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Audio-JEPA introduces a self-supervised learning framework that leverages the Joint-Embedding Predictive Architecture to predict latent spectrogram representations rather than reconstructing raw audio, significantly reducing data requirements compared to standard models like wav2vec 2.0. By utilizing a Vision Transformer backbone for mel-spectrogram patches, this approach achieves comparable performance across speech, music, and environmental sound tasks with minimal hyper-parameter tuning. The method represents a notable efficiency gain in audio representation learning by decoupling the need for massive labeled datasets or raw signal reconstruction.
Audio-JEPA: Joint-Embedding Predictive Architecture for Audio Representation Learning
arXiv:2507.02915v2 Announce Type: replace-cross Abstract: Building on the Joint-Embedding Predictive Architecture (JEPA) paradigm, a recent self-supervised learning framework that predicts latent representations of masked regions in high-level feature spaces, we propose Audio-JEPA (Audio Joint-Embedding Predictive Architecture), tailored specifically for audio data. Audio-JEPA uses a simple Vision Transformer backbone to predict latent representations of masked spectrogram patches rather than reconstructing raw audio. We pre-train on unlabeled AudioSet clips (10s, 32kHz) with random patch masking on mel-spectrograms. We evaluate on the X-ARES suite covering speech, music, and environmental sound tasks. Although our implementation is a straightforward translation of the original model to audio, the results still show comparable performance to wav2vec 2.0 and data2vec while using less than one-fifth of their training data and with no hyper-parameter tuning. All code and pretrained checkpoints are available on GitHub.
Abstract:Building on the Joint-Embedding Predictive Architecture (JEPA) paradigm, a recent self-supervised learning framework that predicts latent representations of masked regions in high-level feature spaces, we propose Audio-JEPA (Audio Joint-Embedding Predictive Architecture), tailored specifically for audio data. Audio-JEPA uses a simple Vision Transformer backbone to predict latent representations of masked spectrogram patches rather than reconstructing raw audio. We pre-train on unlabeled AudioSet clips (10s, 32kHz) with random patch masking on mel-spectrograms. We evaluate on the X-ARES suite covering speech, music, and environmental sound tasks. Although our implementation is a straightforward translation of the original model to audio, the results still show comparable performance to wav2vec 2.0 and data2vec while using less than one-fifth of their training data and with no hyper-parameter tuning. All code and pretrained checkpoints are available on GitHub.
Sound (cs.SD); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS); Signal Processing (eess.SP)
arXiv:2507.02915 [cs.SD]
(or arXiv:2507.02915v2 [cs.SD] for this version)
https://doi.org/10.48550/arXiv.2507.02915 Focus to learn more arXiv-issued DOI via DataCite
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