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Bachelor-Vortrag: Real-Time Use-Case Classification for Intelligent Control of Hearables
Dienstag, 8. Dezember 2020
A current goal in the development of headphones and hearables is that users can wear them at any time. Especially with the integration of more and more features, specifically active noise cancellation (ANC) and transparency features, a method of recognizing different acoustic situations is necessary to enable intelligent control.
In this thesis an approach for intelligent classification of the current wearing situation, or use case of headphones and hearing aids is investigated. In addition to the simple recognition of whether the earpiece is currently in use, the recognition of special cases such as different fittings and closing the speaker outlet is examined. The sensor signals used comprise one external and one internal microphone signal for each side of the head. Real-time capable features are extracted from the sensor signals and tested for their information content regarding a use case classification. Based on headphone simulations, a forward feature selection is conducted to find the set of features enabling the most robust classification regardless of any kinds of disturbance noise. The influence of both air conducted sound (ambient noise) and body conducted sound (e.g. talking or chewing) is considered. The mutual information is utilized to measure the quality of a feature. Five well-known real-time capable classification methods are then applied using the determined features and compared: the Fisher discriminant, the least squares discriminant, logistic regression, decision trees and Gaussian mixture models. Finally, the applicability of the developed principles is verified by an implementation on a real-time system (dSPACE ds1005)
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