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

According to the World Health Organization, noise exposure is a major contributor to public health problems. Long-term exposure to occupational or recreational noise leads to noise-induced hearing loss or tinnitus, whereas environmental noise causes stress, sleep disturbances, chronic high annoyance, and cardiovascular diseases. Active Noise Cancellation (ANC) effectively supplements passive hearing protection to drastically reduce the level of ambient noise at the eardrum, especially at lower frequencies. The state of the art of ANC in headphones consists of either time-invariant or simple adaptive filters. The time-invariant filter approach implements a one-size-fits-all ANC solution. Consequently, the active attenuation of ambient noise varies strongly between users depending on the individual fit of the headphone. On the other hand, simple adaptive filters, such as the least mean squares algorithm, exhibit a slow convergence and tracking speed. Thus, the system cannot adjust to rapid changes in the ambient noise. The first goal of this dissertation is to develop methods that yield a more reliable ANC performance with respect to the fit of a headphone for individual users. The methods shall allow calibration of ANC headphones in the field as well as a configuration of the ANC transfer characteristic. The second goal is to develop efficient adaptive algorithms that yield a close-to-optimal ANC performance, are suitable for relevant audio codecs, and require almost no prior knowledge about the acoustic system.