Echo State Networks (ESNs) constitute an emerging approach for efficiently modeling
Recurrent Neural Networks (RNNs). In this paper we investigate some of the main aspects …

This paper describes a novel model for time series recognition called a Dynamic Time
Warping Neural Network (DTW-NN). DTW-NN is a feedforward neural network that exploits …

Reservoir Computing (RC) is an effective approach to design and train recurrent neural
networks, which is successfully and widely applied in real-valued time series modeling …

The spectral shape holds crucial information for Audio Classification (AC), encompassing
the spectrum's envelope, details, and dynamic changes over time. Conventional methods …

Sign language assists in building communication and bridging gaps in understanding.
Automatic sign language recognition (ASLR) is a field that has recently been studied for …

Echo state networks (ESNs) are recurrent neural networks that can be trained efficiently
because the weights of recurrent connections remain fixed at random values. Investigations …

In this paper, we present a novel approach to human–robot control. Taking inspiration from
behaviour-based robotics and self-organisation principles, we present an interfacing …

Echo State neural networks (ESN), which are a special case of recurrent neural networks,
are studied from the viewpoint of their learning ability, with a goal to achieve their greater …

It has been shown that the ability of echo state networks (ESNs) to generalise in a sentence-
processing task can be increased by adjusting their input connection weights to the training …

Especially in the low-budget and amateur score music production workflow the triangular
communication between editor, director and composer is constrained by limited resources …