Slide
Centre Interdisciplinaire
de Recherche et d’Innovation
en Cybersécurité et Société
de Recherche et d’Innovation
en Cybersécurité et Société
1.
Guimaraes, H. R.; Abdollahi, M.; Zhu, Y.; Maucourt, S.; Coallier, N.; Giovenazzo, P.; Falk, T. H.
Benchmarking Self-Supervised Audio Representations for IoT-Enabled Acoustic Beehive Monitoring Journal Article
In: IEEE Internet of Things Journal, vol. 12, no. 21, pp. 45000–45010, 2025, ISSN: 23274662 (ISSN).
Abstract | Links | BibTeX | Tags: Acoustics, Audio acoustics, Audio representation, Beehive monitoring, Benchmarking, Bioacoustics, Computer vision applications, Deep learning, Honeybee, honeybees, Internet of Things (IoT), IoT, Labeled data, Performance, Real time systems, Self-supervised learning, self-supervised learning (SSL), Societal benefits, Speech applications, Speech recognition, Supervised learning, Universal feature extractors
@article{guimaraesBenchmarkingSelfSupervisedAudio2025,
title = {Benchmarking Self-Supervised Audio Representations for IoT-Enabled Acoustic Beehive Monitoring},
author = {H. R. Guimaraes and M. Abdollahi and Y. Zhu and S. Maucourt and N. Coallier and P. Giovenazzo and T. H. Falk},
url = {https://www.scopus.com/pages/publications/105013592730?origin=resultslist},
doi = {10.1109/JIOT.2025.3599483},
issn = {23274662 (ISSN)},
year = {2025},
date = {2025-01-01},
journal = {IEEE Internet of Things Journal},
volume = {12},
number = {21},
pages = {45000–45010},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
abstract = {Self-supervised learning (SSL) has enabled the development of universal feature extractors that have redefined the performance envelope of computer vision and speech applications. Recent works have started to explore SSL in other domains, including bioacoustics, which could have significant societal benefits. Honeybees (Apis mellifera), for example, are crucial pollinators contributing to one-third of global food production. However, massive colony losses in recent years have raised concerns. Traditional hive monitoring methods rely on intrusive visual inspections by beekeepers, which can further disrupt colony dynamics. As such, Internet of Things (IoT)-based automated monitoring systems have emerged, integrating environmental and bioacoustic sensing to enable real-time, noninvasive hive assessment. In this work, we introduce a comprehensive evaluation and benchmarking of general-purpose and bioacoustic audio representations that generalize across various tasks in IoT-enabled acoustic beehive monitoring, even with limited labeled data. Herein, fourteen models are evaluated across four critical tasks: beehive state detection, beehive strength assessment, buzzing identification, and beekeeper voice activity detection. Reported results demonstrate the strong generalizability of existing representations, paving the way for advanced, scalable honeybee colony monitoring and preservation. © 2014 IEEE.},
keywords = {Acoustics, Audio acoustics, Audio representation, Beehive monitoring, Benchmarking, Bioacoustics, Computer vision applications, Deep learning, Honeybee, honeybees, Internet of Things (IoT), IoT, Labeled data, Performance, Real time systems, Self-supervised learning, self-supervised learning (SSL), Societal benefits, Speech applications, Speech recognition, Supervised learning, Universal feature extractors},
pubstate = {published},
tppubtype = {article}
}
Self-supervised learning (SSL) has enabled the development of universal feature extractors that have redefined the performance envelope of computer vision and speech applications. Recent works have started to explore SSL in other domains, including bioacoustics, which could have significant societal benefits. Honeybees (Apis mellifera), for example, are crucial pollinators contributing to one-third of global food production. However, massive colony losses in recent years have raised concerns. Traditional hive monitoring methods rely on intrusive visual inspections by beekeepers, which can further disrupt colony dynamics. As such, Internet of Things (IoT)-based automated monitoring systems have emerged, integrating environmental and bioacoustic sensing to enable real-time, noninvasive hive assessment. In this work, we introduce a comprehensive evaluation and benchmarking of general-purpose and bioacoustic audio representations that generalize across various tasks in IoT-enabled acoustic beehive monitoring, even with limited labeled data. Herein, fourteen models are evaluated across four critical tasks: beehive state detection, beehive strength assessment, buzzing identification, and beekeeper voice activity detection. Reported results demonstrate the strong generalizability of existing representations, paving the way for advanced, scalable honeybee colony monitoring and preservation. © 2014 IEEE.
2.
Nouboukpo, A.; Allaoui, M. L.; Allili, M. S.
Multi-scale spatial consistency for deep semi-supervised skin lesion segmentation Journal Article
In: Engineering Applications of Artificial Intelligence, vol. 135, 2024, ISSN: 09521976 (ISSN), (Publisher: Elsevier Ltd).
