Abstract:
A method includes accessing, by at least one processing device, an audible signal including at least one in-ear microphone audible signal and at least one external microphone audible signal and at least one noise signal; training a generative network to generate an enhanced external microphone signal from an in-ear microphone signal based on the at least one in-ear microphone audible signal and the at least one external microphone audible signal; and outputting the generative network.
Abstract:
In accordance with an example embodiment of the present invention, a device comprising one or more porous graphene layers, the or each graphene porous layer comprising a multiplicity of pores. The device may form at least part of a flexible and/or stretchable, and or transparent electronic device.
Abstract:
Methods, apparatuses, and computer program products are provided in order to provide 3D audio playback using audio head-mounted devices. The apparatuses may be configured to receive at least one of position and orientation of a first head-mounted device in relation to a first user device, wherein the at least one of the position and orientation received is used to train a model using machine learning. At least one signal quality parameter may be determined based on input data and a filter pair may be determined corresponding with a direction to which a spatial audio signal is rendered based at least in part on the at least one signal quality parameter and the model so as to control spatial audio signal reproduction to take effect a change in the at least one of the position and orientation of the first head-mounted device during rendering of the spatial audio signal.
Abstract:
Methods, apparatuses, and computer program products are provided in order to provide 3D audio playback using audio head-mounted devices. The apparatuses may be configured to receive at least one of position and orientation of a first head-mounted device in relation to a first user device, wherein the at least one of the position and orientation received is used to train a model using machine learning. At least one signal quality parameter may be determined based on input data and a filter pair may be determined corresponding with a direction to which a spatial audio signal is rendered based at least in part on the at least one signal quality parameter and the model so as to control spatial audio signal reproduction to take effect a change in the at least one of the position and orientation of the first head-mounted device during rendering of the spatial audio signal.
Abstract:
Methods, apparatuses, and computer program products are provided in order to provide 3D audio playback using audio head-mounted devices. The apparatuses may be configured to receive at least one of position and orientation of a first head-mounted device in relation to a first user device, wherein the at least one of the position and orientation received is used to train a model using machine learning. At least one signal quality parameter may be determined based on input data and a filter pair may be determined corresponding with a direction to which a spatial audio signal is rendered based at least in part on the at least one signal quality parameter and the model so as to control spatial audio signal reproduction to take effect a change in the at least one of the position and orientation of the first head-mounted device during rendering of the spatial audio signal.
Abstract:
In accordance with an example embodiment of the present invention, a device comprising one or more porous graphene layers, the or each graphene porous layer comprising a multiplicity of pores. The device may form at least part of a flexible and/or stretchable, and or transparent electronic device.
Abstract:
Methods, apparatuses, and computer program products are provided in order to provide 3D audio playback using audio head-mounted devices. The apparatuses may be configured to receive at least one of position and orientation of a first head-mounted device in relation to a first user device, wherein the at least one of the position and orientation received is used to train a model using machine learning. At least one signal quality parameter may be determined based on input data and a filter pair may be determined corresponding with a direction to which a spatial audio signal is rendered based at least in part on the at least one signal quality parameter and the model so as to control spatial audio signal reproduction to take effect a change in the at least one of the position and orientation of the first head-mounted device during rendering of the spatial audio signal.
Abstract:
A method includes accessing, by at least one processing device, an audible signal including at least one in-ear microphone audible signal and at least one external microphone audible signal and at least one noise signal; training a generative network to generate an enhanced external microphone signal from an in-ear microphone signal based on the at least one in-ear microphone audible signal and the at least one external microphone audible signal; and outputting the generative network.