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Promotionsvortrag: Least-Squares Methods for Individualization of Hearables with Active Noise Cancellation
Johannes Fabry
Mittwoch, 18. Oktober 2023
11:00 Uhr
IKS 4G / hybrid
Excessive noise exposure poses significant public health risks, including hearing damage, stress, sleep disturbances, and cardiovascular diseases. Active noise cancellation (ANC) in headphones plays a crucial role in mitigating these risks by reducing ambient noise directly at the ear. ANC headphones accomplish this by generating a cancellation signal that destructively interferes with ambient noise.
However, processing power and battery life impose strict limitations on current ANC headphones. Existing approaches typically employ time-invariant or simple adaptive filters, resulting in suboptimal performance and limited adaptability to individual users and rapidly changing noise environments.
- This dissertation strives to overcome these challenges with two primary objectives:Develop methods for enhancing ANC performance by calibrating ANC headphones in real-world conditions and configuring the ANC transfer characteristic to individual users' needs.
- Create efficient adaptive algorithms for ANC that deliver near-optimal performance, are compatible with relevant audio codecs, and demand minimal prior knowledge about the acoustic system.
This dissertation introduces a method for in-the-field calibration of ANC headphones. It accounts for a user’s individual fit of the headphone by estimating the acoustic primary and secondary path. Evaluations on a vast dataset show a significantly improved ANC performance compared to a generic one-size-fits-all filter design.
The second major contribution of this work is Active Acoustic Equalization (AAE) as a framework that allows users to customize the ANC headphone's transfer characteristic. Thereby, users can adjust how they hear ambient sound to their personal liking. AAE utilizes frequency-domain filter optimization that considers the non-linear perception of magnitude and frequency of the human auditory system. Experimental results demonstrate superior performance over existing methods.
The third major contribution of this work is the development of a system-theoretic model and adaptive algorithms for feed-forward ANC. This model accounts for self-induced disturbances. The novel Composed Kalman Filter (CKF) retains the advantageous properties of the Kalman filter at a fraction of its computational complexity. The proposed online secondary path estimator enhances the signal-to-noise ratio of a measurement signal whilst rendering it inaudible.
