المؤلفون: Arrabito, Robert, Hou, Ming, Fischmeister, Sebastian, Falk, Tiago H., Willoughby, Hannah, Cameron, Madison, Foley, Liam, Normandin, Sarah, Banbury, Simon

المصدر: 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS) Human-Machine Systems (ICHMS), 2024 IEEE 4th International Conference on. :1-4 May, 2024

Relation: 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)

المؤلفون: Moinnereau, Marc-Antoine, Falk, Tiago H.

المصدر: 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS) Human-Machine Systems (ICHMS), 2024 IEEE 4th International Conference on. :1-6 May, 2024

Relation: 2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)

المؤلفون: Abdollahi, Mahsa, Zhu, Yi, Guimarães, Heitor R., Coallier, Nico, Maucourt, Ségolène, Giovenazzo, Pierre, Falk, Tiago H.

مصطلحات موضوعية: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound

الوصف: In this paper, we present a multimodal dataset obtained from a honey bee colony in Montr\'eal, Quebec, Canada, spanning the years of 2021 to 2022. This apiary comprised 10 beehives, with microphones recording more than 2000 hours of high quality raw audio, and also sensors capturing temperature, and humidity. Periodic hive inspections involved monitoring colony honey bee population changes, assessing queen-related conditions, and documenting overall hive health. Additionally, health metrics, such as Varroa mite infestation rates and winter mortality assessments were recorded, offering valuable insights into factors affecting hive health status and resilience. In this study, we first outline the data collection process, sensor data description, and dataset structure. Furthermore, we demonstrate a practical application of this dataset by extracting various features from the raw audio to predict colony population using the number of frames of bees as a proxy.

URL الوصول: http://arxiv.org/abs/2406.03657

المؤلفون: Zhu, Yi, Abdollahi, Mahsa, Maucourt, Segolene, Coallier, Nico, Guimaraes, Heitor R., Giovenazzo, Pierre, Falk, Tiago H.

المصدر: 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Metrology for Agriculture and Forestry (MetroAgriFor), 2023 IEEE International Workshop on. :657-662 Nov, 2023

Relation: 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

المؤلفون: Guimaraes, Heitor R., Abdollahi, Mahsa, Zhu, Yi, Maucourt, Segolene, Coallier, Nico, Giovenazzo, Pierre, Falk, Tiago H.

المصدر: 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) Metrology for Agriculture and Forestry (MetroAgriFor), 2023 IEEE International Workshop on. :663-667 Nov, 2023

Relation: 2023 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

المؤلفون: Guimarães, Heitor R., Pimentel, Arthur, Avila, Anderson R., Rezagholizadeh, Mehdi, Chen, Boxing, Falk, Tiago H.

مصطلحات موضوعية: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound

الوصف: Self-supervised speech representation learning enables the extraction of meaningful features from raw waveforms. These features can then be efficiently used across multiple downstream tasks. However, two significant issues arise when considering the deployment of such methods ``in-the-wild": (i) Their large size, which can be prohibitive for edge applications; and (ii) their robustness to detrimental factors, such as noise and/or reverberation, that can heavily degrade the performance of such systems. In this work, we propose RobustDistiller, a novel knowledge distillation mechanism that tackles both problems jointly. Simultaneously to the distillation recipe, we apply a multi-task learning objective to encourage the network to learn noise-invariant representations by denoising the input. The proposed mechanism is evaluated on twelve different downstream tasks. It outperforms several benchmarks regardless of noise type, or noise and reverberation levels. Experimental results show that the new Student model with 23M parameters can achieve results comparable to the Teacher model with 95M parameters. Lastly, we show that the proposed recipe can be applied to other distillation methodologies, such as the recent DPWavLM. For reproducibility, code and model checkpoints will be made available at \mbox{\url{https://github.com/Hguimaraes/robustdistiller}}.
Comment: Under review on IEEE Transactions on Audio, Speech, and Language Processing (2024)

URL الوصول: http://arxiv.org/abs/2403.08654

المؤلفون: Guimaraes, Heitor R., Zhu, Yi, Mengara, Orson, Avila, Anderson R., Falk, Tiago H.

المصدر: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2023 IEEE International Conference on. :1672-1677 Oct, 2023

Relation: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

المؤلفون: Tiwari, Abhishek, Wu, Gloria, Innanen, Katrina, Mahnam, Amin, Moineau, Bastien, Falk, Tiago H.

المصدر: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2023 IEEE International Conference on. :4746-4751 Oct, 2023

Relation: 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

المؤلفون: Abdollahi, Mahsa, Coallier, Nico, Giovenazzo, Pierre, Falk, Tiago H.

المصدر: 2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Electrical and Computer Engineering (CCECE), 2023 IEEE Canadian Conference on. :320-323 Sep, 2023

Relation: 2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

المؤلفون: Guimaraes, Heitor R., Pimentel, Arthur, Avila, Anderson R., Rezagholizadeh, Mehdi, Chen, Boxing, Falk, Tiago H.

المصدر: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023

Relation: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)