DDSC: Dynamic Dual-Signal Curriculum for Data-Efficient Acoustic Scene Classification under Domain Shift
Abstract
Acoustic scene classification (ASC) suffers from device-induced domain shift, especially when labels are limited. Prior work focuses on curriculum-based training schedules that structure data presentation by ordering or reweighting training examples from easy-to-hard to facilitate learning; however, existing curricula are static, fixing the ordering or the weights before training and ignoring that example difficulty and marginal utility evolve with the learned representation. To overcome this limitation, we propose the Dynamic Dual-Signal Curriculum (DDSC), a training schedule that adapts the curriculum online by combining two signals computed each epoch: a domain-invariance signal and a learning-progress signal. A time-varying scheduler fuses these signals into per-example weights that prioritize domain-invariant examples in early epochs and progressively emphasize device-specific cases. DDSC is lightweight, architecture-agnostic, and introduces no additional inference overhead. Under the official DCASE 2024 Task~1 protocol, DDSC consistently improves cross-device performance across diverse ASC baselines and label budgets, with the largest gains on unseen-device splits.
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 paper presents DDSC, a dynamic curriculum learning method that effectively addresses domain shift in acoustic scene classification, showcasing substantial improvements in model performance under low-label conditions. The innovative methodology and rigorous experimental validation position this work as a valuable contribution to the field of machine learning and audio processing.
Methodology
The proposed Dynamic Dual-Signal Curriculum (DDSC) introduces a novel approach to curriculum learning by dynamically adjusting the weights of training examples based on two signals: domain-invariance and learning-progress. This methodology is innovative as it addresses the static nature of previous curriculum learning methods, allowing for a more adaptive learning process that evolves with the model's understanding. The use of prototype entropy for domain-invariance and smoothed loss change for learning progress is a thoughtful integration that enhances the model's ability to generalize across devices.
Results & evaluation
The experiments conducted on the DCASE 2024 Task 1 dataset demonstrate the effectiveness of DDSC across various architectures and label budgets, particularly under low-label conditions. The results indicate significant improvements in accuracy, especially on unseen-device splits, showcasing the method's robustness. The paper provides a comprehensive comparison with existing curriculum learning strategies, reinforcing the advantages of DDSC.
Reproducibility
The paper mentions that code will be released upon acceptance, which is a positive step towards reproducibility. However, the absence of a direct link to a repository or demo limits immediate access to the implementation details. The methodology is described in sufficient detail to allow for replication, but the lack of a public codebase at this stage is a drawback.
Limitations
One limitation is the reliance on the DCASE 2024 dataset, which may not fully represent all real-world scenarios in acoustic scene classification. Additionally, while the method is architecture-agnostic, its performance may vary with different model architectures not tested in this work. The paper could also benefit from a discussion on the computational efficiency of the proposed method in practical applications.
Broader impact
The DDSC methodology has significant implications for improving acoustic scene classification systems, particularly in environments with limited labeled data and varying device characteristics. This approach could enhance applications in smart devices, urban sound monitoring, and assistive technologies for hearing-impaired individuals, leading to more robust and adaptable audio recognition systems. The paper presents DDSC, a dynamic curriculum learning method that effectively addresses domain shift in acoustic scene classification, showcasing substantial improvements in model performance under low-label conditions. The innovative methodology and rigorous experimental validation position this work as a valuable contribution to the field of machine learning and audio processing.
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