Microsoft Technology Licensing, LLC

Ivan Jelev Tashev

Alejandro Acero

Michael Lewis Seltzer

Hannes Gamper

David Emerson Johnston

John Charles Krumm

Ruston Panabaker

Jeffrey Darren Couckuyt

Neil Warren Black

Henrique Sarmento Malvar

Shuayb M Zarar

Yun-Cheng Ju

Mark R P Thomas

Paramvir Bahl

John Carlton Platt

Jason S Flaks

Piotr Tadeusz Bilinski

Jens Ahrens

Nilesh Madhu

Andrew David Wilson

Ross Garrett Cutler

Yong Rui

Sharad Agarwal

James G Droppo

Christian Holz

Robert Norman Heitkamp

Edward Bryan Cutrell

Mihai R Jalobeanu

Thomas Matthew Gable

Ionut Constandache

Yu-Te Wang

Byung-Jun Yoon

Dimitra Emmanouilidou

Raymond Michael Winters, Iv

Adrian Kuo Ching Lee

Hakim Si Mohammed

Mark Richard Paul Thomas

Tan Gemicioglu

Ranveer Chandra

Anoop Gupta

Michael F Cohen

Thomas Moscibroda

John Allen Tardif

Eric H Rudolph

Thomas M Soemo

Michael Baseflug

Wei Guo

Ville Topias Pulkki

Nikunj Raghuvanshi

Matthai Philipose

Li-Wei He

Ivan Dokmanic

Alex Acero

Molly Rose Suver

Duncan James McKay

Stefanie Lyn Tomko

Daniel J Sisolak

Tyler Gleghorn

Amit Das

Chad R Heinemann

Archontis Politis

Leo Shing

Yan-Hui Tu

Xudong Ni

Chin-Hui Lee

Daniel Allred

Han Zhao

John E Unverferth, Iii

Jong Hwan Ko

Michael Lews Seltzer

Etienne Thuillier

Christoph Felix Hold

David Emerson Johnson

George Nychis

Frank Liu

Hoang T Do

Lae-Hoon Kim

Slavi Mihov

Stephen Edward Hodges

Jay Kapur

Mike Seltzer

This document relates to employing tongue gestures to control a computing device, and training machine learning models to detect tongue gestures. One example relates to a method or technique that can include receiving one or more motion signals from an inertial sensor. The method or technique can also include detecting a tongue gesture based at least on the one or more motion signals, and outputting the tongue gesture.

Inertial sensing of tongue gestures

A computer device is provided that includes a display device, and a sensor system configured to be mounted adjacent to a user's head and to measure an electrical potential near one or more electrodes of the sensor system. The computer device further includes a processor configured to present a periodic motion-based visual stimulus having a changing motion that is frequency-modulated for a target frequency or code-modulated for a target code, detect changes in the electrical potential via the one or more electrodes, identify a corresponding visual evoked potential feature in the detected changes in electrical potential that corresponds to the periodic motion-based visual stimulus, and recognize a user input to the computing device based on identifying the corresponding visual evoked potential feature.

Periodic motion-based visual stimulus

Systems, methods, and computer-readable storage devices are disclosed for improved real-time audio processing. One method including: constructing a deep neural network model, including a plurality of at least one-bit neurons, configured to output a predicted label of audio data, the plurality of at least one-bit neurons arranged in a plurality of layers, including at least one hidden layer, and being connected by a plurality of connections, each connection having at least a one-bit weight, wherein one or both of the plurality of at least one-bit neurons and the plurality of connections have a reduced bit precision; receiving a training data set, the training data set including audio data; training the deep neural network model using the training data set; and outputting a trained deep neural network model configured to output a predicted label of real-time audio data.

Systems, methods, and computer-readable media for improved real-time audio processing

Systems, methods, and computer-readable storage devices are disclosed for improved audio feature discovery using a neural network. One method including: receiving a trained neural network model, the trained neural network configured to output an audio feature classification of audio data; deconstructing the trained neural network model to generate at least one saliency map, the at least one saliency map providing a successful classification of the audio feature; and extracting at least one visualization of the audio feature the trained neural network model relies on for classification based on the at least one saliency map.

