Feature extraction and representation has significant impact on the performance of any
machine learning method. Mel Frequency Cepstrum Coefficient (MFCC) is designed to …

Recent developments in sensor technology and computational analysis methods enable
new strategies to measure and interpret lung acoustic signals that originate internally, such …

Recent studies have demonstrated the potential of unsupervised feature learning for sound
classification. In this paper we further explore the application of the spherical k-means …

We propose a novel deep learning method for local self-supervised representation learning
that does not require labels nor end-to-end backpropagation but exploits the natural order in …

Electronic beehive monitoring extracts critical information on colony behavior and
phenology without invasive beehive inspections and transportation costs. As an integral …

In large MP3 databases, files are typically generated with different parameter settings, ie, bit
rate and sampling rates. This is of concern for MIR applications, as encoding difference can …

As part of the PhysioNet/Computing in Cardiology Challenge 2016, this work focuses on
automatic classification of normal/abnormal phonocardiogram (PCG) recording, with the aim …

This paper introduces StutterNet, a novel deep learning based stuttering detection capable
of detecting and identifying various types of disfluencies. Most of the existing work in this …

Automatic classification of animal vocalizations has great potential to enhance the
monitoring of species movements and behaviors. This is particularly true for monitoring …

We present a novel unsupervised fall detection system that employs the collected acoustic
signals (footstep sound signals) from an elderly person׳ s normal activities to construct a …