Inference and Denoise: Causal Inference-based Neural Speech Enhancement

This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention. Based on the potential outcome framework, the proposed causal inference-based speech enhancement (CISE) separates clean and noisy frames in an intervened noisy speech using a noise detector and assigns both sets of frames to two mask-based enhancement modules (EMs) to perform noise-conditional SE. Specifically, we use the presence of noise as guidance for EM selection during training, and the noise detector selects the enhancement module according to the prediction of the presence of noise for each frame. Moreover, we derived a SE-specific average treatment effect to quantify the causal effect adequately. Experimental evidence demonstrates that CISE outperforms a non-causal mask-based SE approach in the studied settings and has better performance and efficiency than more complex SE models.Comment: Submitted to ICASSP 202

EPG2S: Speech Generation and Speech Enhancement based on Electropalatography and Audio Signals using Multimodal Learning

Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the ability to speak. Although various techniques have been proposed to this end, electropalatography (EPG), which is a monitoring technique that records contact between the tongue and hard palate during speech, has not been adequately explored. Herein, we propose a novel multimodal EPG-to-speech (EPG2S) system that utilizes EPG and speech signals for speech generation and enhancement. Different fusion strategies based on multiple combinations of EPG and noisy speech signals are examined, and the viability of the proposed method is investigated. Experimental results indicate that EPG2S achieves desirable speech generation outcomes based solely on EPG signals. Further, the addition of noisy speech signals is observed to improve quality and intelligibility. Additionally, EPG2S is observed to achieve high-quality speech enhancement based solely on audio signals, with the addition of EPG signals further improving the performance. The late fusion strategy is deemed to be the most effective approach for simultaneous speech generation and enhancement.Comment: Accepted By IEEE Signal Processing Lette

PreFallKD: Pre-Impact Fall Detection via CNN-ViT Knowledge Distillation

Fall accidents are critical issues in an aging and aged society. Recently, many researchers developed pre-impact fall detection systems using deep learning to support wearable-based fall protection systems for preventing severe injuries. However, most works only employed simple neural network models instead of complex models considering the usability in resource-constrained mobile devices and strict latency requirements. In this work, we propose a novel pre-impact fall detection via CNN-ViT knowledge distillation, namely PreFallKD, to strike a balance between detection performance and computational complexity. The proposed PreFallKD transfers the detection knowledge from the pre-trained teacher model (vision transformer) to the student model (lightweight convolutional neural networks). Additionally, we apply data augmentation techniques to tackle issues of data imbalance. We conduct the experiment on the KFall public dataset and compare PreFallKD with other state-of-the-art models. The experiment results show that PreFallKD could boost the student model during the testing phase and achieves reliable F1-score (92.66%) and lead time (551.3 ms)

A Study of Low-Resource Speech Commands Recognition based on Adversarial Reprogramming

In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to modify the acoustic signals (from the target domain) to repurpose a pretrained SCR model (from the source domain). To solve the label mismatches between source and target domains, and further improve the stability of AR, we propose a novel similarity-based label mapping technique to align classes. In addition, the transfer learning (TL) technique is combined with the original AR process to improve the model adaptation capability. We evaluate the proposed AR-SCR system on three low-resource SCR datasets, including Arabic, Lithuanian, and dysarthric Mandarin speech. Experimental results show that with a pretrained AM trained on a large-scale English dataset, the proposed AR-SCR system outperforms the current state-of-the-art results on Arabic and Lithuanian speech commands datasets, with only a limited amount of training data.Comment: Submitted to ICASSP 202

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios

New Plasma Separation Glucose Oxidase-based Glucometer in Monitoring of Blood With Different PO2 Levels

BackgroundThe PalmLab glucometer is a newly designed plasma separation glucose oxidase (GO)-based glucometer. Past studies have shown that the accuracy of GO-based glucometers is compromised when measurements are taken in patients with high PO2 levels. We performed a two-arm study comparing the fitness of the PalmLab blood glucometer with that of a standard glucose analyzer in monitoring blood glucose levels in pediatric patients, especially when arterial partial pressure of oxygen (PO2) was high.MethodsIn the first arm of the study, arterial blood samples from pediatric patients were measured by the PalmLab blood glucometer and the YSI 2302 Plus Glucose/Lactate analyzer. In the second arm of the study, venous blood samples from adult volunteers were spiked with glucose water to prepare three different levels of glucose (65, 150, and 300mg/dL) and then oxygenated to six levels of PO2 (range, 40–400mmHg). The biases of the PalmLab glucometer were calculated.ResultsA total of 162 samples were collected in the first arm of the study. Results of linear regression showed that the coefficient of determination (R2) between PalmLab glucometer and standard glucose analyzer was 0.9864. Error grid analysis revealed that all the results were within Zone A (clinically accurate estimate zone). The biases between the two systems were low at different PO2 levels. In the second arm of the study, the results were also unaffected by changes in PO2.ConclusionThe PalmLab glucometer provides accurate results in samples with high PO2 and is suitable for measuring arterial glucose levels in pediatric patients

