  While log-amplitude mel-spectrogram has widely been used as the feature
representation for processing speech based on deep learning, the effectiveness
of another aspect of speech spectrum, i.e., phase information, was shown
recently for tasks such as speech enhancement and source separation. In this
study, we extensively investigated the effectiveness of including phase
information of signals for eight audio classification tasks. We constructed a
learnable front-end that can compute the phase and its derivatives based on a
time-frequency representation with mel-like frequency axis. As a result,
experimental results showed significant performance improvement for musical
pitch detection, musical instrument detection, language identification, speaker
identification, and birdsong detection. On the other hand, overfitting to the
recording condition was observed for some tasks when the instantaneous
frequency was used. The results implied that the relationship between the phase
values of adjacent elements is more important than the phase itself in audio
classification.
