  The estimation of reverberation time from real-world signals plays a central
role in a wide range of applications. In many scenarios, acoustic conditions
change over time which in turn requires the estimate to be updated
continuously. Previously proposed methods involving deep neural networks were
mostly designed and tested under the assumption of static acoustic conditions.
In this work, we show that these approaches can perform poorly in dynamically
evolving acoustic environments. Motivated by a recent trend towards
data-centric approaches in machine learning, we propose a novel way of
generating training data and demonstrate, using an existing deep neural network
architecture, the considerable improvement in the ability to follow temporal
changes in reverberation time.
