Results 131 to 140 of about 3,612,587 (208)

Mel Frequency Cepstral Coefficient (MFCC) extraction for speaker identification on FPGA

, 2009
Organized by School of Mechatronic Engineering (UniMAP) & co-organized by The Institution of Engineering Malaysia (IEM), 11th - 13th October 2009 at Batu Feringhi, Penang, Malaysia.Feature extraction of speech is one of the most important issues in the ...
Ahmad Nasir, Che Rosli, Phak Len, Eh Kan, Rafikha Aliana, Raof, Razaidi, Hussin   +3 more
core  

Ensemble learning model for deepfake audio detection using multi-feature extraction approach. [PDF]

Sci Rep
Chaudhury P, Narayan LS, Mridhula S, Tiwari R.   +3 more
europepmc   +1 more source

Hybrid deep learning model for multimodal vocal and lung signal analysis in health monitoring. [PDF]

Sci Rep
Revathi S, Mohanasundaram K, Naveen P.
europepmc   +1 more source

SpeechDETECT: an explainable automated speech processing pipeline for early detection of neurological and health changes. [PDF]

Health Inf Sci Syst
Zolnoori M, Esmaeili E, Naserian M, Zolnour A, Rashidi S, Morovati T, Azadmaleki H, Zhang Z, Noble JM, McDonald MV.   +9 more
europepmc   +1 more source

A Sensor-Based TinyML Acoustic Monitoring System for Edge-Side Animal Sound Recognition on Resource-Constrained Microcontrollers. [PDF]

Sensors (Basel)
Wang Z, Yu G.
europepmc   +1 more source

Adaptive federated learning with differential privacy for multi-class respiratory disease recognition from lung sound recordings. [PDF]

Front Digit Health
Haq SR, Lakshmanna K.
europepmc   +1 more source

ENHANCED CARDIOVASCULAR DISEASE DIAGNOSIS USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS (MFCC) AND MACHINE LEARNING: A COMPARATIVE ANALYSIS OF MACHINE LEARNING CLASSIFIERS

Owing to the fact that cardiovascular diseases (CVDs) are one of the main causes of mortality at the global level, so these diseases must be addressed. This study has approached the reported problem through the signals processing of the heart sounds. In particular, state of the art feature Mel-frequency Cepstral Coefficients (MFCC) has been extracted ...
openaire   +2 more sources

Spatial-Temporal Relation Enhancement for Speech Emotion Recognition from Acoustic Signals Using Fibonacci Encoding with Diverse Feature Fusion. [PDF]

Sensors (Basel)
Huang S, Zhang J, Xu J, Zhang Q, Li Z, Wang S, Qiu Z, Xiong Z, Liang J, Fu C.   +9 more
europepmc   +1 more source

A 1D-CNN with advanced data augmentation for robust speech emotion recognition. [PDF]

Sci Rep
Chourasia N, Lamba CS, Gupta AK.
europepmc   +1 more source

Firearm classification from acoustic signals using combined mel spectrogram, MFCC, LFCC, and CRNN networks. [PDF]

Sci Rep
Elkarous L, Jeridi MH, Dhouibi M.
europepmc   +1 more source