Abstract: Systems and methods herein address reference frame selection in video streaming applications using one or more processing units to decode a frame of an encoded video stream that uses an inter-frame depicting an object and an intra-frame depicting the object, the intra-frame being included in a set of intra-frames based at least in part on at least one attribute of the object as depicted in the intra-frame being different from the at least one attribute of the object as depicted in other intra-frames of the set of intra-frames.

Abstract: Systems and methods relate to facial video encoding and reconstruction, particularly in ultra-low bandwidth settings. In embodiments, a video conferencing or other streaming application uses automatically tracked feature cropping information. A bounding shape size—used to identify the cropped region—varies and is dynamically determined to maintain a proportion for feature reconstruction, such as resizing in the event of a zoom-in on a face (or other feature of interest) or a zoom-out. The tracking scheme may be used to smooth sudden movements, including lateral ones, to generate more natural transitions between frames. Tracking and cropping information (e.g., size and position of the cropped region) may be embedded within an encoded bitstream as supplemental enhancement information (“SEI”), for eventual decoding by a receiver and for compositing a decoded face at a proper location in the applicable stream.

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

Abstract: Apparatuses, systems, and techniques are presented to track objects represented in images or video data. In at least one embodiment, motion of one or more objects within a plurality of digital images is determined based, at least in part, on flow information corresponding to the one or more objects.

Abstract: In various examples, audio alerts of emergency response vehicles may be detected and classified using audio captured by microphones of an autonomous or semi-autonomous machine in order to identify travel directions, locations, and/or types of emergency response vehicles in the environment. For example, a plurality of microphone arrays may be disposed on an autonomous or semi-autonomous machine and used to generate audio signals corresponding to sounds in the environment. These audio signals may be processed to determine a location and/or direction of travel of an emergency response vehicle (e.g., using triangulation). Additionally, to identify siren types—and thus emergency response vehicle types corresponding thereto—the audio signals may be used to generate representations of a frequency spectrum that may be processed using a deep neural network (DNN) that outputs probabilities of alert types being represented by the audio data.

Abstract: A virtual reality (VR) audio rendering system and method of using HRTF functions to quickly capture new positional cues to pre-computed audio frames responsive to changes in user position relative to sound systems. In a client-server VR system, when a user position change is detected, the client determines an appropriate HRTF based on the new position and convolves them with a set of audio frames that have been generated by the server based on a prior position, resulting in modified frames for rendering. Meanwhile, the client propagates the new position to the server to generate subsequent audio frames for the corrected position. As HRTF convolution is computationally inexpensive, the latency between user position change and the resultant sound change as perceived by the user can be significantly reduced. As a result, an immersive VR experience of the user can be preserved.

Abstract: A virtual reality (VR) audio rendering system and method of using HRTF functions to quickly capture new positional cues to pre-computed audio frames responsive to changes in user position relative to sound systems. In a client-server VR system, when a user position change is detected, the client determines an appropriate HRTF based on the new position and convolves them with a set of audio frames that have been generated by the server based on a prior position, resulting in modified frames for rendering. Meanwhile, the client propagates the new position to the server to generate subsequent audio frames for the corrected position. As HRTF convolution is computationally inexpensive, the latency between user position change and the resultant sound change as perceived by the user can be significantly reduced. As a result, an immersive VR experience of the user can be preserved.

Abstract: A system and method for providing media-level redundancy in voice-over Internet Protocol (VoIP) systems are disclosed. A central controller receives VoIP calls including a media transmission and call setup data and a standby allocation module included in the central controller transmits the VoIP calls to an active card and a standby card. The active card processes the media transmission of the VoIP call using an array of signal processing modules. The VoIP call setup data is also transmitted to a standby card which stores the call setup data and data identifying the active card processing the VoIP transmission in a profile database. When an active card malfunctions, the central controller transmits an activation signal to the standby card which then loads the contents of the profile database into an array of signal processing modules on the standby card to process the VoIP calls previously processed by the malfunctioning active card.

Abstract: A method of combining a channel quality estimate for the radio channel based on direct measurement of carrier and interferer energies, and a channel quality estimate for the radio channel based on channel decoder metrics, to obtain a final channel quality estimate in terms of carrier-to-interference (C/I) ratio for the radio channel, which is more reliable, consistent and accurate than that obtained with the individual methods. After computing a direct channel quality estimate and a decoder metric-based channel quality estimate for the radio channel, confidence levels, P(direct), P (metric), are assigned to the two estimates. P(direct) is multiplied with the direct channel quality estimate and P(metric) is multiplied with the decoder metric channel quality estimate. The respective products are added to obtain the final channel quality estimate in terms of the carrier-to-interference (C/I) ratio for the radio channel.

Abstract: A method of combining a channel quality estimate for the radio channel based on direct measurement of carrier and interferer energies, and a channel quality estimate for the radio channel based on channel decoder metrics, to obtain a final channel quality estimate in terms of carrier-to-interference (C/I) ratio for the radio channel, which is more reliable, consistent and accurate than that obtained with the individual methods. After computing a direct channel quality estimate and a decoder metric-based channel quality estimate for the radio channel, confidence levels, P(direct), P (metric), are assigned to the two estimates. P(direct) is multiplied with the direct channel quality estimate and P(metric) is multiplied with the decoder metric channel quality estimate. The respective products are added to obtain the final channel quality estimate in terms of the carrier-to-interference (C/I) ratio for the radio channel.