AIMC Topic: Signal-To-Noise Ratio

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Vision-referential speech enhancement of an audio signal using mask information captured as visual data.

The Journal of the Acoustical Society of America
This paper describes a vision-referential speech enhancement of an audio signal using mask information captured as visual data. Smartphones and tablet devices have become popular in recent years. Most of them not only have a microphone but also a cam...

Deep learning enhancement of infrared face images using generative adversarial networks.

Applied optics
This work presents a deep learning framework based on the use of deep convolutional generative adversarial networks (DCGAN) for infrared face image super-resolution. We use DCGAN for upscaling the images by a factor of 4×4, starting at a size of 16×1...

GPU-based deep convolutional neural network for tomographic phase microscopy with ℓ1 fitting and regularization.

Journal of biomedical optics
Tomographic phase microscopy (TPM) is a unique imaging modality to measure the three-dimensional refractive index distribution of transparent and semitransparent samples. However, the requirement of the dense sampling in a large range of incident ang...

CNN-Based Projected Gradient Descent for Consistent CT Image Reconstruction.

IEEE transactions on medical imaging
We present a new image reconstruction method that replaces the projector in a projected gradient descent (PGD) with a convolutional neural network (CNN). Recently, CNNs trained as image-to-image regressors have been successfully used to solve inverse...

A Hybrid Network for ERP Detection and Analysis Based on Restricted Boltzmann Machine.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Detecting and Please provide the correct one analyzing the event-related potential (ERP) remains an important problem in neuroscience. Due to the low signal-to-noise ratio and complex spatio-temporal patterns of ERP signals, conventional methods usua...

Robust Support Matrix Machine for Single Trial EEG Classification.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electroencephalogram (EEG) signals are of complex structure and can be naturally represented as matrices. Classification is one of the most important steps for EEG signal processing. Newly developed classifiers can handle these matrix-form data by ad...

Projection decomposition algorithm for dual-energy computed tomography via deep neural network.

Journal of X-ray science and technology
BACKGROUND: Dual-energy computed tomography (DECT) has been widely used to improve identification of substances from different spectral information. Decomposition of the mixed test samples into two materials relies on a well-calibrated material decom...

Deep Learning-Based Noise Reduction Approach to Improve Speech Intelligibility for Cochlear Implant Recipients.

Ear and hearing
OBJECTIVE: We investigate the clinical effectiveness of a novel deep learning-based noise reduction (NR) approach under noisy conditions with challenging noise types at low signal to noise ratio (SNR) levels for Mandarin-speaking cochlear implant (CI...

A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction.

Medical physics
PURPOSE: Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, an...

An algorithm to increase intelligibility for hearing-impaired listeners in the presence of a competing talker.

The Journal of the Acoustical Society of America
Individuals with hearing impairment have particular difficulty perceptually segregating concurrent voices and understanding a talker in the presence of a competing voice. In contrast, individuals with normal hearing perform this task quite well. This...