The Perception of Phase Intercept Distortion and its Application in Data Augmentation
This addresses data augmentation needs in audio machine learning, but is incremental as it applies a known concept to a new domain.
The paper tackled the problem of phase-intercept distortion in signals, hypothesizing it is imperceptible to humans, and found through experiments that it can improve audio machine learning tasks via data augmentation.
Phase distortion refers to the alteration of the phase relationships between frequencies in a signal, which can be perceptible. In this paper, we discuss a special case of phase distortion known as phase-intercept distortion, which is created by a frequency-independent phase shift. We hypothesize that, though this form of distortion changes a signal's waveform significantly, the distortion is imperceptible. Human-subject experiment results are reported which are consistent with this hypothesis. Furthermore, we discuss how the imperceptibility of phase-intercept distortion can be useful for machine learning, specifically for data augmentation. We conducted multiple experiments using phase-intercept distortion as a novel approach to data augmentation, and obtained improved results for audio machine learning tasks.