Publications

MetricGAN+: An Improved Version of MetricGAN for Speech Enhancement

Cheng Yu

Tsun-An Hsieh

Xugang Lu

Yu Tsao

The discrepancy between the cost function used for training a speech enhancement model and human auditory perception usually makes the quali… (voir plus)ty of enhanced speech unsatisfactory. Objective evaluation metrics which consider human perception can hence serve as a bridge to reduce the gap. Our previously proposed MetricGAN was designed to optimize objective metrics by connecting the metric with a discriminator. Because only the scores of the target evaluation functions are needed during training, the metrics can even be non-differentiable. In this study, we propose a MetricGAN+ in which three training techniques incorporating domain-knowledge of speech processing are proposed. With these techniques, experimental results on the VoiceBank-DEMAND dataset show that MetricGAN+ can increase PESQ score by 0.3 compared to the previous MetricGAN and achieve state-of-the-art results (PESQ score = 3.15).

2021-12-31

Conference of the International Speech Communication Association (inconnu)

doi.org

arxiv.org

MetricGAN-U: Unsupervised Speech Enhancement/ Dereverberation Based Only on Noisy/ Reverberated Speech

Szu-wei Fu

Cheng Yu

Kuo-Hsuan Hung

Mirco Ravanelli

Yu Tsao

Most of the deep learning-based speech enhancement models are learned in a supervised manner, which implies that pairs of noisy and clean sp… (voir plus)eech are required during training. Consequently, several noisy speeches recorded in daily life cannot be used to train the model. Although certain unsupervised learning frameworks have also been proposed to solve the pair constraint, they still require clean speech or noise for training. Therefore, in this paper, we propose MetricGAN-U, which stands for MetricGAN-unsupervised, to further release the constraint from conventional unsupervised learning. In MetricGAN-U, only noisy speech is required to train the model by optimizing non-intrusive speech quality metrics. The experimental results verified that MetricGAN-U outperforms baselines in both objective and subjective metrics.

2021-12-31

IEEE International Conference on Acoustics, Speech and Signal Processing (inconnu)

doi.org

arxiv.org

Multilingual Language Model Adaptive Fine-Tuning: A Study on African Languages

Jesujoba Oluwadara Alabi

David Ifeoluwa Adelani

Marius Mosbach

Dietrich Klakow

and XLM-R) and three NLP tasks (NER, news topic classiﬁcation, and sentiment classiﬁcation) shows that our approach is competitive to ap… (voir plus)plying LAFT on individual languages while requiring signiﬁcantly less disk space. Finally, we show that our adapted PLM also improves the zero-shot cross-lingual transfer abilities of parameter efﬁcient ﬁne-tuning methods.

2021-12-31

arXiv.org (prépublication)

doi.org

NeoRS: a neonatal resting state fMRI data preprocessing pipeline

V. Enguix

J. Kenley

D. Luck

J. Cohen-Adad

G.A. Lodygensky

Resting state fMRI (rsfMRI) has been shown to be a promising tool to study intrinsic functional connectivity and assess its integrity in cer… (voir plus)ebral development. In neonates, where fMRI is limited to few paradigms, rsfMRI was shown to be a relevant tool to explore regional interactions of brain networks. However, to identify the resting state networks, data needs to be carefully processed. Because of the non-collaborative nature of the neonates, the differences in brain size and the reversed contrast compared to adults, neonates can't be processed with the existing adult pipelines. Therefore, we developed NeoRS. The main processing steps include atlas registration, skull tripping, segmentation, slice timing and head motion correction and confounds regression. To address the specificity of neonatal brain imaging, particular attention was given to registration including neonatal atlas type and parameters, such as brain size variations, and contrast differences compared to adults. Furthermore, head motion was scrutinized and optimized, as it is a major issue when processing neonatal data. The pipeline includes visual quality control assessment checkpoints. To assess its effectiveness, we used the data from the Baby Connectome Project including 10 neonates. NeoRS was designed to work on both multi-band and single-band acquisitions and is applicable on smaller datasets. It also includes popular functional connectivity analysis features such as seed based correlations. Language, default mode, dorsal attention, visual, ventral attention, motor and fronto parietal networks were evaluated. The different analyzed networks were in agreement with previously published studies in the neonate. NeoRS is coded in Matlab, it is open-source and available on https://github.com/venguix/NeoRS. NeoRS allows robust image processing of the neonatal rsfMRI data that can be readily customized to different datasets.