Abstract | Links | BibTeX | Tags: Deep learning, Dermatology, Image segmentation, Lesion segmentations, Medical imaging, Multi-scales, Semi-supervised, Semi-supervised learning, Skin lesion, Skin lesion segmentation, Spatial consistency, Spatially constrained mixture model, Spatially-constrained mixture models, Supervised learning, UNets, Unlabeled data
@article{nouboukpo_multi-scale_2024,
title = {Multi-scale spatial consistency for deep semi-supervised skin lesion segmentation},
author = {A. Nouboukpo and M. L. Allaoui and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195700182&doi=10.1016%2fj.engappai.2024.108681&partnerID=40&md5=e1cc2b6a1bb0aed530e8c04583c76167},
doi = {10.1016/j.engappai.2024.108681},
issn = {09521976 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {135},
publisher = {Elsevier Ltd},
abstract = {This paper introduces a novel semi-supervised framework, the Multiscale Spatial Consistency Network (MSCNet), for robust semi-supervised skin lesion segmentation. MSCNet uses local and global spatial consistency to leverage a minimal set of labeled data, supplemented by a large number of unlabeled data, to improve segmentation. The model is is based on a single Encoder–Decoder (ED) network, augmented with a Spatially-Constrained Mixture Model (SCMM) to enforce spatial coherence in predictions. To encode the local spatial consistency, a hierarchical superpixel structure is used capture local region context (LRC), bolstering the model capacity to discern fine-grained lesion details. Global consistency is enforced through the SCMM module, which uses a larger context for lesion/background discrimination. In addition, it enables efficient leveraging of the unlabeled data through pseudo-label generation. Experiments demonstrate that the MSCNet outperforms existing state-of-the-art methods in segmenting complex lesions. The MSCNet has an excellent generalization capability, offering a promising direction for semi-supervised medical image segmentation, particularly in scenarios with limited annotated data. The code is available at https://github.com/AdamaTG/MSCNet. © 2024 Elsevier Ltd},
note = {Publisher: Elsevier Ltd},
keywords = {Deep learning, Dermatology, Image segmentation, Lesion segmentations, Medical imaging, Multi-scales, Semi-supervised, Semi-supervised learning, Skin lesion, Skin lesion segmentation, Spatial consistency, Spatially constrained mixture model, Spatially-constrained mixture models, Supervised learning, UNets, Unlabeled data},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a novel semi-supervised framework, the Multiscale Spatial Consistency Network (MSCNet), for robust semi-supervised skin lesion segmentation. MSCNet uses local and global spatial consistency to leverage a minimal set of labeled data, supplemented by a large number of unlabeled data, to improve segmentation. The model is is based on a single Encoder–Decoder (ED) network, augmented with a Spatially-Constrained Mixture Model (SCMM) to enforce spatial coherence in predictions. To encode the local spatial consistency, a hierarchical superpixel structure is used capture local region context (LRC), bolstering the model capacity to discern fine-grained lesion details. Global consistency is enforced through the SCMM module, which uses a larger context for lesion/background discrimination. In addition, it enables efficient leveraging of the unlabeled data through pseudo-label generation. Experiments demonstrate that the MSCNet outperforms existing state-of-the-art methods in segmenting complex lesions. The MSCNet has an excellent generalization capability, offering a promising direction for semi-supervised medical image segmentation, particularly in scenarios with limited annotated data. The code is available at https://github.com/AdamaTG/MSCNet. © 2024 Elsevier Ltd
3.
Nouboukpo, A.; Allaoui, M. L.; Allili, M. S.
Multi-scale spatial consistency for deep semi-supervised skin lesion segmentation Journal Article
In: Engineering Applications of Artificial Intelligence, vol. 135, 2024, ISSN: 09521976 (ISSN).
