Nvidia Corporation

Abhijit Patait

Ambrish Dantrey

Niranjan Avadhanam

Atousa Torabi

Ram Ganapathi

Revanth Reddy Nalla

Anshul Jain

Ming-Yu Liu

Arun Mohanray Mallya

Mihir Nyayate

Angshuman Ghosh

Aurobinda Maharana

Utkarsh Patankar

Apparatuses, systems, and techniques are presented to reduce noise in audio. In at least one embodiment, a sequence of neural networks is used to remove foreground and background noise from audio including a primary audio signal.

Audio noise removal using one or more neural networks

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.

Emergency response vehicle detection for autonomous driving applications

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.

Frame selection for streaming applications

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&#x2014;and thus emergency response vehicle types corresponding thereto&#x2014;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. The locations, direction of travel, and/or siren type may allow an ego-vehicle or ego-machine to identify an emergency response vehicle and to make planning and/or control decisions in response.

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.

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Pune, India

Abhijit Patait