Qualcomm Incorporated

Daniel Jared Sinder

Venkatraman Atti

Vivek Rajendran

Stephane Pierre Villette

Venkata Subrahmanyam Chandra Sekhar Chebiyyam

Pravin Kumar Ramadas

Zheng Fang

Nikolai Konrad Leung

Subasingha Shaminda Subasingha

Ananthapadmanabhan Arasanipalai Kandhadai

Venkatesh Krishnan

Duminda Dewasurendra

Sen Li

Sharath Manjunath

Wesley James Holland

Jian Shen

Ziad Asghar

Alireza Heidari

Vinesh Sukumar

Miran Chun

Khaled Helmi El-maleh

Venkata Subrahmanyam Chebiyyam

Saurabha Rangrao Tavildar

Seyfullah Halit Oguz

Fatih Murat Porikli

Dai Yang

Ananthapadmanabhan Aasanipalai Kandhadai

Vikram Gupta

Alireza Ryan Heidari

Guillaume Konrad Sautière

Zisis Iason Skordilis

Venkatraman S Atti

Guillaume Konrad SAUTIÈRE

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.

Virtual representative conditioning system

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.

Multi-user experience coordination system

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.

Media segment representation using fixed weights

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.

Media segment prediction for media generation

A method for managing audio during a conference includes steering, at a spatial steering processor of a mobile device, first decoded audio to be projected from a speaker at a first angle. The method also includes steering, at the spatial steering processor, second decoded audio to be projected from the speaker at a second angle. The first decoded audio corresponds to a decoded version of a first audio stream from a first device associated with a first participant of the conference, and the second decoded audio corresponds to a decoded version of a second audio stream from a second device associated with a second participant of the conference. The first decoded audio is synchronized with the second decoded audio.

Conference audio management

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.

Speech encoding using a pre-encoded database

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.

Artificial intelligence based audio coding

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.

Encoding of multiple audio signals

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.

Multi-stream audio coding

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.

Time-domain inter-channel prediction

A method of capturing video includes instructing a microphone to initiate an audio scan of different areas of interest to detect audio activity. The method also includes receiving a feedback signal from the microphone in response to initiating the audio scan. The feedback signal indicates audio activity in a particular area of interest. The method further includes adjusting targeting characteristics of a device based on the feedback signal to focus the device on the particular area of interest.

Device adjustment based on laser microphone feedback

A method and an apparatus for estimating speech signal in split-domain is disclosed. The method includes performing LP analysis on a noisy speech signal to generate a first plurality of LPC and a first residual signal. The method also includes estimating speech LPC spectrum to generate cleaned LPC. The method further includes estimating speech residual spectrum to generate cleaned residual signal. The method also includes synthesizing output signals based on the cleaned LPC and the cleaned residual signal.

Split-domain speech signal enhancement

A method of capturing audio includes initiating capture, at a laser microphone, of first audio of an area of interest. The first audio is captured while the laser microphone is focused on a first target surface associated with the area of interest. The method also includes generating adjustment parameters based on a feedback signal to adjust targeting characteristics of the laser microphone. The method further includes adjusting the targeting characteristics of the laser microphone based on the adjustment parameters to focus the laser microphone on a second target surface associated with the area of interest. The method also includes initiating capture, at the laser microphone, of second audio of the area of interest in response to adjusting the targeting characteristics. The second audio has an audio quality that is greater than the first audio.

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San Diego, CA, United States of America

Daniel Jared Sinder