Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System

2020 ◽  
Author(s):  
Mandar Gogate ◽  
Kia Dashtipour ◽  
Amir Hussain
Keyword(s):  
Deep Learning ◽  
Benchmark Dataset ◽  
Visual Speech ◽  
Noisy Environments ◽  
Baseline System

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

scholarly journals Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5446
Author(s):  
Hyojung Ahn ◽  
Inchoon Yeo
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Detection System ◽  
Noisy Environments ◽  
Acoustic Data ◽  
Detection Techniques ◽  
Machine Operation ◽  
Detection Technology ◽  
Environment Noise

As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.

scholarly journals LIP-READING VIA DEEP NEURAL NETWORKS USING HYBRID VISUAL FEATURES

2018 ◽  
Vol 37 (2) ◽  
pp. 159 ◽  
Author(s):  
Fatemeh Vakhshiteh ◽  
Farshad Almasganj ◽  
Ahmad Nickabadi
Keyword(s):  
Speech Intelligibility ◽  
Deep Neural Networks ◽  
Visual Speech ◽  
Visual Features ◽  
Noisy Environments ◽  
Phone Recognition ◽  
Facial Information ◽  
Visual Speech Recognition ◽  
Lip Reading ◽  
Reading System

Lip-reading is typically known as visually interpreting the speaker's lip movements during speaking. Experiments over many years have revealed that speech intelligibility increases if visual facial information becomes available. This effect becomes more apparent in noisy environments. Taking steps toward automating this process, some challenges will be raised such as coarticulation phenomenon, visual units' type, features diversity and their inter-speaker dependency. While efforts have been made to overcome these challenges, presentation of a flawless lip-reading system is still under the investigations. This paper searches for a lipreading model with an efficiently developed incorporation and arrangement of processing blocks to extract highly discriminative visual features. Here, application of a properly structured Deep Belief Network (DBN)- based recognizer is highlighted. Multi-speaker (MS) and speaker-independent (SI) tasks are performed over CUAVE database, and phone recognition rates (PRRs) of 77.65% and 73.40% are achieved, respectively. The best word recognition rates (WRRs) achieved in the tasks of MS and SI are 80.25% and 76.91%, respectively. Resulted accuracies demonstrate that the proposed method outperforms the conventional Hidden Markov Model (HMM) and competes well with the state-of-the-art visual speech recognition works.

scholarly journals Multisensory integration-attention trade-off in cochlear-implanted deaf individuals

2020 ◽  
Author(s):  
Luuk P.H. van de Rijt ◽  
A. John van Opstal ◽  
Marc M. van Wanrooij
Keyword(s):  
Speech Recognition ◽  
Cochlear Implant ◽  
Divided Attention ◽  
Normal Hearing ◽  
Visual Speech ◽  
Speech Comprehension ◽  
Noisy Environments ◽  
Trade Off ◽  
Attentional Resources ◽  
Attention Tasks

AbstractThe cochlear implant (CI) allows profoundly deaf individuals to partially recover hearing. Still, due to the coarse acoustic information provided by the implant, CI users have considerable difficulties in recognizing speech, especially in noisy environments, even years after implantation. CI users therefore rely heavily on visual cues to augment speech comprehension, more so than normal-hearing individuals. However, it is unknown how attention to one (focused) or both (divided) modalities plays a role in multisensory speech recognition. Here we show that unisensory speech listening and speech reading were negatively impacted in divided-attention tasks for CI users - but not for normal-hearing individuals. Our psychophysical experiments revealed that, as expected, listening thresholds were consistently better for the normal-hearing, while lipreading thresholds were largely similar for the two groups. Moreover, audiovisual speech recognition for normal-hearing individuals could be described well by probabilistic summation of auditory and visual speech recognition, while CI users were better integrators than expected from statistical facilitation alone. Our results suggest that this benefit in integration, however, comes at a cost. Unisensory speech recognition is degraded for CI users when attention needs to be divided across modalities, i.e. in situations with uncertainty about the upcoming stimulus modality. We conjecture that CI users exhibit an integration-attention trade-off. They focus solely on a single modality during focused-attention tasks, but need to divide their limited attentional resources to more modalities during divided-attention tasks. We argue that in order to determine the benefit of a CI for speech comprehension, situational factors need to be discounted by presenting speech in realistic or complex audiovisual environments.Significance statementDeaf individuals using a cochlear implant require significant amounts of effort to listen in noisy environments due to their impoverished hearing. Lipreading can benefit them and reduce the burden of listening by providing an additional source of information. Here we show that the improved speech recognition for audiovisual stimulation comes at a cost, however, as the cochlear-implant users now need to listen and speech-read simultaneously, paying attention to both modalities. The data suggests that cochlear-implant users run into the limits of their attentional resources, and we argue that they, unlike normal-hearing individuals, always need to consider whether a multisensory benefit outweighs the unisensory cost in everyday environments.

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