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Doctoral Defense: Least-Squares Methods for Individualization of Hearables with Active Noise Cancellation

Johannes Fabry
Wednesday, 18th October 2023
11:00 AM
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.

  1. 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.
  2. 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.