Assessing the Decision-Making Process in Human-Robot Collaboration Using a Lego-like EEG Headset

Human-robot collaboration (HRC) has become an emerging field, where the use of a robotic agent has been shifted from a supportive machine to a decision-making collaborator. A variety of factors can influence the effectiveness of decision-making processes during HRC, including the system-related (e.g., robot capability) and human-related (e.g., individual knowledgeability) factors. As a variety of contextual factors can significantly impact the human-robot decision-making process in collaborative contexts, the present study adopts a Lego-like EEG headset to collect and examine human brain activities and utilizes multiple questionnaires to evaluate participants’ cognitive perceptions toward the robot. A user study was conducted where two levels of robot capabilities (high vs. low) were manipulated to provide system recommendations. The participants were also identified into two groups based on their computational thinking (CT) ability. The EEG results revealed that different levels of CT abilities trigger different brainwaves, and the participants’ trust calibration of the robot also varies the resultant brain activities

Pyr3 Induces Apoptosis and Inhibits Migration in Human Glioblastoma Cells

Background/Aims: Glioblastoma, also known as glioblastoma multiforme (GBM), is a fast-growing type of tumor that is the most aggressive brain malignancy in adults. According to GEO profile analysis, patients with high transient receptor potential canonical 3 (TRPC3) expression have poor survival rates. The aim of this study is to evaluate the effects of Ethyl-1-(4-(2,3,3-trichloroacrylamide)phenyl)-5-(trifluoromethyl)-1H-pyrazole-4-carboxylate (Pyr3), a selective TRPC3 channel blocker, on the proliferation and migration of human glioblastoma cells. Methods: We first analyzed the TRPC3 mRNA expression in Gene Expression Omnibus (GEO) database. Then, TRPC3 protein expression was analyzed by Western blotting in three human GBM cell lines. The survival rate was measured by sulforhodamine B. JC1 staining was used to analyze the mitochondria membrane potential by flow cytometric analysis. Besides, the migration and invasion were evaluated by wound healing and Transwell assays. Annexin V and 7-aminoactinomycin D staining was used to monitor the apoptosis by flow cytometric analysis. The expression of apoptotic-related and migration-related proteins after Pyr3 treatment was detected by Western blotting. In addition, an orthotropic xenograft mouse model was used to assay the effect of Pyr3 in the in vivo study. Results: Basis on the results of bioinformatics study, glioma patients with higher TRPC3 expression had a shorter survival time than those with lower TRPC3 expression. GBM cell proliferation was decreased by Pyr3 treatment. The migration and invasion abilities of glioma cells were also inhibited via focal adhesion kinase and myosin light chain dephosphorization after Pyr3 treatment. Moreover, Pyr3 induced caspase-dependent apoptosis and mitochondria membrane potential imbalance in the GBM cells. In a xenograft animal model, Pyr3 in combination with temozolomide (TMZ) inhibited GBM tumor growth. Conclusion: Pyr3 inhibited GBM tumor growth in vitro and in vivo. Pyr3-TMZ combination therapy could be used to treat glioblastoma in the future

Hedgehog overexpression leads to the formation of prostate cancer stem cells with metastatic property irrespective of androgen receptor expression in the mouse model

<p>Abstract</p> <p>Background</p> <p>Hedgehog signalling has been implicated in prostate tumorigenesis in human subjects and mouse models, but its effects on transforming normal basal/stem cells toward malignant cancer stem cells remain poorly understood.</p> <p>Methods</p> <p>We produced pCX-shh-IG mice that overexpress Hedgehog protein persistently in adult prostates, allowing for elucidation of the mechanism during prostate cancer initiation and progression. Various markers were used to characterize and confirm the transformation of normal prostate basal/stem cells into malignant cancer stem cells under the influence of Hedgehog overexpression.</p> <p>Results</p> <p>The pCX-shh-IG mice developed prostatic intraepithelial neoplasia (PIN) that led to invasive and metastatic prostate cancers within 90 days. The prostate cancer was initiated through activation of P63⁺basal/stem cells along with simultaneous activation of Hedgehog signalling members, suggesting that P63⁺/Patch1⁺and P63⁺/Smo⁺cells may serve as cancer-initiating cells and progress into malignant prostate cancer stem cells (PCSCs). In the hyperplastic lesions and tumors, the progeny of PCSCs differentiated into cells of basal-intermediate and intermediate-luminal characteristics, whereas rare ChgA⁺neuroendocrine differentiation was seen. Furthermore, in the metastatic loci within lymph nodes, kidneys, and lungs, the P63⁺PCSCs formed prostate-like glandular structures, characteristic of the primitive structures during early prostate development. Besides, androgen receptor (AR) expression was detected heterogeneously during tumor progression. The existence of P63⁺/AR^-, CK14⁺/AR^-and CD44⁺/AR^-progeny indicates direct procurement of AR^-malignant cancer trait.</p> <p>Conclusions</p> <p>These data support a cancer stem cell scenario in which Hedgehog signalling plays important roles in transforming normal prostate basal/stem cells into PCSCs and in the progression of PCSCs into metastatic tumor cells.</p