WavJEPA: Semantic learning unlocks robust audio foundation models for raw waveforms
Abstract
Learning audio representations from raw waveforms overcomes key limitations of spectrogram-based audio representation learning, such as the long latency of spectrogram computation and the loss of phase information. Yet, while self-supervised speech representation learning from raw waveforms has been remarkably successful, these approaches have not achieved similar feats for general-purpose audio representation learning from waveforms. Here, we propose WavJEPA, a waveform-based version of the Joint-Embedding Predictive Architecture. WavJEPA leverages high-level semantic representation learning to tackle the shortcomings of representation learning at the speech unit or token level. We show that this approach substantially outperforms state-of-the-art time-domain audio foundation models across a wide variety of downstream benchmark tasks, while requiring considerably fewer computational resources. Additionally, to overcome the performance drop that time-domain models typically exhibit in noisy and reverberant real-world acoustic environments, we present WavJEPA-Nat. WavJEPA-Nat is a multi-channel extension of the WavJEPA architecture trained on simulated naturalistic scenes. We find that WavJEPA-Nat is highly robust to reverberation and noise. These results highlight the feasibility and computational efficiency of general-purpose audio representation learning from raw waveforms, showcasing the potential for low-latency, robust time-domain audio foundation models for real-world applications.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of WavJEPA, a novel framework for robust audio representation learning from raw waveforms that significantly outperforms existing models while requiring fewer computational resources. This work not only advances the state of audio foundation models but also emphasizes the importance of semantic learning in overcoming traditional limitations associated with audio representation tasks.
Methodology
The methodology presented in WavJEPA is innovative, leveraging a novel Joint-Embedding Predictive Architecture (JEPA) to learn audio representations directly from raw waveforms. This approach addresses key limitations of traditional spectrogram-based methods, such as latency and loss of phase information. The architecture includes a waveform encoder, context encoder, target encoder, and predictor, which collectively enable the model to predict latent representations from temporally distributed context. The introduction of WavJEPA-Nat further enhances robustness against noise and reverberation by utilizing a multi-channel approach, showcasing a thoughtful adaptation of the framework to real-world challenges.
Results & evaluation
The experiments conducted are comprehensive, evaluating WavJEPA and WavJEPA-Nat across multiple benchmark suites (HEAR and ARCH). The paper reports substantial improvements over state-of-the-art models in various tasks, demonstrating the effectiveness of the proposed approach. The use of extensive ablation studies to analyze the impact of different parameters on performance adds rigor to the experimental evaluation. However, the paper could benefit from more detailed comparisons with other recent models to contextualize its contributions further.
Reproducibility
The paper provides detailed specifications of the training process, including hyperparameters, model architecture, and data preprocessing steps, which enhances reproducibility. However, the lack of a public code repository or demo URL limits the ability for others to easily replicate the results. Including a link to a GitHub repository or similar would significantly improve this aspect.
Limitations
While the results are promising, the paper acknowledges that WavJEPA's performance on speech tasks is not as strong as larger models trained on extensive speech datasets. Additionally, the reliance on simulated naturalistic scenes may not fully capture the complexity of real-world audio environments. Future work should explore the integration of diverse datasets to enhance generalization capabilities.
Broader impact
The advancements presented in WavJEPA have significant implications for real-time audio applications, including speech recognition, audio scene analysis, and generative audio tasks. The ability to learn robust representations from raw waveforms opens avenues for low-latency models suitable for deployment in various real-world scenarios, potentially transforming how audio processing tasks are approached in machine learning. The main contribution of this paper is the introduction of WavJEPA, a novel framework for robust audio representation learning from raw waveforms that significantly outperforms existing models while requiring fewer computational resources. This work not only advances the state of audio foundation models but also emphasizes the importance of semantic learning in overcoming traditional limitations associated with audio representation tasks.
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