Abstract: Various embodiments of an apparatus, method(s), system(s) and computer program product(s) described herein are directed to a Visualization Engine. The Visualization Engine receives audio data associated with a user account accessing a virtual meeting via a communications environment client software application. The Visualization Engine detects presence of a pre-selected type(s) of audio event(s) in the received audio data. The Visualization Engine generates a visualization representative of at least one attribute of the detected audio event(s). During playback of the audio data in the virtual meeting, the Visualization Engine renders the visualization within the communications environment client software application of the user account.

Abstract: One example method includes presenting, by a client device, a view of a virtual conference hosted by a virtual conference provider, the virtual conference including a plurality of participants, the client device associated with a participant of the plurality of participants, the view including a plurality of groupings of participants within a virtual conference area, each grouping associated with a different meeting or sub-meeting of the virtual conference; assign a location within the virtual conference area to the participant; receiving, at the client device from the conference provider, one or more audio streams associated with one or more audio sources within the plurality of groupings, the one or more audio streams provided by one or more remote client devices; determining a first location within the virtual conference area of a first audio source of the one or more audio sources; generating a plurality of spatialized audio streams based on the first location of the first audio source, the location of th

Abstract: One example method for music collaboration using virtual conferencing includes receiving, by a client device, audio streams associated with a plurality of musicians in a virtual conference, each musician assigned to a virtual position within a virtual space established by the virtual conference, the client device associated with a participant in the virtual conference, the participant having a participant virtual position within the virtual space; determining relative virtual positions of each musician of at least a subset of the plurality of musicians in the virtual conference with respect to the participant virtual position; generating a plurality of spatialized audio streams based on the relative virtual positions of the respective musicians and the respective audio streams; and outputting the spatialized audio streams.

Abstract: Example methods and systems provide automatic audio equalization for online conferences. A target, ideal, audio frequency response for use in online conferencing audio can be designed and preset for reference, and the equalizer can operate in real time to reach the target frequency response for any input. The equalizer can selectively apply per-band equalization to the audio signal as needed to adjust an energy value for each of multiple frequency bands to produce an output audio signal that that can be, as an example, routed through meeting servers or other infrastructure to other conference participants. The equalization can compensate for conditions local to a speaker that would otherwise adversely affect audio quality.

Abstract: Techniques for modification of far end audio signals are provided. In an example method, a computing device establishes a conference meeting including a first user equipment (UE) among a plurality of UE. The computing device then receives far end audio (FEA) data from the first UE. The computing device generates first modified FEA comprising determining a time-domain sum-of-absolute-difference (SAD) from the FEA data and determines a difference between the first modified FEA and a predefined value. Responsive to the difference exceeding a predefined threshold, the computing device determines a second modified FEA comprising determining a frequency-domain SAD from the FEA data and stores the second modified FEA in a buffer. The second modified is then output from the buffer.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for generating echo recordings. The system receives, by an autoencoder, an audio signal representation that represents an audio signal and a target echo embedding that comprises information about a target room. The autoencoder comprises an encoder and a decoder. The system generates, by the encoder, a content embedding and an estimated echo embedding. The system generates, by the decoder, an echo recording representation based on the content embedding and the target echo embedding.

Abstract: For online audio/video conferencing applications deployed in an open office environment, using shared conference devices, it can be advantageous to define an acoustic fence. A non-participant audio received from outside the acoustic fence can be considered noise and filtered out before transmission of an audio signal to a far end recipient. Three suppression stages are used to filter the non-participant audio. The first suppression stage uses beamformers for suppression. The second suppression stage is mask-based, and the third suppression stage is reference-based. The three suppression stages filter out non-participant audio signals, having a wide range of frequencies.

Abstract: One example method includes receiving, by a machine learning (“ML”) model of a conference client application, audio signals received from a microphone of a client device, the client device connected to a virtual meeting via the conference client application, the virtual meeting hosted by a virtual conference provider; determining, by the ML model, a plurality of candidate reactions associated with the audio signals, the ML comprising a plurality of convolutional neural network (“CNN”) layers and at least one fully connected layer; selecting a reaction from the plurality of candidate reactions; and transmitting the reaction to the virtual conference provider.

Abstract: An adaptive screen encoding method comprising: using a computer, creating and storing, in computer memory, a plurality of conditions for use by a server configured to determine which of picture coding type to select; detecting a current picture by a sender for a content type including textual content, graphical content, and natural image content; determining a percentage of static macroblocks corresponding to the current picture; selecting the picture coding type based on the content type, the plurality of conditions, and the percentage of static macroblocks, wherein the method is performed by one or more special-purpose computing devices.

