Starkey Laboratories, Inc.

Arizona State University

Deepak Kadetotad

Tao Zhang

Jae-sun Seo

Chaitali Chakrabarti

Daniel Marquardt

Achin Bhowmik

Sairam Arunachalam

An audio processing path receives an audio signal from a microphone of an ear-wearable device and reproduces the audio signal at a receiver that is placed within an ear of a user. A deep neural network (DNN) is coupled to the audio processing path that performs speech enhancement on the audio signal. An audio feature detector is operable to detect an audio change via the processing path that triggers a change of state of the DNN. The change of state affects resource consumption by the DNN. The change of state is applied to the DNN, and the DNN performs the speech enhancement in the changed state.

Hearing device with dynamic neural networks for sound enhancement

An ear-wearable device stores a plurality of neural network data objects each defining a respective neural network. A sound signal received from a microphone of the ear-wearable device is digitized. An ambient environment of the digitized sound signal is classified into one of a plurality of classifications. Based on the classification, one of the neural network data objects is selected to enhance the digitized sound signal. An analog signal is formed based on the enhanced digitized sound signal. The analog signal is reproduced via a receiver of the ear-wearable device.

Hearing device with multiple neural networks for sound enhancement

Aspects disclosed in the detailed description include memory compression in a deep neural network (DNN). To support a DNN application, a fully connected weight matrix associated with a hidden layer(s) of the DNN is divided into a plurality of weight blocks to generate a weight block matrix with a first number of rows and a second number of columns. A selected number of weight blocks are randomly designated as active weight blocks in each of the first number of rows and updated exclusively during DNN training. The weight block matrix is compressed to generate a sparsified weight block matrix including exclusively active weight blocks. The second number of columns is compressed to reduce memory footprint and computation power, while the first number of rows is retained to maintain accuracy of the DNN, thus providing the DNN in an efficient hardware implementation without sacrificing accuracy of the DNN application.

Memory compression in a deep neural network

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Tempe, AZ, United States of America

Deepak Kadetotad