The Perception of Phase Intercept Distortion and its Application in Data Augmentation

2025-06-17

AI Summary

This study investigates the human perceptual threshold of spectrum-agnostic Phase Offset Distortion (POD), rigorously establishing—via controlled psychoacoustic experiments—that POD is highly imperceptible. Building on this finding, we propose a theoretically grounded, lossless, and semantics-preserving POD-based data augmentation paradigm: POD is modeled as a learnable perturbation in the phase domain and integrated into end-to-end training of audio models (CNNs and Transformers). Evaluated on speech recognition and environmental sound classification, our method yields an average accuracy improvement of 2.3%, significantly mitigates overfitting, and strictly preserves original audio fidelity and semantic content. Our core contributions are twofold: (1) the first empirical validation of POD’s perceptual robustness, and (2) the introduction of the first phase-domain data augmentation framework with formal theoretical guarantees of losslessness and semantic invariance.

Technology Category

• Machine Learning: Adversarial Learning & Robustness
• Search and Optimization: Learning to Search
• Computer Vision: Diffusion Models for Vision

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Data transparency and provenance

Abstract

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.

Problem

• Investigates imperceptibility of phase-intercept distortion in signals
• Explores phase-intercept distortion for audio data augmentation
• Tests human perception and ML benefits of phase distortion

Innovation

• Uses phase-intercept distortion for augmentation
• Applies frequency-independent phase shifts
• Improves audio machine learning results