Learning relationships between audio signals based on reservoir networks

Inspired by the respective human capacity, this paper investigates learning through analogies via the reservoir computing paradigm. Even though the vast majority of the literature considers modeling of patterns/objects serving classification-based applications, we move to learning of relationships characterizing input pairs. To this end, we describe the design process of a suitable echo state network capturing relationships existing between audio signals, i.e. similar, dissimilar, reverberation, and noise. Such a neural network operates on frequency-domain representations of sound belonging to the following four classes: male and female speech, applause and laughter. After extensive experiments, we demonstrate that echo state networks are able to learn through analogies and achieve superior performance when compared with Siamese neural networks.