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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14571 |
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| _version_ | 1866917289750691840 |
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| author | Krishnan, Venkatakrishnan Vaidyanathapuram Condit-Schultz, Nathaniel |
| author_facet | Krishnan, Venkatakrishnan Vaidyanathapuram Condit-Schultz, Nathaniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14571 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Perception of Phase Intercept Distortion and its Application in Data Augmentation Krishnan, Venkatakrishnan Vaidyanathapuram Condit-Schultz, Nathaniel Signal Processing Machine Learning Audio and Speech Processing 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. |
| title | The Perception of Phase Intercept Distortion and its Application in Data Augmentation |
| topic | Signal Processing Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.14571 |