  In recent years, the introduction of neural networks (NNs) into the field of
speech enhancement has brought significant improvements. However, many of the
proposed methods are quite demanding in terms of computational complexity and
memory footprint. For the application in dedicated communication devices, such
as speakerphones, hands-free car systems, or smartphones, efficiency plays a
major role along with performance. In this context, we present an efficient,
high-performance hybrid joint acoustic echo control and noise suppression
system, whereby our main contribution is the postfilter NN, performing both
noise and residual echo suppression. The preservation of nearend speech is
improved by a Bark-scale auditory filterbank for the NN postfilter. The
proposed hybrid method is benchmarked with state-of-the-art methods and its
effectiveness is demonstrated on the ICASSP 2023 AEC Challenge blind test set.
We demonstrate that it offers high-quality nearend speech preservation during
both double-talk and nearend speech conditions. At the same time, it is capable
of efficient removal of echo leaks, achieving a comparable performance to
already small state-of-the-art models such as the end-to-end DeepVQE-S, while
requiring only around 10 % of its computational complexity. This makes it
easily realtime implementable on a speakerphone device.
