Towards Lightweight Adaptation of Speech Enhancement Models in Real-World Environments
Abstract
Recent studies have shown that post-deployment adaptation can improve the robustness of speech enhancement models in unseen noise conditions. However, existing methods often incur prohibitive computational and memory costs, limiting their suitability for on-device deployment. In this work, we investigate model adaptation in realistic settings with dynamic acoustic scene changes and propose a lightweight framework that augments a frozen backbone with low-rank adapters updated via self-supervised training. Experiments on sequential scene evaluations spanning 111 environments across 37 noise types and three signal-to-noise ratio ranges, including the challenging [-8, 0] dB range, show that our method updates fewer than 1% of the base model's parameters while achieving an average 1.51 dB SI-SDR improvement within only 20 updates per scene. Compared to state-of-the-art approaches, our framework achieves competitive or superior perceptual quality with smoother and more stable convergence, demonstrating its practicality for lightweight on-device adaptation of speech enhancement models under real-world acoustic conditions.
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 a lightweight self-supervised adaptation framework for speech enhancement models that efficiently updates model parameters in real-world acoustic environments. This work represents a significant step toward making advanced speech processing technologies more accessible and practical for on-device applications.
Methodology
The paper presents a novel self-supervised adaptation framework leveraging low-rank adapters for speech enhancement models. This approach addresses the critical issue of adapting models to dynamic acoustic environments without the need for extensive parameter updates, which is a significant advancement over traditional methods that require fine-tuning a large number of parameters. The methodology is well-structured, clearly outlining the adaptation process and the rationale behind using low-rank adapters. However, the paper could benefit from a more detailed explanation of the self-supervised training process and how pseudo-targets are generated.
Results & evaluation
The experimental setup is robust, involving evaluations across 111 environments and multiple noise types, which strengthens the validity of the results. The metrics used (PESQ, STOI, and SI-SDR) are appropriate for assessing speech enhancement quality. The results demonstrate that the proposed method achieves competitive performance compared to state-of-the-art approaches while maintaining a significantly lower computational footprint. However, the paper lacks a detailed comparison of the proposed method with other lightweight adaptation techniques beyond RemixIT, which could provide a more comprehensive view of its relative performance.
Reproducibility
The paper provides a thorough description of the experimental setup, including model architectures, training procedures, and dataset details, which enhances reproducibility. However, the absence of publicly available code or datasets limits the ability for other researchers to replicate the results directly. Including a link to a repository or providing access to the datasets used would significantly improve reproducibility.
Limitations
One limitation of the proposed method is its reliance on the quality of the pseudo-targets generated during adaptation. If the initial model is not sufficiently robust, the adaptation may not yield optimal results. Additionally, while the method shows promise for dynamic environments, its performance in highly variable or extreme conditions remains to be tested. The paper also does not address the potential computational overhead associated with the self-supervised training phase.
Broader impact
The proposed lightweight adaptation framework has significant implications for real-world applications, particularly in mobile and edge computing environments where computational resources are limited. By enabling effective on-device adaptation of speech enhancement models, this work could improve accessibility for users of hearing aids and other assistive listening devices in diverse acoustic settings. The approach could also be extended to other domains requiring real-time audio processing, enhancing the practicality of machine learning solutions in everyday applications. The main contribution of this paper is the introduction of a lightweight self-supervised adaptation framework for speech enhancement models that efficiently updates model parameters in real-world acoustic environments. This work represents a significant step toward making advanced speech processing technologies more accessible and practical for on-device applications.
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