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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.03637 |
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| _version_ | 1866910220166365184 |
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| author | Yamamoto, Kohei Okusa, Kosuke |
| author_facet | Yamamoto, Kohei Okusa, Kosuke |
| contents | Transformer-based audio self-supervised learning (SSL) models commonly use spectrograms, vision-style Transformers, and masked modeling objectives. However, convolutional patchification with temporal downsampling lowers the effective Nyquist frequency and introduces aliasing, while naïve low-pass filtering may remove task-relevant high-frequency cues. We present AaSP, an aliasing-aware self-supervised pre-training framework for audio spectrogram transformers. AaSP combines an aliasing-aware patch representation, teacher-student masked modeling, a cross-attention predictor, and multi-mask contrastive regularization to learn representations that integrate features from alias-prone modulation bands while remaining stable across masked views. Its patch-embedding module, Aliasing-aware Patch Embedding (AaPE), augments standard patch tokens with features from alias-prone modulation bands using a band-limited complex sinusoidal kernel with a two-sided exponential window. The kernel's frequency and decay parameters are estimated from the input, enabling adaptive subband analysis whose outputs are fused with standard patch tokens. We pre-train on AudioSet and evaluate the learned representations by fine-tuning and linear evaluation on acoustic/environmental, speech, and music recognition benchmarks. Under fine-tuning, the full AaSP framework achieves state-of-the-art results on AS-20K, ESC-50, and NSynth among compared self-supervised baselines, while remaining competitive elsewhere. Linear evaluation shows a similar trend, including gains on US8K and NSynth. Overall, AaSP learns representations that are more stable under aliasing-sensitive temporal perturbations and competitive for downstream transfer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_03637 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AaSP: Aliasing-aware Self-Supervised Pre-Training for Audio Spectrogram Transformers Yamamoto, Kohei Okusa, Kosuke Sound Machine Learning Transformer-based audio self-supervised learning (SSL) models commonly use spectrograms, vision-style Transformers, and masked modeling objectives. However, convolutional patchification with temporal downsampling lowers the effective Nyquist frequency and introduces aliasing, while naïve low-pass filtering may remove task-relevant high-frequency cues. We present AaSP, an aliasing-aware self-supervised pre-training framework for audio spectrogram transformers. AaSP combines an aliasing-aware patch representation, teacher-student masked modeling, a cross-attention predictor, and multi-mask contrastive regularization to learn representations that integrate features from alias-prone modulation bands while remaining stable across masked views. Its patch-embedding module, Aliasing-aware Patch Embedding (AaPE), augments standard patch tokens with features from alias-prone modulation bands using a band-limited complex sinusoidal kernel with a two-sided exponential window. The kernel's frequency and decay parameters are estimated from the input, enabling adaptive subband analysis whose outputs are fused with standard patch tokens. We pre-train on AudioSet and evaluate the learned representations by fine-tuning and linear evaluation on acoustic/environmental, speech, and music recognition benchmarks. Under fine-tuning, the full AaSP framework achieves state-of-the-art results on AS-20K, ESC-50, and NSynth among compared self-supervised baselines, while remaining competitive elsewhere. Linear evaluation shows a similar trend, including gains on US8K and NSynth. Overall, AaSP learns representations that are more stable under aliasing-sensitive temporal perturbations and competitive for downstream transfer. |
| title | AaSP: Aliasing-aware Self-Supervised Pre-Training for Audio Spectrogram Transformers |
| topic | Sound Machine Learning |
| url | https://arxiv.org/abs/2512.03637 |