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: 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: Systems and methods for enhancing group sound during a networked conference are provided. A server computer establishes a networked conference among a plurality of computer devices. The server computer receives one or more group sound indicators from one or more computer devices of the plurality of computer devices within a selected time interval. In response to determining that the total number of the one or more computer devices corresponding to the one or more group sound indicators is equal to or greater than a selected threshold, the server computer transmits to the plurality of computer devices a control signal identifying a group sound corresponding to the one or more group sound indicators. The server computer causes the plurality of computer devices to reproduce the group sound identified in the control signal.

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: 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: Enhancing group sound during a networked conference is disclosed. In an embodiment, a method includes generating an audio signal at a user equipment (UE), detecting a group sound in the audio signal, generating a group sound indicator that identifies the detected group sound, and transmitting the group sound indicator to a network server. The method also includes receiving, from the network server, a control signal that identifies a selected group sound, and reproducing, at the UE, the selected group sound identified by the control signal.

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: Artificial intelligence models are trained with training datasets of known input/output values. Test datasets are used to evaluate the trained artificial intelligence models. Context mismatch between the training dataset and the test dataset can slow down the development of artificial intelligence models. The described systems and methods can identify context similar datasets for the purpose of training and testing an artificial intelligence model. In one embodiment, a context similarity detector can ingest and combine a training dataset and a test dataset and generate a context similarity score for the two. If the score is above a threshold, the datasets are similar, and the relevant artificial intelligence model can be trained with one and tested with the other.

Abstract: Methods and systems provide users of a communication platform with intelligent, real-time noise suppression for audio signals broadcasted in a communication session. The system receives an input audio signal from an audio capture device; processes the input audio signal to provide a second version of the audio signal with noise suppression based on DSP techniques; transmits the second version of the audio signal to a communication platform for real-time streaming; classifies, via a machine learning algorithm, whether the second version of the audio signal contains noise beyond a noise threshold; based on a classification that the second version of the audio signal contains noise beyond the noise threshold, processes the second version of the audio signal to provide a third version of the audio signal with noise suppression based on AI techniques; and transmits the third version of the audio signal to the communication platform.

Abstract: Dynamic adjustment of audio characteristics for enhancing musical sound during a networked conference is disclosed. In an embodiment, a method is provided for sound enhancement performed by a device coupled to a network. The method includes receiving an audio signal to be transmitted over the network, detecting when musical content is present in the audio signal, processing the audio signal to enhance voice characteristics to generate an enhanced audio signal when the musical content is not detected, processing the audio signal to enhance music characteristic to generate the enhanced audio signal when the musical content is detected, and transmitting the enhanced audio signal over the network.

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 and systems provide users of a communication platform with intelligent, real-time noise suppression for audio signals broadcasted in a communication session. The system receives an input audio signal from an audio capture device; processes the input audio signal to provide a second version of the audio signal with noise suppression based on DSP techniques; transmits the second version of the audio signal to a communication platform for real-time streaming; classifies, via a machine learning algorithm, whether the second version of the audio signal contains noise beyond a noise threshold; based on a classification that the second version of the audio signal contains noise beyond the noise threshold, processes the second version of the audio signal to provide a third version of the audio signal with noise suppression based on AI techniques; and transmits the third version of the audio signal to the communication platform.

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 target speaker extraction. A target speaker extraction system receives an audio frame of an audio signal. A multi-speaker detection model analyzes the audio frame to determine whether the audio frame includes only a single-speaker or multiple speakers. When the audio frame includes only a single-speaker, the system inputs the audio frame to a target speaker VAD model to suppress speech in the audio frame from a non-target speaker based on comparing the audio frame to a voiceprint of a target speaker. When the audio frame includes multiple speakers, the system inputs the audio frame to a speech separation model to separate the voice of the target speaker from a voice mixture in the audio frame.

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: Enhancing group sound during a networked conference is disclosed. In an embodiment, a method includes generating an audio signal at a user equipment (UE), detecting a group sound in the audio signal, generating a group sound indicator that identifies the detected group sound, and transmitting the group sound indicator to a network server. The method also includes receiving, from the network server, a control signal that identifies a selected group sound, and reproducing, at the UE, the selected group sound identified by the control signal.

Abstract: Methods and systems provide users of a communication platform with intelligent, real-time noise suppression for audio signals broadcasted in a communication session. The system receives an input audio signal from an audio capture device; processes the input audio signal to provide a second version of the audio signal with noise suppression based on DSP techniques; transmits the second version of the audio signal to a communication platform for real-time streaming; classifies, via a machine learning algorithm, whether the second version of the audio signal contains noise beyond a noise threshold; based on a classification that the second version of the audio signal contains noise beyond the noise threshold, processes the second version of the audio signal to provide a third version of the audio signal with noise suppression based on AI techniques; and transmits the third version of the audio signal to the communication platform.