  The ability to generalize to a wide range of recording devices is a crucial
performance factor for audio classification models. The characteristics of
different types of microphones introduce distributional shifts in the digitized
audio signals due to their varying frequency responses. If this domain shift is
not taken into account during training, the model's performance could degrade
severely when it is applied to signals recorded by unseen devices. In
particular, training a model on audio signals recorded with a small number of
different microphones can make generalization to unseen devices difficult. To
tackle this problem, we convolve audio signals in the training set with
pre-recorded device impulse responses (DIRs) to artificially increase the
diversity of recording devices. We systematically study the effect of DIR
augmentation on the task of Acoustic Scene Classification using CNNs and Audio
Spectrogram Transformers. The results show that DIR augmentation in isolation
performs similarly to the state-of-the-art method Freq-MixStyle. However, we
also show that DIR augmentation and Freq-MixStyle are complementary, achieving
a new state-of-the-art performance on signals recorded by devices unseen during
training.
