The speech denoising Neural Network architecture we propose in this paper is based on Adaptive Spline Neural Network (ASNN). It is an architecture for real-time oriented applications, due to its low size complexity and high parallelism. Results show improvements in Signal to Noise Ratio (SNR) and better performances in comparison with classical denoising neural networks. Key-Words: Speech Enhan...

Signal denoising is the process of reducing the unwanted noise in order to restore the original signal. Donoho and Johnstone’s denoising algorithm based on wavelet thresholding replace the small coefficients by zero and keep or shrink the coefficients with absolute value above the threshold. So the threshold selection becomes more important in signal denoising. In this paper the threshold selec...

Hyperspectral imagery (HSI) denoising is a challenging problem because of the difficulty in preserving both spectral and spatial structures simultaneously. In recent years, sparse coding, among many methods dedicated to the problem, has attracted much attention and showed state-of-the-art performance. Due to the low-rank property of natural images, an assumption can be made that the latent clea...

The electrocardiogram (ECG) is widely used for the diagnosis of heart diseases. However, ECG signals are easily contaminated by different noises. This paper presents efficient denoising and compressed sensing (CS) schemes based on basis pursuit (BP). In process signal reconstruction, low-pass filtering method alternating direction multipliers (ADMM) optimization algorithm used. introduces dual ...

Wavelet-based signal processing has become commonplace in the signal processing community over the past decade.Wavelet analysis is often very effective because it provides a simple approach for dealing with local aspects of a signal. Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. ...

In order to improve the transmission effect of low-power communication signal Internet Things and compress enhancement time signal, this paper designs a method based on nonlocal mean denoising. Firstly, residual one-dimensional layer is preprocessed by convolution core obtain layer. Then, according two classification recognition methods, noise reduction feature realized, algorithm used remove T...