0438 SNORE SOUND ANALYSIS: WITHIN AND BEYOND HUMAN HEARING RANGE

An 'End-to-Evolution' Hybrid Approach for Snore Sound Classification

Whilst snoring itself is usually not harmful to a person’s health, it can be an indication of Obstructive Sleep Apnoea (OSA), a serious sleep-related disorder. As a result, studies into using snoring as acoustic based marker of OSA are gaining in popularity. Motivated by this, the INTERSPEECH 2017 ComParE Snoring sub-challenge requires classification from which areas in the upper airways differ...

Snore Sound Classification Using Image-Based Deep Spectrum Features

In this paper, we propose a method for automatically detecting various types of snore sounds using image classification convolutional neural network (CNN) descriptors extracted from audio file spectrograms. The descriptors, denoted as deep spectrum features, are derived from forwarding spectrograms through very deep task-independent pre-trained CNNs. Specifically, activations of fully connected...

Covert visual search within and beyond the effective oculomotor range

Covert spatial attention is tightly coupled to the eye-movement system, but the precise nature of this coupling remains contentious. Recent research has argued that covert attention and overt eye-movements many share a common biological limit, such that covert exogenous orienting of attention is limited to stimuli that fall within the range of possible eye movements (the effective oculomotor ra...

Of Sound and Hearing

Effect of sound classification by neural networks in the recognition of human hearing

In this paper, we focus on two basic issues: (a) the classification of sound by neural networks based on frequency and sound intensity parameters (b) evaluating the health of different human ears as compared to of those a healthy person. Sound classification by a specific feed forward neural network with two inputs as frequency and sound intensity and two hidden layers is proposed. This process...