Abstract: In various embodiments, a collaboration endpoint transmits an ultrasonic signal into an area based on a frequency sweep. The collaboration endpoint receives a reflected signal that comprises the ultrasonic signal reflected off an object located in the area. The collaboration endpoint detects, based on the reflected signal, the object as being a potential user. The collaboration endpoint determines, based on the reflected signal, a distance from the collaboration endpoint to the potential user. The collaboration endpoint initiates, based on the distance from the collaboration endpoint to the potential user, a wake-up sequence of the collaboration endpoint to exit a standby mode.

Abstract: In various embodiments, a collaboration endpoint transmits an ultrasonic signal into an area based on a frequency sweep. The collaboration endpoint receives a reflected signal that comprises the ultrasonic signal reflected off an object located in the area. The collaboration endpoint detects, based on the reflected signal, the object as being a potential user. The collaboration endpoint determines, based on the reflected signal, a distance from the collaboration endpoint to the potential user. The collaboration endpoint initiates, based on the distance from the collaboration endpoint to the potential user, a wake-up sequence of the collaboration endpoint to exit a standby mode.

Abstract: In various embodiments, a collaboration endpoint transmits an ultrasonic signal into an area. The collaboration endpoint receives a reflected signal that comprises the ultrasonic signal reflected off an object located in the area. The collaboration endpoint detects, based on the reflected signal, the object as being a potential user. The collaboration endpoint determines, based on the reflected signal, a distance from the collaboration endpoint to the potential user. The collaboration endpoint initiates, based on the distance from the collaboration endpoint to the potential user, a wake-up sequence of the collaboration endpoint to exit a standby mode.

Abstract: This disclosure relates to solutions for eliminating undesired audio artifacts, such as background noises, on an audio channel. A process for implementing the technology can include receiving a set of audio segments, analyzing the segments using a first ML model to identify a first probability of unwanted background noises in the segments, and if the first probability exceeds a threshold, analyzing the segments using a second ML model to determine a second probability that the one or more background features exist in the segments. In some aspects, the process can include attenuating audio artifacts in the segments, if the second probability exceeds a second threshold. In some implementations, dynamic time stretching and shrinking can be applied to the noise attenuation. Systems and machine-readable media are also provided.

Abstract: This disclosure relates to solutions for eliminating undesired audio artifacts, such as background noises, on an audio channel. A process for implementing the technology can include receiving a set of audio segments, analyzing the segments using a first ML model to identify a first probability of unwanted background noises in the segments, and if the first probability exceeds a threshold, analyzing the segments using a second ML model to determine a second probability that the one or more background features exist in the segments. In some aspects, the process can include attenuating audio artifacts in the segments, if the second probability exceeds a second threshold. In some implementations, dynamic time stretching and shrinking can be applied to the noise attenuation. Systems and machine-readable media are also provided.

Abstract: This disclosure relates to solutions for eliminating undesired audio artifacts, such as background noises, on an audio channel. A process for implementing the technology can include receiving a set of audio segments, analyzing the segments using a first ML model to identify a first probability of unwanted background noises in the segments, and if the first probability exceeds a threshold, analyzing the segments using a second ML model to determine a second probability that the one or more background features exist in the segments. In some aspects, the process can include attenuating audio artifacts in the segments, if the second probability exceeds a second threshold. In some implementations, dynamic time stretching and shrinking can be applied to the noise attenuation. Systems and machine-readable media are also provided.