Emotional quantification of soundscapes by learning between samples

Predicting the emotional responses of humans to soundscapes is a relatively recent field of research coming with a wide range of promising applications. This work presents the design of two convolutional neural networks, namely ArNet and ValNet, each one responsible for quantifying arousal and valence evoked by soundscapes. We build on the knowledge acquired from the application of traditional machine learning techniques on the specific domain, and design a suitable deep learning framework. Moreover, we propose the usage of artificially created mixed soundscapes, the distributions of which are located between the ones of the available samples, a process that increases the variance of the dataset leading to significantly better performance. The reported results outperform the state of the art on a soundscape dataset following Schafer\u2019s standardized categorization considering both sound\u2019s identity and the respective listening context

Hybrid framework for categorising sounds of mysticete whales

This study addresses a problem belonging to the domain of whale audio processing, more specifically the automatic classification of sounds produced by the Mysticete species. The specific task is quite challenging given the vast repertoire of the involved species, the adverse acoustic conditions and the nearly inexistent prior scientific work. Two feature sets coming from different domains (frequency and wavelet) were designed to tackle the problem. These are modelled by means of a hybrid technique taking advantage of the merits of a generative and a discriminative classifier. The dataset includes five species (Blue, Fin, Bowhead, Southern Right, and Humpback) and it is publicly available at http://www.mobysound.org/. The authors followed a thorough experimental procedure and achieved quite encouraging recognition rates