BioME: A Resource-Efficient Bioacoustic Foundational Model for IoT Applications
Abstract
Passive acoustic monitoring has become a key strategy in biodiversity assessment, conservation, and behavioral ecology, especially as Internet-of-Things (IoT) devices enable continuous in situ audio collection at scale. While recent self-supervised learning (SSL)-based audio encoders, such as BEATs and AVES, have shown strong performance in bioacoustic tasks, their computational cost and limited robustness to unseen environments hinder deployment on resource-constrained platforms. In this work, we introduce BioME, a resource-efficient audio encoder designed for bioacoustic applications. BioME is trained via layer-to-layer distillation from a high-capacity teacher model, enabling strong representational transfer while reducing the parameter count by 75%. To further improve ecological generalization, the model is pretrained on multi-domain data spanning speech, environmental sounds, and animal vocalizations. A key contribution is the integration of modulation-aware acoustic features via FiLM conditioning, injecting a DSP-inspired inductive bias that enhances feature disentanglement in low-capacity regimes. Across multiple bioacoustic tasks, BioME matches or surpasses the performance of larger models, including its teacher, while being suitable for resource-constrained IoT deployments. For reproducibility, code and pretrained checkpoints are publicly available.
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 BioME, a resource-efficient audio encoder designed for bioacoustic applications, which achieves state-of-the-art performance while significantly reducing computational costs. This work represents a meaningful advancement in the field of audio representation learning, particularly in the context of ecological monitoring, and demonstrates the potential of integrating traditional signal processing techniques with modern deep learning approaches.
Methodology
The methodology presented in this paper is robust and innovative, leveraging layer-to-layer knowledge distillation to create a compact audio encoder, BioME, that retains high performance on bioacoustic tasks. The integration of modulation-aware features via FiLM conditioning is particularly noteworthy, as it introduces a novel inductive bias that enhances feature disentanglement, which is crucial for effective audio representation in resource-constrained environments. The use of a multi-domain pretraining strategy further strengthens the model's generalization capabilities across diverse bioacoustic tasks.
Results & evaluation
The experimental evaluation is thorough, utilizing a variety of datasets and benchmarks, including the BEANS benchmark for bioacoustic tasks. The results demonstrate that BioME outperforms larger models, including its teacher model, in several scenarios, particularly in resource-constrained setups. The ablation studies provide clear insights into the contributions of different architectural components, validating the effectiveness of the proposed modifications and confirming the model's robustness across various tasks.
Reproducibility
The authors have made efforts to ensure reproducibility by providing code and pretrained checkpoints publicly. However, the paper lacks specific URLs for accessing these resources, which could enhance reproducibility further. Detailed descriptions of the datasets and training procedures are included, which aids in replicating the experiments.
Limitations
One limitation is the potential overfitting observed in larger model configurations, particularly in specific tasks like binary classification for beehive monitoring. Additionally, while the model shows promise, the paper does not extensively discuss the trade-offs between model size and performance in all contexts, which could be important for practical applications.
Broader impact
The implications of this work are significant for ecological monitoring and conservation efforts, as it enables efficient and effective bioacoustic monitoring using resource-constrained IoT devices. The advancements in self-supervised learning for audio representation can also influence broader applications in machine learning, particularly in fields requiring real-time audio processing and analysis. The main contribution of this paper is the introduction of BioME, a resource-efficient audio encoder designed for bioacoustic applications, which achieves state-of-the-art performance while significantly reducing computational costs. This work represents a meaningful advancement in the field of audio representation learning, particularly in the context of ecological monitoring, and demonstrates the potential of integrating traditional signal processing techniques with modern deep learning approaches.
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