Apple Inc.

Google Inc.

Samsung Electronics Co., Ltd.

Cadence Design Systems, Inc.

Cirrus Logic Inc.

Yang Lu

Esge Brandt Andersen

Vasu Iyengar

Sarthak Khanal

Fatos Myftari

Behshad Behzadi

Zaheed Sabur

Andrew P Bright

Giovanni Motta

Xu Liu

Aram M Lindahl

Jeffrey Donald Alderson

Jon David Hendrix

Dayong Zhou

Vladan Bajic

Hanchi Chen

George Alban Heitz

John Woodruff

Steve Cheng

Gokay Baris Gultekin

Luv Kothari

Huisheng Wang

Angana Ghosh

Shijing Xian

Akshay Kannan

Srikanth Pandiri

Nuo Xu

Ali Abdollahzadeh Milani

Tom-Davy W Saux

Domenico Carbotta

Weiyi Qi

Qi Wang

Jing Wang

Woosung Choi

Uihui Kwon

Thomas M Jensen

Nicolo D'Ercole

Alessandro Agostini

Alvin Abdagic

Takeshi Okagaki

Jennifer Daniel

Navneet Gandhi

Adrien Olczak

Mehek Sharma

Saetbyeol Ahn

Fan Chen

Shirin Farrahi

Cristian Udrescu

Keqian Yan

Jackie Xiang

Jiawei Chen

Ajay Gokhale

Alexander Robertson

Dror Ayalon

Jongchol Kim

Implementations described herein relate to causing emoji(s) that are associated with a given emotion class expressed by a spoken utterance to be visually rendered for presentation to a user at a display of a client device of the user. Processor(s) of the client device may receive audio data that captures the spoken utterance, process the audio data to generate textual data that is predicted to correspond to the spoken utterance, and cause a transcription of the textual data to be visually rendered for presentation to the user via the display. Further, the processor(s) may determine, based on processing the textual data, whether the spoken utterance expresses a given emotion class. In response to determining that the spoken utterance expresses the given emotion class, the processor(s) may cause emoji(s) that are stored in association with the given emotion class to be visually rendered for presentation to the user via the display.

System(s) and method(s) for causing contextually relevant emoji(s) to be visually rendered for presentation to user(s) in smart dictation

Aspects of the subject technology provide for generation of a self-voice signal by an electronic device that is operating in an active noise cancellation mode. In this way, during a phone call, a video conference, or while listening to audio content, a user of the electronic device may benefit from active cancellation of ambient noise while still being able to hear their own voice when they speak. In various implementations described herein, the concurrent self-voice and automatic noise cancellation features are facilitated by accelerometer-based control of sidetone and/or active noise cancellation operations.

Adaptive noise cancellation and speech filtering for electronic devices

An ear cup housing has several reference microphones, an error microphone and a speaker. A processor drives the speaker for acoustic noise cancellation and transparency, by processing the microphone signals, and performs an oversight process by adjusting the reference microphone signals in response to detecting wind noise events and scratch events. In another aspect, the ear cup housing has an outside face that is joined to an inside face by a perimeter and the reference microphones are on the perimeter. Other aspects are also described and claimed.

Headphone with multiple reference microphones ANC and transparency

Implementations set forth herein relate to an automated assistant that can selectively determine whether to incorporate a verbatim interpretation of portions spoken utterances into an entry field and/or incorporate synonymous content into the entry field. For instance, a user can be accessing an interface that provides an entry field (e.g., address field) for receiving user input. In order to provide input for entry field, the user can select the entry field and/or access a GUI keyboard to initialize an automated assistant for assisting with filling the entry field. Should the user provide a spoken utterance, the user can elect to provide a spoken utterance that embodies the intended input (e.g., an actual address) or a reference to the intended input (e.g., a name). In response to the spoken utterance, the automated assistant can fill the entry field with the intended input without necessitating further input from the user.

Voice-controlled entry of content into graphical user interfaces

Signal to noise ratio, SNR, is determined in an acoustic ambient environment of an against-the-ear audio device worn by a user, wherein the acoustic ambient environment contains speech by a talker. When the SNR is above a threshold, dynamic range control is applied, as positive gain versus input level, to an audio signal from one or more microphones of the audio device. When the SNR is below the threshold, the dynamic range control applies as zero gain or negative gain to the audio signal. Other aspects are also described and claimed.

Ambient noise aware dynamic range control and variable latency for hearing personalization

Aspects of the subject technology relate to a device including a microphone, a filter and a processor. The filter receives an audio signal including ambient noise and a voice of a user of the device from the microphone. At least a portion of ambient noise is filtered from the audio signal. The processor determines a level of the ambient noise in the received audio signal and dynamically adjusts a gain applied to the filtered audio signal based on the level of the ambient noise.

Self-voice adaptation

Embodiments include herein are directed towards a method for use in an electronic design environment is provided. Embodiments may include receiving a netlist associated with an electronic design and performing genetic optimization on a portion of the netlist to identify and place one or more capacitors on a printed circuit board to minimize an impedance associated with a power plane. Embodiments may further include displaying, at a graphical user interface, a placement of the one or more capacitors, wherein the placement is based upon, at least in part, the performing.

System and method for decoupling capacitor selection and placement using genetic optimization

Embodiments help to provide a cross-device security schema for an audio device and a master device to which it is tethered (e.g., a smartphone). An example security scheme provides flexible mechanisms for locking and unlocking the audio device and the device to which it is tethered. For instance, an example security scheme may include: (a) an unlock sync feature that unlocks the audio device and keeps the audio device unlocked whenever the master device is unlocked, (b) a separate audio device unlock process that unlocks the audio device only (without unlocking the master device, and (c) an on-head detection process that, in at least some scenarios, locks the audio device in response to a determination that the audio device is not being worn.

Cross-device security scheme for tethered devices

Headphone with multiple reference microphones and oversight of ANC and transparency

A system for reconstructing wafer maps of semiconductor wafers includes: a processor; and memory having instructions stored thereon that, when executed by the processor, cause the processor to: receive test data of a wafer at sparse sampling locations of the wafer, the sparse sampling locations being selected based on a probing mask; and compute a reconstructed wafer map by performing compressed sensing with Zernike polynomials on the test data at sparse locations of the wafer.

Systems and methods for wafer map analysis

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San Jose, CA, United States of America

Yang Lu