Conexant Systems, Inc.

Synaptics Corporation

Google Inc.

Trausti Thormundsson

Ragnar Hlynur Jonsson

Govind Kannan

Francesco Nesta

Vilhjalmur S Thorvaldsson

Ali Abdollahzadeh Milani

Yair Kerner

Harry K Lau

Youhong Lu

Saeed Mosayyebpour Kaskari

James Walter Wihardja

Sverrir Olafsson

Mark E Miller

Wensen Liu

Jayvon Timmons

Saeed Mosayyebpour

Shlomi I Regev

Willie Wu

Steve Rui

Jens Kristian Poulsen

Derek T Walton

Mouna Elkhatib

Jonathan Chien

Sahar E Bou-Ghazale

Stuart Whitfield Hutson

Harry Lau

Hari Hariharan

Frederic Philippe Denis Mustiere

Thorarinn Vikingur Sveinsson

Richard Edwin Howard

Xiaoran Fan

Chris X Gao

Thomas Aaron Gulliver

Sveinn V Grimsson

Xiangyuan Zhao

Randall Deetz

Mark Miller

Rich Howard

Kannan Govind

Techniques and apparatuses are described that perform heart rate variability detection using a hearable. A hearable, such as an earbud, is capable of performing a novel physiological monitoring process termed herein audioplethysmography, which is an active acoustic method capable of sensing subtle physiologically-related changes observable at a user's outer and middle ear. Instead of relying on other auxiliary sensors, such as optical or electrical sensors, audioplethysmography involves transmitting and receiving acoustic signals to monitor a user's biometrics, including heart rate variability and/or blood pressure. In addition to being relatively unobtrusive, some hearables can be configured to support audioplethysmography without the need for additional hardware. As such, the size, cost, and power usage of the hearable can help make health monitoring accessible to a larger group of people and improve the user experience with hearables.

Detecting heart rate variability using a hearable

Systems and methods for audio listening devices, comprise a speaker coupled to a first housing, a sound port having a first end and a second end, wherein the first end is coupled to the first housing, and the second end is configured to be inserted in an ear canal of a person such that sound waves emitted from the speaker propagates via a secondary path to the ear canal through the sound port, active noise cancellation (ANC) components configured to generate anti-noise signals through the micro-speakers to cancel external noise, and a first microphone disposed within the sound port at the second end of the sound port such that the first microphone is configured to detect the anti-noise signal that propagates through the sound port via the secondary path and the external noise that propagates via a primary path.

Active noise cancelling earbud devices

Systems and methods for enhancing a headset user's own voice include at least two outside microphones, an inside microphone, audio input components operable to receive and process the microphone signals, a voice activity detector operable to detect speech presence and absence in the received and/or processed signals, and a cross-over module configured to generate an enhanced voice signal. The audio processing components includes a low frequency branch comprising low pass filter banks, a low frequency spatial filter, a low frequency spectral filter and an equalizer, and a high frequency branch comprising highpass filter banks, a high frequency spatial filter, and a high frequency spectral filter.

Bone conduction headphone speech enhancement systems and methods

Systems and methods provide input and output mode control for audio processing on a user device. Audio processing may be configured by monitoring audio activity on a device having at least one microphone and a digital audio processing unit, collecting information from the monitoring of the activity, including an identification of at least one application utilizing audio processing, and determining a context for the audio processing, the context including at least one of a hardware, software, audio signal and/or environmental context. An audio signal processing configuration is determined based on the application and determined context, an associated audio signal processing mode is selected, and an optimized audio signal generated.

Input/output mode control for audio processing

Adaptive noise cancellation systems and methods comprise a reference sensor operable to sense environmental noise and generate a corresponding reference signal, an error sensor operable to sense noise in a noise cancellation zone and generate a corresponding error signal, a noise cancellation filter operable to receive the reference signal and generate an anti-noise signal to cancel the environmental noise in the cancellation zone, an adaptation module operable to receive the reference signal and the error signal and adaptively adjust the anti-noise signal.

Noise amplification control in adaptive noise cancelling systems

Active noise cancellation systems and methods include a feedforward path configured to receive a reference signal comprising ambient noise and adaptively generate an anti-noise signal to cancel the ambient noise and comprising an adaptive gain component configured to adaptively adjust a gain of the anti-noise signal, and a logic device configured to determine a leakage profile based on the adaptive gain component. The feedforward path includes a feedforward adaptive filter tuned to generate the anti-noise signal corresponding to the reference signal in accordance with the determined leakage profile. The leakage profile is selected from a plurality of stored leakage profiles that are tuned for a corresponding gain, and which corresponds to an ear coupling condition associated with a fit between the active noise cancellation system and a user. An adaptive transparency filter receives the reference signal and generates an ambient inclusion signal for output to a user.

