Massachusetts Institute of Technology

Paul T Calamia

Christopher J Smalt

Trina Rae Vian

Joseph John Lacirignola

David Christopher Maurer

Thomas Francis Quatieri, Jr

Edward H Chen

David Francis Aubin, Jr

Nima Mesgarani

Kerry Alexander Johnson

James O'Sullivan

Paula P Collins

Christine Michelle Weston

Michael S Brandstein

Gregory Alan Ciccarelli

Joseph Perricone

Michael Nolan

Stephanie Haro

Mary Katherine Byrd

Shakti Davis

Olha Townsend

In one aspect of the present disclosure, method includes: receiving neural data responsive to a listener's auditory attention; receiving an acoustic signal responsive to a plurality of acoustic sources; for each of the plurality of acoustic sources: generating, from the received acoustic signal, audio data comprising one or more features of the acoustic source, forming combined data representative of the neural data and the audio data, and providing the combined data to a classification network configured to calculate a similarity score between the neural data and the acoustic source using one or more similarity metrics; and using the similarity scores calculated for each of the acoustic sources to identify, from the plurality of acoustic sources, an acoustic source associated with the listener's auditory attention.

End-to-end deep neural network for auditory attention decoding

Systems, apparatus, and methods for collecting, interpreting, and utilizing noise exposure data may include sensors to obtain an analog signal representative of impulse noise sound pressure and an analog signal representative of continuous noise sound pressure. At least one ADC may generate digital signals by sampling the analog signals at rates equal to or greater than twice the reciprocal of a minimum impulse noise rise time. Accelerometers may obtain data in close proximity to and remote from the sensors. At least one processor may include a first combining node to combine the digital signals to represent both the continuous noise and the impulse noise, a shock-artifact detection filter to identify a time frame including a shock artifact based on the accelerometry data, a frequency filter to generate a background-removed audio signal, an adaptive filter to estimate the shock artifact, and a second combining node to produce a shock-artifact-free audio signal.

High fidelity systems, apparatus, and methods for collecting noise exposure data

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Framingham, MA, United States of America

Paul T Calamia