Abstract | Links | BibTeX | Tags: Deep learning, Dermatology, Image segmentation, Lesion segmentations, Medical imaging, Multi-scales, Semi-supervised, Semi-supervised learning, Skin lesion, Skin lesion segmentation, Spatial consistency, Spatially constrained mixture model, Spatially-constrained mixture models, Supervised learning, UNets, Unlabeled data
@article{nouboukpoMultiscaleSpatialConsistency2024,
title = {Multi-scale spatial consistency for deep semi-supervised skin lesion segmentation},
author = {A. Nouboukpo and M. L. Allaoui and M. S. Allili},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195700182&doi=10.1016%2fj.engappai.2024.108681&partnerID=40&md5=e1cc2b6a1bb0aed530e8c04583c76167},
doi = {10.1016/j.engappai.2024.108681},
issn = {09521976 (ISSN)},
year = {2024},
date = {2024-01-01},
journal = {Engineering Applications of Artificial Intelligence},
volume = {135},
publisher = {Elsevier Ltd},
abstract = {This paper introduces a novel semi-supervised framework, the Multiscale Spatial Consistency Network (MSCNet), for robust semi-supervised skin lesion segmentation. MSCNet uses local and global spatial consistency to leverage a minimal set of labeled data, supplemented by a large number of unlabeled data, to improve segmentation. The model is is based on a single Encoder–Decoder (ED) network, augmented with a Spatially-Constrained Mixture Model (SCMM) to enforce spatial coherence in predictions. To encode the local spatial consistency, a hierarchical superpixel structure is used capture local region context (LRC), bolstering the model capacity to discern fine-grained lesion details. Global consistency is enforced through the SCMM module, which uses a larger context for lesion/background discrimination. In addition, it enables efficient leveraging of the unlabeled data through pseudo-label generation. Experiments demonstrate that the MSCNet outperforms existing state-of-the-art methods in segmenting complex lesions. The MSCNet has an excellent generalization capability, offering a promising direction for semi-supervised medical image segmentation, particularly in scenarios with limited annotated data. The code is available at https://github.com/AdamaTG/MSCNet. © 2024 Elsevier Ltd},
keywords = {Deep learning, Dermatology, Image segmentation, Lesion segmentations, Medical imaging, Multi-scales, Semi-supervised, Semi-supervised learning, Skin lesion, Skin lesion segmentation, Spatial consistency, Spatially constrained mixture model, Spatially-constrained mixture models, Supervised learning, UNets, Unlabeled data},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a novel semi-supervised framework, the Multiscale Spatial Consistency Network (MSCNet), for robust semi-supervised skin lesion segmentation. MSCNet uses local and global spatial consistency to leverage a minimal set of labeled data, supplemented by a large number of unlabeled data, to improve segmentation. The model is is based on a single Encoder–Decoder (ED) network, augmented with a Spatially-Constrained Mixture Model (SCMM) to enforce spatial coherence in predictions. To encode the local spatial consistency, a hierarchical superpixel structure is used capture local region context (LRC), bolstering the model capacity to discern fine-grained lesion details. Global consistency is enforced through the SCMM module, which uses a larger context for lesion/background discrimination. In addition, it enables efficient leveraging of the unlabeled data through pseudo-label generation. Experiments demonstrate that the MSCNet outperforms existing state-of-the-art methods in segmenting complex lesions. The MSCNet has an excellent generalization capability, offering a promising direction for semi-supervised medical image segmentation, particularly in scenarios with limited annotated data. The code is available at https://github.com/AdamaTG/MSCNet. © 2024 Elsevier Ltd
4.
Nouboukpo, A.; Allili, M. S.
Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints Proceedings Article
In: A., Premaratne K. Benferhot S. Antonucci (Ed.): Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS, Florida Online Journals, University of Florida, 2021, ISSN: 23340754, (ISSN: 23340754).
Abstract | Links | BibTeX | Tags: Classification and clustering, Group structure, Learn+, Mixture components, Mixture modeling, Mixtures, Multilevels, Number of class, Prior-knowledge, Semi-supervised learning, Supervised learning, Unlabeled data
@inproceedings{nouboukpo_weakly_2021,
title = {Weakly Semi Supervised learning based Mixture Model With Two-Level Constraints},
author = {A. Nouboukpo and M. S. Allili},
editor = {Premaratne K. Benferhot S. Antonucci A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131143535&doi=10.32473%2fflairs.v34i1.128490&partnerID=40&md5=21cda84d36649f4835be079ea2566717},
doi = {10.32473/flairs.v34i1.128490},
issn = {23340754},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS},
volume = {34},
publisher = {Florida Online Journals, University of Florida},
abstract = {We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes the number of classes is known but does not assume any prior knowledge about the number of mixture components in each class. Therefore, our model: (1) allocates multiple mixture components to individual classes, (2) estimates automatically the number of components of each class, 3) propagates class labels to unlabelled data in a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasets show the robustness and performance of our model over state-of-the-art methods. © 2021 by the authors. All rights reserved.},
note = {ISSN: 23340754},
keywords = {Classification and clustering, Group structure, Learn+, Mixture components, Mixture modeling, Mixtures, Multilevels, Number of class, Prior-knowledge, Semi-supervised learning, Supervised learning, Unlabeled data},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a new weakly supervised approach for classification and clustering based on mixture models. Our approach integrates multi-level pairwise group and class constraints between samples to learn the underlying group structure of the data and propagate (scarce) initial labels to unlabelled data. Our algorithm assumes the number of classes is known but does not assume any prior knowledge about the number of mixture components in each class. Therefore, our model: (1) allocates multiple mixture components to individual classes, (2) estimates automatically the number of components of each class, 3) propagates class labels to unlabelled data in a consistent way to predefined constraints. Experiments on several real-world and synthetic data datasets show the robustness and performance of our model over state-of-the-art methods. © 2021 by the authors. All rights reserved.