Abstract: Computer-implemented method for determining a personalized head-related transfer function, the method comprising: receiving a training dataset comprising one or more training images of ears of one or more first users, a training input vector indicative of one or more first directions relative to the first user's head, and one or more values of the personalized head-related directional function related to the first users and the first directions; training an artificial neural network on the training dataset; receiving an inference dataset comprising an inference image of a second user's ear or a pair of inference images of the second user's ears, and an inference input vector indicative of a second direction relative to the second user's head; and processing the inference dataset by the artificial neural network to predict one or more personalized values of the directional function, the values related to the second user and the second direction.

Abstract: A computer-implemented method for determining a frequency response of an audio system, the method comprising: training a Generative Adversarial Network, GAN, discriminator on a first training dataset comprising measured frequency responses of reference audio systems to a test signal and an evaluator scoring of the audio system to predict a predicted scoring for the reference audio systems, training a GAN generator on a second training dataset comprising evaluator scorings to predict a predicted frequency response for the reference audio systems, wherein training the GAN generator comprises processing the predicted frequency response by the trained GAN discriminator to predict a predicted scoring; and processing a production dataset comprising an input scoring of a production audio system by the trained GAN generator to predict a frequency response of the production audio system.

Abstract: It is described a computer implemented method for generating a personalized sound signal transfer function, the method comprising: determining first data, wherein the first data represents a first sound signal transfer function, wherein the first sound signal transfer function is associated with a user's ear and with a first sound signal direction relative to the user's ear; determining, based on the first data, second data, wherein the second data represents a second sound signal transfer function, wherein the second sound signal transfer function is associated with the user's ear and with a second sound signal direction relative to the user's ear.

Abstract: A method for playback of an audio signal at an individual seat-based sound (ISS) system in a coherent listening environment having a plurality of ISS systems and a plurality of listening modes. Each ISS system has a single transducer. A listening mode and a set of playback preferences are selected. A crosstalk cancellation algorithm for the ISS system of interest is generated using impulse response measurements of the audio signal, taken only in an acoustical domain, of a transfer function between the audio signal directly after the single transducer in the ISS system of interest and the audio signal at the head of the listener in the ISS system of interest. The crosstalk cancellation algorithm for the selected listening mode is applied to the audio signal, and the audio signal is played back at the single transducer of the ISS system of interest.

Abstract: A computer-implemented method for determining a frequency response of an audio system, the method comprising: training a Generative Adversarial Network, GAN, discriminator on a first training dataset comprising measured frequency responses of reference audio systems to a test signal and an evaluator scoring of the audio system to predict a predicted scoring for the reference audio systems, training a GAN generator on a second training dataset comprising evaluator scorings to predict a predicted frequency response for the reference audio systems, wherein training the GAN generator comprises processing the predicted frequency response by the trained GAN discriminator to predict a predicted scoring; and processing a production dataset comprising an input scoring of a production audio system by the trained GAN generator to predict a frequency response of the production audio system.

Abstract: A method for playback of an audio signal at an individual seat-based sound (ISS) system in a coherent listening environment having a plurality of ISS systems and a plurality of listening modes. Each ISS system has a single transducer. A listening mode and a set of playback preferences are selected. A crosstalk cancellation algorithm for the ISS system of interest is generated using impulse response measurements of the audio signal, taken only in an acoustical domain, of a transfer function between the audio signal directly after the single transducer in the ISS system of interest and the audio signal at the head of the listener in the ISS system of interest. The crosstalk cancellation algorithm for the selected listening mode is applied to the audio signal, and the audio signal is played back at the single transducer of the ISS system of interest.

Abstract: An audio system for controlling and modifying an audio signal in a room having selectively reconfigurable architecture is disclosed. The audio system comprises a plurality of speakers mounted to an architecture configuration having one or more movable room-defining components and a control system in electronic communication with the plurality of speakers and the one or more movable room-defining components. The control system is configured to adjust one or more tuning parameters of one or more of the plurality of speakers based a position of the one or more movable room-defining components.

Abstract: A computer-implemented method for determining a scoring indicative of a sound quality of an audio system, the method comprising: sending at least one test signal to at least one reference audio system; measuring a first frequency response of the reference audio system to the test signal; receiving at least one reference scoring indicative of a sound quality of the reference audio system; supplying a training dataset comprising the first frequency response and the reference scoring to an artificial neural network; training the artificial neural network on the training dataset to predict a scoring for the audio system; sending at least one test signal to at least one production audio system; measuring a second frequency response of the production audio system; and processing an input dataset comprising the second frequency response by the artificial neural network to predict a scoring indicative of a sound quality of the production audio system.

Abstract: Systems and methods are provided for personalized three-dimensional audio. In one embodiment, a sound calibration system comprises a headrest having a first speaker, a second speaker, and one or more sensors, the headrest configured to engage a head of a user, and a controller with computer readable instructions stored on non-transitory memory. The instructions, when executed, cause the controller to create customized spatial audio by utilizing a head-related impulse response (HRIR) that is modified based on an input audio signal, the location of the audio source and receiver, and the head position of the user. The resulting audio output is generated by applying the HRIR and interaural crosstalk cancellation filters to frequencies above a threshold frequency.

