WHAMR! is a dataset for noisy and reverberant speech separation. It extends WHAM! by introducing synthetic reverberation to the
speech sources in addition to the existing noise. Room impulse responses were generated and convolved using
pyroomacoustics. Reverberation times were chosen to approximate domestic and classroom environments (expected to be similar to the restaurants and coffee shops where the WHAM! noise was collected), and
further classified as high, medium, and low reverberation based on a
qualitative assessment of the mixture’s noise recording.