Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and it remains a challenging task to effectively retrieve the anechoic speech signals from reverberation ones. In the present study, we proposed a novel integrated deep and ensemble learning algorithm (IDEA) for speech dereverberation. The IDEA consists of offline and online phases. In the offline phase, we train multiple dereverberation models, each aiming to precisely dereverb speech signals in a particular acoustic environment; then a unified fusion function is estimated that aims to integrate the information of multiple dereverberation models. In the online phase, an input utterance is first processed by each of the dereverberation models. The outputs of all models are integrated accordingly to generate the final anechoic signal. We evaluated the IDEA on designed acoustic environments, including both matched and mismatched conditions of the training and testing data. Experimental results confirm that the proposed IDEA outperforms single deep-neural-network-based dereverberation model with the same model architecture and training data

Vertical Macular Asymmetry Measures Derived From SD-OCT for Detection of Early Glaucoma.

PurposeTo test the hypothesis that vertical asymmetry in macular ganglion cell/inner plexiform layer (GCIPL) thickness can improve detection of early glaucoma.MethodsSixty-nine normal eyes and 101 glaucoma eyes had macular imaging with spectral-domain optical coherence tomography (OCT; 200 × 200 cube). The resulting GCIPL thickness matrix was grouped into a 20 × 20 superpixel array and superior superpixels were compared to their inferior counterparts. A global asymmetry index (AI) was defined as the grand mean of the asymmetry ratios. To measure local asymmetry, the corresponding thickness measurements of three rows above and below the horizontal raphe were compared individually and in combinations. Global and local AIs were compared to the best-performing GCIPL thickness parameters with area under the receiver operating curves (AUC) and sensitivity/specificities.ResultsAge or axial length did not influence AIs in normal subjects (P ≥ 0.08). Global and local AIs were significantly higher in the glaucoma group compared to normal eyes. Minimum (AUC = 0.962, 95% confidence interval [CI]: 0.936-0.989) and inferotemporal thickness (AUC = 0.944, 95% CI: 0.910-0.977; P = 0.122) performed best for detection of early glaucoma. The AUC for global AI was 0.851 (95% CI: 0.792-0.909) compared to 0.916 (95% CI: 0.874-0.958) for the best local AI. Combining minimum or inferotemporal GCIPL thickness and the best local AI led to higher partial AUCs (0.088 and 0.085, 90% specificity, P = 0.120 and 0.130, respectively) than GCIPL thickness measures.ConclusionsMacular vertical thickness asymmetry measures did not perform better than sectoral or minimum GCIPL thickness for detection of early glaucoma. Combining local asymmetry parameters with the best sectoral GCIPL thickness measures enhanced this task

CasNet: Investigating Channel Robustness for Speech Separation

Recording channel mismatch between training and testing conditions has been shown to be a serious problem for speech separation. This situation greatly reduces the separation performance, and cannot meet the requirement of daily use. In this study, inheriting the use of our previously constructed TAT-2mix corpus, we address the channel mismatch problem by proposing a channel-aware audio separation network (CasNet), a deep learning framework for end-to-end time-domain speech separation. CasNet is implemented on top of TasNet. Channel embedding (characterizing channel information in a mixture of multiple utterances) generated by Channel Encoder is introduced into the separation module by the FiLM technique. Through two training strategies, we explore two roles that channel embedding may play: 1) a real-life noise disturbance, making the model more robust, or 2) a guide, instructing the separation model to retain the desired channel information. Experimental results on TAT-2mix show that CasNet trained with both training strategies outperforms the TasNet baseline, which does not use channel embeddings.Comment: Submitted to ICASSP 202

Asymmetry of the Bjerknes positive feedback between the two types of El Niño

Corresponding to the pronounced amplitude asymmetry for the central Pacific (CP) and eastern Pacific (EP) types of El Niño, an asymmetry in the strength of the Bjerknes positive feedback is found between these two types of El Niño, which is manifested as a weaker relationship between the zonal wind anomaly and the zonal gradient of sea surface temperature (SST) anomaly in the CP El Niño. The strength asymmetry mainly comes from a weaker sensitivity of the zonal gradient of sea level pressure (SLP) anomaly to that of diabatic heating anomaly during CP El Niño. This weaker sensitivity is caused by (1) a large cancelation induced by the negative SST-cloud thermodynamic feedback to the positive dynamical feedback for CP El Niño, (2) an off-equator shift of the maximum SLP anomalies during CP El Niño, and (3) a suppression of the mean low-level convergence when CP El Niño events occur more often. Key Points Asymmetry of the Bjerknes positive feedback exits between the CP and EP El NiñosThe strength of the Bjerknes positive feedback acts to induce the ENSO diversityThe asymmetry is caused by a different response of zonal SLP to diabatic heatin

Wearable multi-channel microelectrode membranes for elucidating electrophysiological phenotypes of injured myocardium

Understanding the regenerative capacity of small vertebrate models has provided new insights into the plasticity of injured myocardium. Here, we demonstrate the application of flexible microelectrode arrays (MEAs) in elucidating electrophysiological phenotypes of zebrafish and neonatal mouse models of heart regeneration. The 4-electrode MEA membranes were designed to detect electrical signals in the aquatic environment. They were micro-fabricated to adhere to the non-planar body surface of zebrafish and neonatal mice. The acquired signals were processed to display an electrocardiogram (ECG) with high signal-to-noise-ratios, and were validated via the use of conventional micro-needle electrodes. The 4-channel MEA provided signal stability and spatial resolution, revealing the site-specific electrical injury currents such as ST-depression in response to ventricular cryo-injury. Thus, our polymer-based and wearable MEA membranes provided electrophysiological insights into long-term conduction phenotypes for small vertebral models of heart injury and regeneration with a translational implication for monitoring cardiac patients