2021-12-31

Frontiers in Neuroinformatics (publié)

doi.org

arxiv.org

Optimizing deep learning for Magnetoencephalography (MEG): From sensory perception to sex prediction and brain fingerprinting

Arthur Dehgan

Irina Rish

Karim Jerbi

2021-12-31

2022 Conference on Cognitive Computational Neuroscience (publié)

doi.org

Peer-to-Peer Energy Trading and Energy Conversion in Interconnected Multi-Energy Microgrids Using Multi-Agent Deep Reinforcement Learning

Tianyi Chen

Shengrong Bu

Xue Liu

Jikun Kang

F. Richard Yu

Zhu Han

A key aspect of multi-energy microgrids (MEMGs) is the capability to efficiently convert and store energy in order to reduce the costs and e… (voir plus)nvironmental impact. Peer-to-peer (P2P) energy trading is a novel paradigm for decentralised energy market designs. In this paper, we investigate the external P2P energy trading problem and internal energy conversion problem within interconnected residential, commercial and industrial MEMGs. These two problems are complex decision-making problems with enormous high-dimensional data and uncertainty, so a multi-agent deep reinforcement learning approach combining the multi-agent actor-critic algorithm with the twin delayed deep deterministic policy gradient algorithm is proposed. The proposed approach can handle the high-dimensional continuous action space and aligns with the nature of P2P energy trading with multiple MEMGs. Simulation results based on three real-world MG datasets show that the proposed approach significantly reduces each MGâ€™s average hourly operation cost. The impact of carbon tax pricing is also considered.

2021-12-31

IEEE Transactions on Smart Grid (publié)

doi.org

PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records

Tianyi Zhang

Shirui Zhang

Ziwei Chen

Yoshua Bengio

Dianbo Liu

Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources, especially when communicatio… (voir plus)n technology develops rapidly and an unprecedented amount of data could be collected on mobile devices nowadays. Federated learning method exploits not only the data but the computational power of all devices in the network to achieve more efficient model training. Nevertheless, while most traditional federated learning methods work well for homogeneous data and tasks, adapting the method to heterogeneous data and task distribution is challenging. This limitation has constrained the applications of federated learning in real-world contexts, especially in healthcare settings. Inspired by the fundamental idea of meta-learning, in this study we propose a new algorithm, which is an integration of federated learning and meta-learning, to tackle this issue. In addition, owing to the advantage of transfer learning for model generalization, we further improve our algorithm by introducing partial parameter sharing to balance global and local learning. We name this method partial meta-federated learning (PMFL). Finally, we apply the algorithms to two medical datasets. We show that our algorithm could obtain the fastest training speed and achieve the best performance when dealing with heterogeneous medical datasets.

2021-12-31

IEEE Conference on Big Data (publié)

PRACTICAL GUIDE

Paolo Bellavista

2021-12-31

(publié)

www.semanticscholar.org

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

Eric Larsen

Andrea Lodi

2021-12-31

INFORMS Journal on Computing (publié)

doi.org

arxiv.org

(Private)-Retroactive Carbon Pricing [(P)ReCaP]: A Market-based Approach for Climate Finance and Risk Assessment

Yoshua Bengio

Prateek Gupta

Dylan Radovic

Maarten Scholl

Andrew Williams

Christian Schroeder de Witt

Tianyu Zhang

Yang Zhang

Insufficient Social Cost of Carbon (SCC) estimation methods and short-term decision-making horizons have hindered the ability of carbon emit… (voir plus)ters to properly correct for the negative externalities of climate change, as well as the capacity of nations to balance economic and climate policy. To overcome these limitations, we introduce Retrospective Social Cost of Carbon Updating (ReSCCU), a novel mechanism that corrects for these limitations as empirically measured evidence is collected. To implement ReSCCU in the context of carbon taxation, we propose Retroactive Carbon Pricing (ReCaP), a market mechanism in which polluters offload the payment of ReSCCU adjustments to insurers. To alleviate systematic risks and minimize government involvement, we introduce the Private ReCaP (PReCaP) prediction market, which could see real-world implementation based on the engagement of a few high net-worth individuals or independent institutions.

2021-12-31

SSRN Electronic Journal (inconnu)

doi.org

arxiv.org

Question Personalization in an Intelligent Tutoring System

Sabina Elkins

Robert Belfer

Ekaterina Kochmar

Iulian V. Serban

Jackie CK Cheung

2021-12-31

AIED (2) (publié)