Active noise cancelling based on leakage profile

Systems and methods for processing an audio signal include an audio input operable to receive an input signal comprising a time-domain, single-channel audio signal, a subband analysis block operable to transform the input signal to a frequency domain input signal comprising a plurality of k-spaced under-sampled subband signals, a reverberation reduction block operable to reduce reverberation effect, including late reverberation, in the plurality of k-spaced under-sampled subband signals, a noise reduction block operable to reduce background noise from the plurality of k-spaced under-sampled subband signals, and a subband synthesis block operable to transform the subband signals to the time-domain, thereby producing an enhanced output signal.

Real-time single-channel speech enhancement in noisy and time-varying environments

Adaptive noise cancellation systems and methods comprise a reference sensor operable to sense environmental noise and generate a corresponding reference signal, an error sensor operable to sense noise in a noise cancellation zone and generate a corresponding error signal, a noise cancellation filter operable to receive the reference signal and generate an anti-noise signal to cancel the environmental noise in the cancellation zone, an adaptation module operable to receive the reference signal and the error signal and adaptively adjust the anti-noise signal. The adaptation module includes a noise amplification control module operable to adaptively control noise amplification in at least one hiss region of the anti-noise signal, while achieving cancellation in non-hiss regions of the anti-noise signal.

An end detector configured to receive the feature data and detect an end point of a keyword, and a start detector configured to receive an indication of the detected end point and process the feature data associated with corresponding input frames to detect a start point of the keyword. The start detector and end detector comprise neural networks trained through a process using a cross-entropy cost function for non-Region of Target (ROT) frames and a One-Spike Connectionist Temporal Classification cost function for ROT frames.

Robust start-end point detection algorithm using neural network

A classification training system for binary and multi-class classification comprises a neural network operable to perform classification of input data, a training dataset including pre-segmented, labeled training samples, and a classification training module operable to train the neural network using the training dataset. The classification training module includes a forward pass processing module, and a backward pass processing module. The backward pass processing module is operable to determine whether a current frame is in a region of target (ROT), determine ROT information such as beginning and length of the ROT and update weights and biases using a cross-entropy cost function and One Spike Connectionist Temporal Classification (OSCTC) cost function. The backward pass module further computes a soft target value using ROT information and computes a signal output error using the soft target value and network output value.

Binary and multi-class classification systems and methods using one spike connectionist temporal classification

An acoustic event detection and classification system includes a start-end point detector and multi-class acoustic event classification. A classification training system comprises a neural network configured to perform classification of input data, a training dataset including pre-segmented, labeled training samples, and a classification training module configured to train the neural network using the training dataset. The classification training module includes a forward pass processing module, and a backward pass processing module. The backward pass processing module is configured to determine whether a current frame is in a region of target (ROT), determine ROT information such as beginning and length of the ROT and update weights and biases using a cross-entropy cost function and a many-or-one detection (MOOD) cost function. The backward pass module further computes a soft target value using ROT information and computes a signal output error using the soft target value and network output value.

Recurrent neural network based acoustic event classification using complement rule

Adaptive noise cancellation systems and methods comprise a reference sensor operable to sense environmental noise and generate a corresponding reference signal, an error sensor operable to sense noise in a noise cancellation zone and generate a corresponding error signal, a noise cancellation filter operable to receive the reference signal and generate an anti-noise signal to cancel the environmental noise in the cancellation zone, an adaptation module operable to receive the reference signal and the error signal and adaptively adjust the anti-noise signal, and a transient activity detection module operable to receive the reference signal, detect a transient noise event and selectively disable the adaptation module during the detected transient noise event.

Robust adaptive noise cancelling systems and methods

Adaptive noise cancellation systems and methods include a reference sensor to sense environmental noise and generate a corresponding reference signal, an error sensor to sense noise in a noise cancellation zone and generate a corresponding error signal, a noise cancellation path including a noise cancellation filter and a variable gain component, the noise cancellation path receiving the reference signal and generating an anti-noise signal to cancel noise at an eardrum reference point, and an adaptation module to receive the reference signal and the error signal and adaptively adjust weights of the noise cancellation filter and/or the variable gain component. An adaptive gain control block updates the variable gain component. Inputs to the adaptive gain control block are conditioned using programmable filters to protect against low frequency transients and/or high frequency distractors in the environmental noise. The programmable filters are tuned to optimize cancellation at an eardrum reference point.

Extended bandwidth adaptive noise cancelling system and methods

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Irvine, CA, United States of America

Trausti Thormundsson