of producing a medical image of some or all of a patient and a processor programmed to create a probabilistic diagnosis.

Abstract: Methods and systems for obtaining a probabilistic diagnosis based on medical images of a patient are disclosed. In exemplary embodiments, such methods include the steps of scanning some or all of a patient to obtain a medical image; evaluating the medical image for one or more designated key features; assigning discrete values to the one or more designated key features to form a patient scan key feature pattern; and transmitting the values of the one or more designated key features to a processor programmed to match the patient scan key feature pattern to one or more known disease-specific key feature patterns to create a probabilistic diagnosis and transmit the probabilistic diagnosis to a user. Associated systems include a medical imaging device that is capable of producing a medical image of some or all of a patient and a processor programmed to create a probabilistic diagnosis.