  Many spatial filtering algorithms used for voice capture in, e.g.,
teleconferencing applications, can benefit from or even rely on knowledge of
Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been
proposed which, however, suffer from performance degradation under acoustically
adverse conditions or need prior knowledge on the properties of the interfering
sources. While state-of-the-art RTF estimators ignore prior knowledge about the
acoustic enclosure, audio signal processing algorithms for teleconferencing
equipment are often operating in the same or at least a similar acoustic
enclosure, e.g., a car or an office, such that training data can be collected.
In this contribution, we use such data to train Variational Autoencoders (VAEs)
in an unsupervised manner and apply the trained VAEs to enhance imprecise RTF
estimates. Furthermore, a hybrid between classic RTF estimation and the trained
VAE is investigated. Comprehensive experiments with real-world data confirm the
efficacy for the proposed method.
