357 research outputs found
HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders
This paper introduces an end-to-end neural speech restoration model,
HD-DEMUCS, demonstrating efficacy across multiple distortion environments.
Unlike conventional approaches that employ cascading frameworks to remove
undesirable noise first and then restore missing signal components, our model
performs these tasks in parallel using two heterogeneous decoder networks.
Based on the U-Net style encoder-decoder framework, we attach an additional
decoder so that each decoder network performs noise suppression or restoration
separately. We carefully design each decoder architecture to operate
appropriately depending on its objectives. Additionally, we improve performance
by leveraging a learnable weighting factor, aggregating the two decoder output
waveforms. Experimental results with objective metrics across various
environments clearly demonstrate the effectiveness of our approach over a
single decoder or multi-stage systems for general speech restoration task.Comment: Accepted by INTERSPEECH 202
Brain-Driven Representation Learning Based on Diffusion Model
Interpreting EEG signals linked to spoken language presents a complex
challenge, given the data's intricate temporal and spatial attributes, as well
as the various noise factors. Denoising diffusion probabilistic models (DDPMs),
which have recently gained prominence in diverse areas for their capabilities
in representation learning, are explored in our research as a means to address
this issue. Using DDPMs in conjunction with a conditional autoencoder, our new
approach considerably outperforms traditional machine learning algorithms and
established baseline models in accuracy. Our results highlight the potential of
DDPMs as a sophisticated computational method for the analysis of
speech-related EEG signals. This could lead to significant advances in
brain-computer interfaces tailored for spoken communication
Effect of Hydraulic Activity on Crystallization of Precipitated Calcium Carbonate (PCC) for Eco-Friendly Paper
Wt% of aragonite, a CaCO3 polymorph, increased with higher hydraulic activity (°C) of limestone in precipitated calcium carbonate (PCC) from the lime-soda process (Ca(OH)2-NaOH-Na2CO3). Only calcite, the most stable polymorph, was crystallized at hydraulic activity under 10 °C, whereas aragonite also started to crystallize over 10 °C. The crystallization of PCC is more dependent on the hydraulic activity of limestone than CaO content, a factor commonly used to classify limestone ores according to quality. The results could be effectively applied to the determination of polymorphs in synthetic PCC for eco-friendly paper manufacture
DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework
Childhood and adolescent obesity rates are a global concern because obesity
is associated with chronic diseases and long-term health risks. Artificial
intelligence technology has emerged as a promising solution to accurately
predict obesity rates and provide personalized feedback to adolescents. This
study emphasizes the importance of early identification and prevention of
obesity-related health issues. Factors such as height, weight, waist
circumference, calorie intake, physical activity levels, and other relevant
health information need to be considered for developing robust algorithms for
obesity rate prediction and delivering personalized feedback. Hence, by
collecting health datasets from 321 adolescents, we proposed an adolescent
obesity prediction system that provides personalized predictions and assists
individuals in making informed health decisions. Our proposed deep learning
framework, DeepHealthNet, effectively trains the model using data augmentation
techniques, even when daily health data are limited, resulting in improved
prediction accuracy (acc: 0.8842). Additionally, the study revealed variations
in the prediction of the obesity rate between boys (acc: 0.9320) and girls
(acc: 0.9163), allowing the identification of disparities and the determination
of the optimal time to provide feedback. The proposed system shows significant
potential in effectively addressing childhood and adolescent obesity
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