Abstract: Systems and techniques are provided for conditioning virtual representatives. For example, a method can include obtaining, by a conditioning engine, a baseline model for a virtual representative; obtaining, by the conditioning engine, one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative; generating, based on the baseline model and the one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative, a conditioned model for the virtual representative; and outputting the conditioned model for the virtual representative.

Abstract: Systems and techniques are provided for conditioning virtual representatives. For example, a method can include obtaining, by a conditioning engine, a baseline model for a virtual representative; obtaining, by the conditioning engine, one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative; generating, based on the baseline model and the one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative, a conditioned model for the virtual representative; and outputting the conditioned model for the virtual representative.

Abstract: Disclosed are techniques for operating a voice assistant system. In an aspect, a large language processing subsystem of the voice assistant system may receive input audio data that corresponds to an input speech. The large language processing subsystem of the voice assistant system may process the input audio data to obtain an input text of the input speech and an input paralinguistic element of the input speech. The large language processing subsystem of the voice assistant system may generate a response based on the input text and the input paralinguistic element of the input speech. The large language processing subsystem of the voice assistant system may convert the response into output audio data that corresponds to an output speech.

Abstract: A device includes a memory configured to store a collection of sets of weights, each of the sets of weights representing a respective media segment. The device also includes one or more processors configured to generate data representing the detected first input speech segment and to pass the data representing the detected first input speech segment into a collection of memory units. Each memory unit of the collection of memory units includes a set of weights from the collection of sets of weights. The one or more processors are also configured to generate a first estimate of an associated media segment that represents the detected first input speech segment. The associated media segment corresponds to a first memory unit in the collection of memory units.

Abstract: A device includes a memory and one or more processors coupled to the memory and configured to execute instructions from the memory. Execution of the instructions causes the one or more processors to combine two or more data portions to generate input data for a decoder network. A first data portion of the two or more data portions is based on a first encoding of a data sample by a multiple description coding network and content of a second data portion of the two or more data portions depends on whether data based on a second encoding of the data sample by the multiple description coding network is available. Execution of the instructions also causes the one or more processors to obtain, from the decoder network, output data based on the input data and to generate a representation of the data sample based on the output data.

Abstract: Systems and techniques are described herein for improved feature correlation. For instance, an apparatus for improved featured correlation is provided. The method may include a projector configured to project a pattern into a scene for feature correlation by an imaging device that captures images of the pattern as projected into the scene; wherein the apparatus is separate from the imaging device.

Abstract: Systems and techniques are provided for conditioning virtual representatives. For example, a method can include obtaining, by a conditioning engine, a baseline model for a virtual representative; obtaining, by the conditioning engine, one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative; generating, based on the baseline model and the one or more conditioning inputs configured to condition an action in one or more multi-user experiences of the virtual representative, a conditioned model for the virtual representative; and outputting the conditioned model for the virtual representative.

Abstract: Systems and techniques are provided for coordinating multi-user experiences. For example, a process can include obtaining a plurality of settings associated with a plurality of multi-user experience participants. The plurality of settings includes one or more arbitrated settings and one or more non-arbitrated settings. The process can include arbitrating, by a settings arbitration engine, the one or more arbitrated settings to generate one or more adjusted settings for each arbitrated setting. The process can include generating, by an experience adaptation engine, an adapted multi-user experience, wherein the adapted multi-user experience is configured to enforce the one or more adjusted settings for each arbitrated setting.

Abstract: Systems and techniques are provided for adapting digital content. For example, a process can include obtaining a digital content comprising a default configuration for outputting the digital content to a device; outputting, by the device, the digital content based on the default configuration for outputting the digital content. The process can include obtaining, from a monitoring engine, content interaction information associated with outputting, by the device, the digital content based on the default configuration for outputting the digital content. The monitoring engine is configured to monitor one or more interactions between one or more users of the device and the digital content. The process can include generating, based on the content interaction information, a content adaptation for the digital content. The process can include outputting, by the device, the content adaptation for the digital content.

Abstract: A device includes one or more processors configured to input one or more segments of an input media stream into a feature extractor. The one or more processors are further configured to pass an output of the feature extractor into an utterance classifier to produce at least one representation of at least one utterance class of a plurality of utterance classes. The one or more processors are further configured to pass the output of the feature extractor and the at least one representation into a segment matcher to produce a media output segment identifier.

Abstract: A device includes a memory configured to store a collection of sets of weights, each of the sets of weights representing a respective media segment. The device also includes one or more processors configured to generate data representing the detected first input speech segment and to pass the data representing the detected first input speech segment into a collection of memory units. Each memory unit of the collection of memory units includes a set of weights from the collection of sets of weights. The one or more processors are also configured to generate a first estimate of an associated media segment that represents the detected first input speech segment. The associated media segment corresponds to a first memory unit in the collection of memory units.

