Results 31 to 40 of about 28,361 (199)

Normal distribution analysis (mean±std) of the combined mel-frequency cepstral coefficients (MFCCs) using the deep breathing dataset.

, 2022
Normal distribution analysis (mean±std) of the combined mel-frequency cepstral coefficients (MFCCs) using the deep breathing dataset.
Mohanad Alkhodari (11944473), Ahsan H. Khandoker (7039793)   +1 more
core   +1 more source

A computer-aided-diagnosis system for neuromuscular diseases using Mel frequency Cepstral coefficients

Scientific African, 2021
Amyotrophic Lateral Sclerosis (ALS) and Myopathy are the most well-known neuromuscular diseases. Electromyography (EMG) signal is hugely used in the diagnosis of these neuromuscular disorders.
Abdelali Belkhou, Atman Jbari, Othmane El Badlaoui   +2 more
doaj   +1 more source

Audio anomaly detection in industrial machines using inverse-Mel scale spectrograms

Systems and Soft Computing
Industrial machine sounds are commonly analyzed using Mel frequency scales, which are based on human auditory response and are primarily sensitive to low-frequency sounds.
Kader B.T. Shaikh, Naresh P. Jawarkar, Vasif Ahmed, Nadir N. Charniya   +3 more
doaj   +1 more source

Verifikasi Suara menggunakan Jaringan Syaraf Tiruan dan Ekstraksi Ciri Mel Frequency Cepstral Coefficient

, 2017
Voice recording is an important part of the evidence for the suspect, so it is necessary to verify the voice suspects to prove the allegations of the suspect.
Andi Kurniawan
core   +1 more source

Comparative Study of Spectrogram, Cepstrum and Mel-Frequency Analysis for Bushing Fault Diagnosis using Sound Signal. [PDF]

, 2020
This research paper aims to present the Comparative Study of Spectrogram, Cepstrum and Mel-Frequency Analysis for Bushing Fault Diagnosis using Sound Signal, have three case of condition testing for shaded pole motor. This research used shaded pole motor
Thungsuk, N., Tanaram, T., 32847, Tati, S., 32845, 32846   +5 more
core  

VOICE RECOGNITION SECURITY SYSTEM USING MEL-FREQUENCY CEPSTRUM COEFFICIENTS

, 2016
Objective: Voice Recognition is a fascinating field spanning several areas of computer science and mathematics. Reliable speaker recognition is a hardproblem, requiring a combination of many techniques; however modern methods have been able to achieve an
Mahalakshmi P, A, Sharmila, M, Muruganandam   +2 more
core   +3 more sources

Pre-Configured Deep Convolutional Neural Networks with Various Time-Frequency Representations for Biometrics from ECG Signals

Applied Sciences, 2019
We evaluated electrocardiogram (ECG) biometrics using pre-configured models of convolutional neural networks (CNNs) with various time-frequency representations.
Yeong-Hyeon Byeon, Keun-Chang Kwak
doaj   +1 more source

Environment Sound Classification Based on Visual Multi-Feature Fusion and GRU-AWS

IEEE Access, 2020
There are two major questions regarding Environmental Sound Classification (ESC). What is the best audio recognition framework, and what is the most robust audio feature?
Ning Peng, Aibin Chen, Guoxiong Zhou, Wenjie Chen, Wenzhuo Zhang, Jing Liu, Fubo Ding   +6 more
doaj   +1 more source

Speech reconstruction from mel frequency cepstral coefficients and pitch frequency [PDF]

2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100), 2002
This paper presents a novel low complexity, frequency domain algorithm for reconstruction of speech from the mel-frequency cepstral coefficients (MFCC), commonly used by speech recognition systems, and the pitch frequency values. The reconstruction technique is based on the sinusoidal speech representation.
Dan Chazan, Ron Hoory, Gilad Cohen, Meir Zibulski   +3 more
openaire   +1 more source

Flow chart showing the procedure for computation of mel-frequency cepstral coefficients.

, 2014
Flow chart showing the procedure for computation of mel-frequency cepstral coefficients.
Chandra Sekhar Seelamantula (531536), P. V. D. S. Sundeep (531535), Rohini Balakrishnan (463161), Samira Agnihotri (531534)   +3 more
core   +1 more source