Abstract: A headphone system includes a calibration microphone for performing a calibration routine with a user. The calibration microphone receives a stimulus signal emitted by the headphone system and generates a response signal indicating variations in the stimulus signal that arise due to physiological attributes of the user. Based on the stimulus signal and the response signal, the calibration engine generates response data. The calibration engine processes the response data based on a headphone transfer function (HPTF) associated with the headphone system in order to create an inverse filter that can reduce or remove acoustic variations caused by the headphone system. The calibration engine generates a personalized HRTF for the user based on the response data and the inverse filter. The personalized HRTF can be used to implement highly accurate 3D audio and is thereby well-suited for applications to immersive audio and audio-visual entertainment.

Abstract: Computer-implemented method for determining a personalized head-related transfer function, the method comprising: receiving a training dataset comprising one or more training images of ears of one or more first users, a training input vector indicative of one or more first directions relative to the first user's head, and one or more values of the personalized head-related directional function related to the first users and the first directions; training an artificial neural network on the training dataset; receiving an inference dataset comprising an inference image of a second user's ear or a pair of inference images of the second user's ears, and an inference input vector indicative of a second direction relative to the second user's head; and processing the inference dataset by the artificial neural network to predict one or more personalized values of the directional function, the values related to the second user and the second direction.

Abstract: There is described a computer implemented method for generating a personalized sound signal transfer function, the method comprising: receiving, by a sound receiving means, a sound signal at or in a user's ear; determining, based on the received sound signal, first data, wherein the first data represents a first sound signal transfer function associated with the user's ear; determining, based on the first data, second data, wherein the second data represents a second sound signal transfer function associated with the user's ear.

Abstract: It is described a computer implemented method for generating a personalized sound signal transfer function, the method comprising: determining first data, wherein the first data represents a first sound signal transfer function, wherein the first sound signal transfer function is associated with a user's ear and with a first sound signal direction relative to the user's ear; determining, based on the first data, second data, wherein the second data represents a second sound signal transfer function, wherein the second sound signal transfer function is associated with the user's ear and with a second sound signal direction relative to the user's ear.

Abstract: A headphone system includes a calibration microphone for performing a calibration routine with a user. The calibration microphone receives a stimulus signal emitted by the headphone system and generates a response signal indicating variations in the stimulus signal that arise due to physiological attributes of the user. Based on the stimulus signal and the response signal, the calibration engine generates response data. The calibration engine processes the response data based on a headphone transfer function (HPTF) associated with the headphone system in order to create an inverse filter that can reduce or remove acoustic variations caused by the headphone system. The calibration engine generates a personalized HRTF for the user based on the response data and the inverse filter. The personalized HRTF can be used to implement highly accurate 3D audio and is thereby well-suited for applications to immersive audio and audio-visual entertainment.

Abstract: In various embodiments, a method includes receiving a first audio channel signal; causing a first driver and a second driver of an audio output device to output a first frequency range of the first audio channel signal with a first power output; causing the first driver to output a second frequency range of the first audio channel signal with a second power output; and causing the second driver to output the second frequency range of the first audio channel signal with a third power output that is higher than the second power output, wherein the second frequency range is higher than the first frequency range.

Abstract: Techniques for calibrating listening devices are disclosed herein. The techniques include emitting a predetermined audio signal using an outward-facing transducer located on a first portion of a head-mounted device worn by the user, receiving the predetermined audio signal at a microphone located on a second portion of the head-mounted device, the second portion being different from the first portion, determining a transfer function for the user based on the received predetermined audio signal, and applying the transfer function to audio signals transmitted to the user.

Abstract: A headphone system includes a calibration microphone for performing a calibration routine with a user. The calibration microphone receives a stimulus signal emitted by the headphone system and generates a response signal indicating variations in the stimulus signal that arise due to physiological attributes of the user. Based on the stimulus signal and the response signal, the calibration engine generates response data. The calibration engine processes the response data based on a headphone transfer function (HPTF) associated with the headphone system in order to create an inverse filter that can reduce or remove acoustic variations caused by the headphone system. The calibration engine generates a personalized HRTF for the user based on the response data and the inverse filter. The personalized HRTF can be used to implement highly accurate 3D audio and is thereby well-suited for applications to immersive audio and audio-visual entertainment.

Abstract: Techniques for calibrating listening devices are disclosed herein. The techniques include emitting a predetermined audio signal using an outward-facing transducer located on a first portion of a head-mounted device worn by the user, receiving the predetermined audio signal at a microphone located on a second portion of the head-mounted device, the second portion being different from the first portion, determining a transfer function for the user based on the received predetermined audio signal, and applying the transfer function to audio signals transmitted to the user.

Abstract: Systems and methods to calibrate listening devices are disclosed herein. In some embodiments, a method to calibrate earphones includes determining a Head Related Transfer Functions (HRTF) corresponding to different parts of a user's anatomy (e.g., one or both of a listener's pinnae). The resulting HRTFs are combined to form a composite HRTF. In some embodiments, a first and a second HRTF are respectively determined for a first and second part of the user's anatomy. A composite HRTF of the user is generated by combining portions of the first and second HRTFs.

Abstract: Systems and methods of calibrating listening devices are disclosed herein. In one embodiment, a method of calibrating a listening device (e.g., a headset) includes determining head related transfer functions (HRTF) corresponding to different parts of the user's anatomy. The resulting HRTFs are combined to form a composite HRTF.