Results 131 to 140 of about 3,484 (180)

Hybrid deep learning and YAMNet features for asthma diagnosis from respiratory sounds. [PDF]

Sci Rep
Shatat GAE, Moustafa HE, Saraya MS, Marzouk AA, Hekal AA.   +4 more
europepmc   +1 more source

Explainable ResNet-long short-term memory model for the classification of bowel sounds frequency based on multifeature fusion. [PDF]

J Int Med Res
Zhang W, Wang Y, Zhu J, Wu Q, Xu C, Liu L, Zhu S, Liu X, Lin J, Yu C, Yin Q, Ding X, Xu Z.   +12 more
europepmc   +1 more source

Optimizing MFCC Parameters for Breathing Phase Detection. [PDF]

Sensors (Basel)
Zhantleuova AK, Makashev YK, Duzbayev NT.   +2 more
europepmc   +1 more source

Speech impairment detection in children using time frequency features of speech and deep learning techniques. [PDF]

Front Hum Neurosci
Manoswini M, Swain BR, Sahoo B, Gourisaria MK, Jha AV, Bizon N, Swetapadma A.   +6 more
europepmc   +1 more source

Robust Heart Sound Analysis With MFCC and Light Weight Convolutional Neural Network. [PDF]

IEEE Open J Eng Med Biol
Hasan A, Karim M.
europepmc   +1 more source

The Utility of Speech and Language Analytics for Screening Alzheimer's Disease.

Neurodegener Dis
Siddiqui A, Kathiresan T, Opler M, Cohen A, Alber J, Snyder PJ, Vogel AP.   +6 more
europepmc   +1 more source

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A comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequency Cepstral Coefficients (IMFCC) features for an Automatic Bird Species Recognition System

2018 IEEE Latin American Conference on Computational Intelligence (LA-CCI), 2018
In this paper a comparative between Mel Frequency Cepstral Coefficients (MFCC) and Inverse Mel Frequency Cepstral Coefficients (IMFCC) features for an automatic bird species recognition system is proposed with the aim to validate IMFCC as a feature that can also be extracted for bird species recognition.
Aldonso Becerra
exaly   +3 more sources

Mel Frequency Cepstral Coefficients and Support Vector Machines for Cough Detection

Lecture Notes in Computer Science, 2023
Dustin Van Der Haar, Mpho Mashika, Van Der Haar Dustin   +2 more
exaly   +2 more sources

Computing Mel-frequency cepstral coefficients on the power spectrum

2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221), 2002
We present a method to derive Mel-frequency cepstral coefficients directly from the power spectrum of a speech signal. We show that omitting the filterbank in signal analysis does not affect the word error rate. The presented approach simplifies the speech recognizers front end by merging subsequent signal analysis steps into a single one.
Sirko Molau, Michael Pitz, Ralf Schlüter, Hermann Ney   +3 more
openaire   +1 more source