Amazon Technologies, Inc.

Meta Platforms, Inc.

Creative Technology Corporation

Rawles LLC

Applied Materials, Inc.

Dts, Inc.

Gn Resound A/s

Srs Labs, Inc.

Paypal, Inc.

Meta Platforms Technologies, LLC

Fortemedia, Inc.

Jun Yang

Joshua Warren Bingham

Robert Ridder

Steven Verity

Philip Ryan Hilmes

Richard J Oliver

Colin Randall McEnroe

James Tracey

Hongbin Ji

Chad Georges Seguin

Chung-Shin Kang

Jian Guo

Ian Minett

Yingbin Liu

Klaas Vogelsang

Fa-Long Luo

Fei Li

Brent Edwards

Ming Zhang

Chun Kiat Ho

Zezhang Hou

Lin Song

Zhenyin Yang

Kai Xie

Caslav V Pavlovic

Yuehao Wu

Meng Zang

Ke Zheng

Wen Lu

Xing He

Craig McFarland

Nick Michael

Chinmayee Rathi

Haoliang Dong

Rick Oliver

Arye Nehorai

In embodiments of a digital lithography system, physical design data prepared at a data prep server in a hierarchical data structure. A leaf node comprises a repeater nod, comprising a bitmap image and a plurality of locations at which the bitmap appears in a physical design. At an EYE server, a repeater node bitmap is adjusted based upon, for example, spatial light modulator rotational adjustment and substrate distortion. The adjusted repeater node and the plurality of locations in which the adjusted repeater appears is compared to the repeater of the data prep server and its plurality of locations. In further embodiments, a rasterizer generates a checksum of bitmap to be printed to a substrate, from the EYE server bitmap. The checksum is compared to a checksum of the EYE server bitmap.

Data inspection for digital lithography for HVM using offline and inline approach

A computer system receives a set of user-defined rules are that useable by a computer service to automate a decision flow. The computer system generates a graph model from the user-defined rules. From the graph model, the computer system determines an input dependency model that is indicative of a set of inputs referred to in the graph model. The input dependency model is useable by an orchestrator to coordinate accesses to the one or more data stores in which the set of inputs is stored. The computer system receives the set of inputs and determines one or more automated decisions by applying the set of inputs to the graph model.

Automated decision platform

A method of detecting human voice activity includes determining a presence of human voice in a frame of audio signal using a plurality of features extracted from the frame of audio signal. The extracted features can include a number of zero-crossings, a periodicity metric, an energy ratio between a low frequency band and a high frequency band, and an envelope-to-floor ratio (EFR) in the frame of audio signal. Each of the features is associated with predefined criteria indicative of a presence of human voice, and based on comparisons of the features to the respective predefined criteria, the voice activity detector determines whether the frame of audio signal includes a human voice.

Voice activity detection in audio signal

A system that suppresses a plurality of interferences of different types in a received audio signal. The system comprises one or more microphones and an audio controller. The one or more microphones are configured to detect the audio signal. The audio controller applies an interference estimation algorithm to the audio signal to generate an attenuation coefficient for each of the plurality of interferences of different types. The audio controller applies the attenuation coefficients to the audio signal to generate an interference-suppressed audio signal in which the plurality of interferences of different types is suppressed. The audio controller determines a time domain signal based on the interference-suppressed audio signal to provide to an end user.

Joint suppression of interferences in audio signal

An electronic device includes one or more microphones that generate audio signals and a wind noise detection subsystem. The electronic device may also include a wind noise reduction subsystem. The wind noise detection subsystem applies multiple wind noise detection techniques to the set of audio signals to generate corresponding indications of whether wind noise is present. The wind noise detection subsystem determines whether wind noise is present based on the indications generated by each detection technique and generates an overall indication of whether wind noise is present. The wind noise reduction subsystem applies one or more wind noise reduction techniques to the audio signal if wind noise is detected. The wind noise detection and reduction techniques may work in multiple domains (e.g., the time, spatial, and frequency domains).

