Intel Corporation

Intel Corportation

Piotr Klinke

Jan Banas

Przemyslaw Maziewski

Pawel Trella

Damian Koszewski

Pawel Pach

Adam Kupryjanow

Kuba Lopatka

Roksana Sokolowska-Kostyk

Dominik Stanczak

Kuba Łopatka

Paweł Trella

Paweł Pach

Jedrzej Prysko

A method of audio device performance testing generates virtual audio device data packages.

Method and system of audio device performance testing

Apparatus, systems, methods, and articles of manufacture are disclosed for acoustic signal processing adaptive to microphone distances. An example system includes a microphone to convert an acoustic signal to an electrical signal and one or more processors to: estimate a distance between a source of the acoustic signal and the microphone; select a signal processing mode based on the distance; and process the electrical signal in accordance with the selected processing mode.

Acoustic signal processing adaptive to user-to-microphone distances

Techniques are provided for audio capture path evaluation of microphones incorporated into a device under test (DUT). A methodology implementing the techniques according to an embodiment includes estimating impulse responses (IRs) of the DUT microphones based on a comparison of a test audio signal received through the DUT microphones, at a selected measurement angle, to the test audio signal received through a reference microphone. The method also includes calculating group delays for the DUT microphones based on phase responses of the estimated IRs and calculating an average of the group delays. The method further includes calculating a distance, projected onto the measurement angle, between the DUT microphones and a geometric center of the DUT microphones. The distance is calculated as a product of the speed of sound and a difference between the average delay and the group delays for the DUT microphones. The process is repeated for additional measurement angles.

Open-loop multichannel audio impulse response measurement and capture path evaluation

A mechanism, method, and computer readable medium to enhance speech enabled devices. The method comprising receiving, by an ultrasonic attack detector of a speech enabled device, an audio stream from one or more microphones and a segmentation signal from a keyword detector indicating a location of a detected keyword within the audio stream, preprocessing, by the ultrasonic attack detector, a segmented portion of the audio stream including the detected keyword to obtain a spectrogram, and executing, by the ultrasonic attack detector, a neural network classifier using the spectrogram as input, the neural network classifier to discern real human speech from intermodulation distortion products resulting from ultrasonic attacks on the speech enabled device.

Ultrasonic attack detection employing deep learning

Techniques are provided for defending against an ultrasonic attack on a speech enabled device. A methodology implementing the techniques according to an embodiment includes detecting voice activity in an audio signal received by the device and generating an ultrasonic jamming signal in response to the detection. The jamming signal is broadcast over a loudspeaker for up to the duration of the detected voice activity to defend against the ultrasonic attack. According to another embodiment, the ultrasonic jamming signal is generated in response to detection of a wake-on-voice key phrase in the received audio signal, and the jamming signal is broadcast over the loudspeaker for a time duration selected to be less than or equal to a time window during which spoken commands are accepted by the device following the wake-on-voice key phrase detection. The jamming signal may include white or colored noise, combinations of tones, and/or a periodic sweep frequency.

Ultrasonic attack prevention for speech enabled devices

Growing community of inventors

Szczecin, Poland

Piotr Klinke