Abstract: A device includes one or more processors configured to input one or more segments of an input media stream into a feature extractor. The one or more processors are further configured to pass an output of the feature extractor into an utterance classifier to produce at least one representation of at least one utterance class of a plurality of utterance classes. The one or more processors are further configured to pass the output of the feature extractor and the at least one representation into a segment matcher to produce a media output segment identifier.

Abstract: Systems and techniques are described herein for encoding and/or decoding audio information. For example, a process can process an input audio segment to generate a representation of the input audio segment, and can compare the representation of the input audio segment to representations stored in a memory. The representations represent a plurality of audio segments. The process can determine, based on the comparison, target representation(s) of target audio segment(s) from the representations stored in the memory. The process can determine one or more indices associated with the target audio segment(s). The process can then packetize the one or more indices and transmit the one or more packetized indices (e.g., to a decoder configured to decode the packetized indices).

Abstract: Methods, systems, and devices for encoding are described. A device, which may be otherwise known as user equipment (UE), may support standards-compatible audio encoding (e.g., speech encoding) using a pre-encoded database. The device may receive a digital representation of an audio signal and identify, based on receiving the digital representation of the audio signal, a database that is pre-encoded according to a coding standard and that includes a quantity of digital representations of other audio signals. The device may encode the digital representation of the audio signal using a machine learning scheme and information from the database pre-encoded according to the coding standard. The device may generate a bitstream of the digital representation that is compatible with the coding standard based on encoding the digital representation of the audio signal, and output a representation of the bitstream.

Abstract: Techniques are described for coding audio signals. For example, using a neural network, a residual signal is generated for a sample of an audio signal based on inputs to the neural network. The residual signal is configured to excite a long-term prediction filter and/or a short-term prediction filter. Using the long-term prediction filter and/or the short-term prediction filter, a sample of a reconstructed audio signal is determined. The sample of the reconstructed audio signal is determined based on the residual signal generated using the neural network for the sample of the audio signal.

Abstract: A device includes an encoder configured to determine, during a first period, that a first audio signal is a leading signal and that a second audio signal is a lagging signal. The encoder is also configured to generate a first frame of at least one encoded signal based on a first modified version of the second audio signal that is generated by adjusting the second audio signal based on a first mismatch value. The encoder is configured to determine, during a second period, that the first audio signal is the leading signal and that the second audio signal is the lagging signal. The encoder is configured to generate a second frame of the at least one encoded signal based on a second modified version of the second audio signal that is generated by adjusting the second audio signal based on the first mismatch value and a second mismatch value.

Abstract: Methods, systems, and devices for encoding are described. A device, which may be otherwise known as user equipment (UE), may support standards-compatible audio encoding (e.g., speech encoding) using a pre-encoded database. The device may receive a digital representation of an audio signal and identify, based on receiving the digital representation of the audio signal, a database that is pre-encoded according to a coding standard and that includes a quantity of digital representations of other audio signals. The device may encode the digital representation of the audio signal using a machine learning scheme and information from the database pre-encoded according to the coding standard. The device may generate a bitstream of the digital representation that is compatible with the coding standard based on encoding the digital representation of the audio signal, and output a representation of the bitstream.

Abstract: Techniques are described for coding audio signals. For example, using a neural network, a residual signal is generated for a sample of an audio signal based on inputs to the neural network. The residual signal is configured to excite a long-term prediction filter and/or a short-term prediction filter. Using the long-term prediction filter and/or the short-term prediction filter, a sample of a reconstructed audio signal is determined. The sample of the reconstructed audio signal is determined based on the residual signal generated using the neural network for the sample of the audio signal.

Abstract: A method includes decoding a low-band portion of an encoded mid channel to generate a decoded low-band mid channel. The method also includes filtering the decoded low-band mid channel according to one or more filter coefficients to generate a low-band filtered mid channel. The method also includes generating an inter-channel predicted signal based on the low-band filtered mid channel and the inter-channel prediction gain. The method further includes generating a low-band left channel and a low-band right channel based on an up-mix factor, the decoded low-band mid channel, and the inter-channel predicted signal.

Abstract: A method includes receiving, at an audio encoder, multiple streams of audio data. The method includes assigning a priority to each stream of the multiple streams and determining, based on the priority of each stream of the multiple streams, a permutation sequence for encoding of the multiple streams. The method also includes encoding at least a portion of each stream of the multiple streams according to the permutation sequence.