Adaptive filter algorithms and misalignment criteria for blind binaural channel identification in hearing-aids

Blind channel identification (BCI) is known from communications as a bitrate saving alternative to the more conventional pilot-based identification of source-receiver transfer functions. In multi-microphone signal processing, however, the source signal is per se not available for acoustic channel identification. The emerging bin-aural signal processing discipline is a good example where BCI may thus be considered necessary for tasks such as acoustic localization or equalization. This paper evaluates current algorithms from BCI in order to make a pair of hearing-aids aware of time-varying head-related transfer functions without knowledge of the source signal. Thereby, unrestricted bitrate is assumed to share both ear signals in a central processor. Using simulations of binaural signals, we explore four two-channel adaptive identification algorithms and three evaluation criteria as candidates for hearing-aids. Depending on the criteria, the study shows striking similarities for specific configurations of algorithm and data, but also reveals important differences.