Detection and removal of wind noise

A method of detecting voice activity includes performing a video analysis on a frame of video signal to determine a position of a user in the frame and to identify one or more beams of a corresponding audio signal associated with a region including the position of the user. The identified one or more beams of audio signal are analyzed to determine whether voice is present in the frame. When a user is not identified during the video analysis of the frame of video signal, audio analysis is not performed on the corresponding frame of audio signal.

Voice activity detection using audio and visual analysis

A system manages temperature and excursion effects on a loudspeaker. Data corresponding to a temperature of the loudspeaker and data corresponding to an excursion (i.e., displacement) of a membrane of the loudspeaker are determined. The dynamic ranges of the temperature and excursion data are compressed, combined, and smoothed. This smoothed data is multiplied with a delayed version of audio data; peak values of the result may be limited. This operation may be performed on multiple frequency bands of audio data. The final output data is combined to form a full-band signal and sent to a loudspeaker.

Excursion and thermal management for audio output devices

An effective and simple psychoacoustic bass generation system generates a harmonic signal having inter-modulation controllable to remain below a threshold level and includes a high-pass filter configured to pass harmonics which are reproducible with fidelity by the loudspeaker or other transducer and a loudness matching block configured to compensate the loudness of the desired harmonics to match the loudness of the original signal.

Adaptive bass processing system

A system that includes an automatic volume leveler (AVL) that processes audio data based on audio category and desired volume level. The system may select different settings for audio data associated with different audio sources (e.g., content providers), audio categories (e.g., types of audio data, such as music, voice, etc.), genres, and/or the like. For example, the system may distinguish between music signals and voice signals (e.g., speech) and may apply a first gain curve for the music and a second gain curve for the speech. Additionally or alternatively, the system may distinguish between different genres of music and may apply different gain curves based on the genre. Further, the system may select a gain curve based on a desired volume level. Therefore, output audio data generated by performing AVL may be optimized based on the audio category, audio source, genre, desired volume level, and/or the like.

Automatic volume leveler

Devices and techniques are generally described for automatic volume leveling of an audio signal. An audio signal may be received and a first portion of the audio signal may be stored in a buffer. A root mean square (RMS) estimate of a level of the first portion of the audio signal may be determined based at least in part on a second portion of the audio signal previous to the first portion. A modified audio signal may be generated by multiplying the first portion of the audio signal in the buffer by a variable gain. The variable gain may be based at least in part on the RMS estimate. The modified audio signal may be sent to a loudspeaker.

Automatic volume leveling

A system that performs automatic volume control and leveling (AVCL) based on audio category and desired volume level. The system may select different settings for audio data associated with different audio sources (e.g., content providers), audio categories (e.g., types of audio data, such as music, voice, etc.), genres, and/or the like. For example, the system may distinguish between music signals and voice signals (e.g., speech) and may apply a first gain curve for the music and a second gain curve for the speech. The gain curve may include an adaptive noise gate to reduce in-channel noise, such as by attenuating quiet sounds. The system may select the gain curve based on a desired volume level, and in some examples may modify the gain curve based on a signal-to-noise ratio (SNR) of the input audio and/or based on environmental noise.

Automatic volume control and leveler

A hands-free audio device has a receive path and a transmit path, which may operate at different audio sampling rates. The transmit path has an interference suppressor that receives a reference signal from the receive path and that suppresses interference in microphone signals received from a microphone array. The interference suppressor is followed in the transmit path by a multi-channel adaptive beamformer that produce a plurality of directional audio signals. A beam selector is configured to select one of the directional audio signals based on voice activity, echo detection, and signal energy.

Adaptive directional audio enhancement and selection

A system capable of directing audio output to a portion of a shared acoustic environment. For example, the system may divide the environment into two or more sound zones and may generate audio output directed to one or more sound zones. The system may distinguish between target sound zones and quiet sound zones and may determine a set of global filter coefficients with which to direct the audio output. The system may generate a first set of filter coefficients that increase audio volume in the target sound zones and a second set of filter coefficients that increase a ratio of audio volume between the target sound zones and the quiet sound zones. The system may generate the set of global filter coefficients using a combination of the first set and the second set. The system may also direct audio from multiple audio sources in different directions.

Sound zone reproduction system

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San Jose, CA, United States of America

Jun Yang