Systems, methods, and computer-readable media for improved audio feature discovery using a neural network

A computing system that facilitates decoding a spherical harmonics (SH) representation of a three-dimensional sound signal to a binaural sound signal is described herein. The computing system generates a binaural sound signal based upon the SH representation, a tapering window function that is selected based on an SH encoding order of the SH representation, and a coloration compensation filter that incorporates the tapering window function. The computing system causes the binaural sound signal to be played over at least two speakers.

Computing system for binaural ambisonics decoding

A facility for processing output from a network of mechanical sensors is described. The facility accesses time-series data outputted by the network of sensors. The facility applies to the accessed time-series data a trained autoencoder to obtain a version of the accessed time-series data in which noise present in the accessed time-series data is at least partially suppressed. The facility stores the obtained version of the accessed time-series data, such as in order to perform human activity recognition against the obtained version of the accessed time-series data.

Learning-based noise reduction in data produced by a network of sensors, such as one incorporated into loose-fitting clothing worn by a person

Systems, methods, and computer-readable storage devices are disclosed for improved real-time audio processing. One method including: receiving audio data including a plurality of frames having a plurality of frequency bins; calculating, for each frequency bin, an approximate speech signal estimation based on the plurality of frames; calculating, for each approximate speech signal estimation, a clean speech estimation and at least one additional target including an ideal ratio mask using a trained neural network model; and calculating, for each frequency bin, a final clean speech estimation using the calculated at least one additional target including the calculated ideal ratio mask and the calculated clean speech estimation.

An ultrasonic gesture recognition system is provided that recognizes gestures based on analysis of return signals of an ultrasonic pulse that is reflected from a gesture. The system transmits an ultrasonic chirp and samples a microphone array at sample intervals to collect a return signal for each microphone. The system then applies a beamforming technique to frequency domain representations of the return signals to generate an acoustic image with a beamformed return signal for multiple directions. The system then generates a feature image from the acoustic images to identify, for example, distance or depth from the microphone array to the gesture for each direction. The system then submits the feature image to a deep learning system to classify the gesture.

Ultrasonic based gesture recognition

The derivation of personalized HRTFs for a human subject based on the anthropometric feature parameters of the human subject involves obtaining multiple anthropometric feature parameters and multiple HRTFs of multiple training subjects. Subsequently, multiple anthropometric feature parameters of a human subject are acquired. A representation of the statistical relationship between the plurality of anthropometric feature parameters of the human subject and a subset of the multiple anthropometric feature parameters belonging to the plurality of training subjects is determined. The representation of the statistical relationship is then applied to the multiple HRTFs of the plurality of training subjects to obtain a set of personalized HRTFs for the human subject.

HRTF personalization based on anthropometric features

A system is provided that employs a statistical approach to semi-supervised speech enhancement with a low-order non-negative matrix factorization ('NMF'). The system enhances noisy speech based on multiple dictionaries with dictionary atoms derived from the same clean speech samples and generates an enhanced speech representation of the noisy speech by combining, for each dictionary, a clean speech representation of the noisy speech generated based on a NMF using the dictionary atoms of the dictionary. The system generates frequency-domain ('FD') clean speech sample representations of the clean speech samples, for example, using a Fourier transform. To generate each dictionary, the system generates a dictionary-unique initialization of the dictionary atoms and the activations and performs a NMF of the FD clean speech samples.

Speech enhancement with low-order non-negative matrix factorization

Systems and methods for HRTF personalization are provided. More specifically, the systems and methods provide HRTF personalization utilizing non-parametric processing of three-dimensional head scans. Accordingly, the systems and methods for HRTF personalization generate a personalized set of HRTFs for a user without having to extract specific geometric and/or anthropometric features from a three dimensional head scan of a user and/or from the three dimensional head scans of training subjects in a database.

Systems and methods for non-parametric processing of head geometry for HRTF personalization

Systems and methods of providing an audio signal are disclosed herein. In one embodiment, a method of delivering an audio signal from a device toward a user's ear includes, for example, transmitting a filtered audio signal from a transducer positioned at a location on the device that is longitudinally spaced apart from an entrance of an auditory canal of the user's ear when the device is worn on the user's head.

Systems and methods for audio creation and delivery

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Kirkland, WA, United States of America

Ivan Jelev Tashev