Results 41 to 50 of about 8,304 (223)
An automatic method using MFCC features for sleep stage classification [PDF]
Sleep stage classification is a necessary step for diagnosing sleep disorders. Generally, experts use traditional methods based on every 30 seconds (s) of the biological signals, such as electrooculograms (EOGs), electrocardiograms (ECGs ...
Wei Pei +10 more
core +1 more source
Discrimination between patients with CVDs and healthy people by voiceprint using the MFCC and Pitch
Diagnostyka, 2021 Heart diseases cause many deaths around the world every year, and his death rate makes him the leader of the killer diseases. But early diagnosis can be helpful to decrease those several deaths and save lives.
Abdelhamid Bourouhou +3 more
doaj +1 more source
Speech depression recognition based on attentional residual network
Frontiers in Bioscience-Landmark, 2021 Background: Depressive disorder is a common affective disorder, also known as depression, which is characterized by sadness, loss of interest, feelings of guilt or low self-worth and poor concentration.
Xiaoyong Lu +3 more
doaj +1 more source
Use of spectral subband moments in MFCC computation [PDF]
ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359), 2003 Mel frequency cepstral coefficients (MFCCs) are currently the most popular form of parameterization of the speech signal in speech recognition systems. In this paper, we look at a way to improve the extraction of these features using information about the spectral characteristics of the signal to modify filter-bank shapes.
Eigil Gjelsvik, Kuldip K. Paliwal
openaire +1 more source
Advanced Science, EarlyView.WS2‐based in‐memory sensing reservoir computing integrates sensing, memory, and computation in one compact device. It achieves ∼94% N‐MNIST, ∼93% eye motion perception, and ∼89% speech recognition with ultra‐low energy (∼25.5 fJ/spike). The system shows stability at 95% humidity, endurance over 1.5M cycles, and supports synaptic plasticity, enabling ...
Dayanand Kumar +9 more
wiley +1 more source
Towards interpretable speech biomarkers: exploring MFCCs
Scientific Reports, 2023 Abstract While speech biomarkers of disease have attracted increased interest in recent years, a challenge is that features derived from signal processing or machine learning approaches may lack clinical interpretability. As an example, Mel frequency cepstral coefficients (MFCCs) have been identified in several studies as a useful ...
Brian Tracey +9 more
openaire +3 more sources
Ion‐Gating Reservoir Computing for Preprocessing‐Free Speech Recognition from Throat Vibrations
Advanced Electronic Materials, EarlyView.This work presents a throat‐mounted mechanoelectric sensor integrated with an ion‐gel/graphene reservoir device for on‐device speech recognition. The system converts raw biomechanical vibrations into rich nonlinear current dynamics, enabling efficient classification through a simple linear readout. The approach highlights a compact and tunable physical‐
Daiki Nishioka +5 more
wiley +1 more source
Hindi vowel classification using QCN-MFCC features
, 2016 SummaryIn presence of environmental noise, speakers tend to emphasize their vocal effort to improve the audibility of voice. This involuntary adjustment is known as Lombard effect (LE).
Bhowmick, Anirban +2 more
core +1 more source
Experiment 1: EKM confusion matrices using MFCC on EMIR.
, 2023 Experiment 1: EKM confusion matrices using MFCC on EMIR.
Richard Sutcliffe (15332790) +8 more
core +1 more source
Comparison of VTOL UAV Battery Level for Propeller Faulty Classification Model
JOIV: International Journal on Informatics VisualizationThe degradation of batteries in UAVs may result in various problems, such as connectivity troubles, flight delays, and unexpected accidents. Flight safety and reliability are affected by propeller efficiency and performance.
Fareisya Zulaikha Mohd Sani +4 more
doaj +1 more source