  In this paper, we introduce a data-compilation ensemble, primarily intended
to serve as a resource for researchers in the field of dereverberation,
particularly for data-driven approaches. It comprises speech and song samples,
together with acoustic guitar sounds, with original annotations pertinent to
emotion recognition and Music Information Retrieval (MIR). Moreover, it
includes a selection of impulse response (IR) samples with varying
Reverberation Time (RT) values, providing a wide range of conditions for
evaluation. This data-compilation can be used together with provided Python
scripts, for generating auralized data ensembles in different sizes: tiny,
small, medium and large. Additionally, the provided metadata annotations also
allow for further analysis and investigation of the performance of
dereverberation algorithms under different conditions. All data is licensed
under Creative Commons Attribution 4.0 International License.
