This paper presents and explores a robust deep learning framework for auscultation
analysis. This aims to classify anomalies in respiratory cycles and detect diseases, from …

Deep learning can be used for audio signal classification in a variety of ways. It can be used
to detect and classify various types of audio signals such as speech, music, and …

This paper provides an analysis of state-of-the-art activation functions with respect to
supervised classification of deep neural network. These activation functions comprise of …

The recognition of pig cough sound is a prerequisite for early warning of respiratory
diseases in pig houses, which is essential for detecting animal welfare and predicting …

To improve device robustness, a highly desirable key feature of a competitive data-driven
acoustic scene classification (ASC) system, a novel two-stage system based on fully …

In aquaculture, accurate detection of fish feeding intensity is a critical step for establishing an
on-demand feeding system. In this paper, a novel fish feeding intensity detection method …

Fusion strategies that utilize time-frequency features have achieved superior performance in
acoustic scene classification tasks. However, the existing fusion schemes are mainly …

In this paper, we propose lightweight deep neural networks for Acoustic Scene Classification
(ASC) and a visualization method for presenting a sound scene context. To this end, we first …

In this work, we propose an approach that features deep feature embedding learning and
hierarchical classification with triplet loss function for Acoustic Scene Classification (ASC). In …

In this paper, a feature extraction method is proposed based on the non-negative matrix
factorization (NMF) for classifiers for monitoring domestic activities with acoustic signals …