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

Fault diagnosis of electric motors is a fundamental task for production line testing, and it is
usually performed by experienced human operators. In the recent years, several methods …

The objective of this paper is to provide a review of recent advances in automatic vibration-
and audio-based fault diagnosis in machinery using condition monitoring strategies. It …

Regular inspection of railway track health is crucial for maintaining safe and reliable train
operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts …

Electroencephalogram (EEG) signals are widely used to study the activity of the brain, such
as to determine sleep stages. These EEG signals are nonlinear and non-stationary in …

Speech processing is very important research area where speaker recognition, speech
synthesis, speech codec, speech noise reduction are some of the research areas. Many of …

In recent years, fault diagnosis of rotating equipment based on vibration signals is receiving
much importance due to its vital role as part of the Conditional Based Monitoring (CBM) …

This paper presents a novel approach for discriminating abnormal electric motors from
normal motors using real sound data. The proportion of abnormal data among all training …

This paper proposes singular value decomposition (SVD)-based feature extraction methods
for fault classification of an induction motor: a short-time energy (STE) plus SVD technique in …

In this paper our main aim to provide the difference between cepstral and non-cepstral
feature extraction techniques. Here we try to cover-up most of the comparative features of …