Abstract: An apparatus and/or method discloses a video conference with enhanced audio quality using high-fidelity audio sharing (“HAS”). In one embodiment, a network connection between a first user equipment (“UE”) and a second UE is established via a communication network for providing an interactive real-time meeting. After sending a first calibration audio signal from the first UE to the second UE, a second calibration audio signal is retuned from the second UE to the first UE according to the first calibration audio signal. Upon identifying a far end audio (“FEA”) delay based on the first calibration audio signal and the second calibration audio signal, a first mixed audio data containing the first shared audio data and first FEA data is fetched from an audio buffer. The first FEA data is subsequently removed or extracted from the mixed audio data in response to the FEA delay.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for generating echo recordings. The system receives, by an autoencoder, an audio signal representation that represents an audio signal and a target echo embedding that comprises information about a target room. The autoencoder comprises an encoder and a decoder. The system generates, by the encoder, a content embedding and an estimated echo embedding. The system generates, by the decoder, an echo recording representation based on the content embedding and the target echo embedding.

Abstract: One example method for providing spatial audio in virtual conference includes receiving, at a client device from a conference provider, an audio stream associated with an audio source, the audio stream provided by a remote client device, the client device and the remote client device participating in a virtual conference hosted by the conference provider, the client device associated with a user; determining a location of the audio source in the virtual conference with respect to the user's head; generating a plurality of spatialized audio streams based on the locations of the audio source and the audio stream; and outputting the spatialized audio streams.

Abstract: Online audio and video conference applications can utilize a noise removal module to eliminate unwanted audio from a participant’s speech. A noise removal module can rely on differentiating between human speech versus other audio to filter out noise. However, in some conference environments, participant and non-participant human speech can be present. Artificial intelligence models can be trained to detect both noise and non-participant audio, based on a variety of factors. The models can label captured audio and various noise removal modules can filter noise based on the output of the models.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media relate to a method for audio super resolution. The system receives an audio signal. When the sampling rate of the audio signal is below a sampling rate threshold or the frequency range of the audio signal is below a frequency range threshold, the audio signal is input to an audio super resolution model comprising a machine learning model. The audio signal is processed by the audio super resolution model to generate a synthetic audio signal with a wider frequency range than the frequency range of the audio signal.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for generating echo recordings. The system receives, by an autoencoder, an audio signal representation that represents an audio signal and a target echo embedding that comprises information about a target room. The autoencoder comprises an encoder and a decoder. The system generates, by the encoder, a content embedding and an estimated echo embedding. The system generates, by the decoder, an echo recording representation based on the content embedding and the target echo embedding.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media relate to a method for acoustic echo cancellation. The system inputs one or more signal representations into an acoustic echo cancellation network comprising one or more network blocks to generate a mask, each network block comprising one or more convolutional blocks, each convolutional block comprising one or more neural networks. The system combines the mask and a near-end audio signal representation to generate an echo-cancelled audio signal representation. The system generates an echo-cancelled audio signal based on the echo-cancelled audio signal representation.

Abstract: An adaptive screen encoding method comprising: using a computer, creating and storing, in computer memory, a plurality of conditions for use by a server configured to determine which of picture coding type to select; detecting a current picture by a sender for a content type including textual content, graphical content, and natural image content; determining a percentage of static macroblocks corresponding to the current picture; selecting the picture coding type based on the content type, the plurality of conditions, and the percentage of static macroblocks, wherein the method is performed by one or more special-purpose computing devices.

Abstract: An adaptive screen encoding method comprising: using a computer, creating and storing, in computer memory, a plurality of conditions for use by a server configured to determine which of picture coding type to select; detecting a current picture by a sender for a content type including textual content, graphical content, and natural image content; determining a percentage of static macroblocks corresponding to the current picture; selecting the picture coding type based on the content type, the plurality of conditions, and the percentage of static macroblocks, wherein the method is performed by one or more special-purpose computing devices.

Abstract: An apparatus and/or method discloses a video conference with enhanced audio quality using high-fidelity audio sharing (“HAS”). In one embodiment, a network connection between a first user equipment (“UE”) and a second UE is established via a communication network for providing an interactive real-time meeting. After sending a first calibration audio signal from the first UE to the second UE, a second calibration audio signal is retuned from the second UE to the first UE according to the first calibration audio signal. Upon identifying a far end audio (“FEA”) delay based on the first calibration audio signal and the second calibration audio signal, a first mixed audio data containing the first shared audio data and first FEA data is fetched from an audio buffer. The first FEA data is subsequently removed or extracted from the mixed audio data in response to the FEA delay.

Abstract: An apparatus and/or method discloses a video conference with enhanced audio quality using high-fidelity audio sharing (“HAS”). In one embodiment, a network connection between a first user equipment (“UE”) and a second UE is established via a communication network for providing an interactive real-time meeting. After sending a first calibration audio signal from the first UE to the second UE, a second calibration audio signal is retuned from the second UE to the first UE according to the first calibration audio signal. Upon identifying a far end audio (“FEA”) delay based on the first calibration audio signal and the second calibration audio signal, a first mixed audio data containing the first shared audio data and first FEA data is fetched from an audio buffer. The first FEA data is subsequently removed or extracted from the mixed audio data in response to the FEA delay.