Clustered and deep echo state networks for signal noise reduction

Bidirectional deep echo state networks

We propose a deep architecture for the classification of multivariate time series. By means of a recurrent and untrained reservoir we generate a vectorial representation that embeds temporal relationships in the data. To improve the memorization capability, we implement a bidirectional reservoir, whose last state captures also past dependencies in the input. We apply dimensionality reduction to...

Deep Echo State Networks for Diagnosis of Parkinson's Disease

In this paper, we introduce a novel approach for diagnosis of Parkinson’s Disease (PD) based on deep Echo State Networks (ESNs). The identification of PD is performed by analyzing the whole time-series collected from a tablet device during the sketching of spiral tests, without the need for feature extraction and data preprocessing. We evaluated the proposed approach on a public dataset of spir...

Robust Noise Reduction and Echo Cancellation

Restricted Echo State Networks

Echo state networks are a powerful type of reservoir neural network, but the reservoir is essentially unrestricted in its original formulation. Motivated by limitations in neuromorphic hardware, we remove combinations of the four sources of memory—leaking, loops, cycles, and discrete time—to determine how these influence the suitability of the reservoir. We show that loops and cycles can replic...

Acoustic echo control and noise reduction for cabin car communication

A Cabin Car Communication System (CCCS) has the goal of improving the communication among passengers inside the car. Wind, road and engine noise, the distance between passengers and other factors make difficult the communication inside vehicles. The driver must often look away from the road and passengers move out of normal seating positions. The CCCS makes use of a set of microphones to pick u...