Results 41 to 50 of about 6,514 (211)
Flow chart showing the procedure for computation of mel-frequency cepstral coefficients.
Flow chart showing the procedure for computation of mel-frequency cepstral coefficients.
Chandra Sekhar Seelamantula (531536) +3 more
core +1 more source
Spoken Digit Recognition using the k-Nearest-Neighbor method and Mel Frequency Cepstral Coefficients [PDF]
This study investigates the utilization of the k-nearest-neighbor algorithm within the framework of machine learning for speech recognition applications. The AudioMNIST dataset is used for performing the evaluations in which the model predicts the spoken
Sorin MURARU, Catalina Lucia COCIANU
doaj +1 more source
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Automatic Cough Detection from Audio Signals Using Deep Learning and Hybrid CNN–SVM Models [PDF]
Coughing is a natural physiological reflex that helps maintain respiratory health by clearing the airways of irritants, fluids, and pathogens. It also serves as a key clinical indicator for various respiratory conditions, including asthma, infections ...
Barkani Fatima +2 more
doaj +1 more source
Speech-Based Vehicle Movement Control Solution
The article describes a speech-based robotic prototype designed to aid the movement of elderly or handicapped individuals. Mel frequency cepstral coefficients (MFCC) are used for the extraction of speech features and a deep belief network (DBN) is ...
Gurpreet Kaur +2 more
doaj +1 more source
Mel Frequency Cepstral Coefficient: A Review [PDF]
Shalbbya Ali +3 more
openaire +1 more source
Ion‐Gating Reservoir Computing for Preprocessing‐Free Speech Recognition from Throat Vibrations
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
The subject matter of the study is the analysis of the influence of pre-processing stages of the audio on the accuracy of speaker language regognition. The importance of audio pre-processing has grown significantly in recent years due to its key role in
Olesia Barkovska, Anton Havrashenko
doaj +3 more sources
Speech emotion recognition (SER) involves identifying a speaker’s emotional state from their speech utterance. Prior research has explored various cepstral features for developing SER systems. Among these, Mel-frequency cepstral coefficients (MFCC)
Nagarajan Sugan +2 more
doaj +1 more source
Soft Active Electromyography Interface for Machine Learning‐Enabled Silent Speech Recognition
A soft, hand‐worn electromyography interface enables intent‐driven silent speech recognition without continuous facial attachment. The device integrates liquid‐metal interconnects, a transparent flexible circuit, and elastomer encapsulation with a fingertip electrode that contacts perioral muscles only on demand.
Yuta Kurotaki +8 more
wiley +1 more source

