Results 171 to 180 of about 3,612,587 (208)

Ball bearing fault detection using an acoustic based machine learning approach. [PDF]

Sci Rep
Chandrakala CB, Karumanchi SS, Raghudathesh GP, Madhwesh N.   +3 more
europepmc   +1 more source

Fault Diagnosis of Motor Bearing Transmission System Based on Acoustic Characteristics. [PDF]

Sensors (Basel)
Ma L, Zhang Y, Wang Z.
europepmc   +1 more source

Stacked convolutional neural network for emotion recognition using multi feature speech analysis. [PDF]

Sci Rep
Roy C, Kharel W, Das P, Panigrahi R, Hareesha KS, Pramanik M, Bhoi AK.   +6 more
europepmc   +1 more source

Material Classification from Non-Line-of-Sight Acoustic Echoes Using Wavelet-Acoustic Hybrid Feature Fusion. [PDF]

Sensors (Basel)
Alakuş DO, Türkoğlu İ.
europepmc   +1 more source

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   +4 more sources

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Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)

Infrared Physics and Technology, 2021
Abstract Experimentation and analysis of Functional near-infrared spectroscopy (fNIRS) in Brain-Computer Interface (BCI) has increasingly been studied as a communication possibility for patients who are severely paralyzed. This study has applied this technique to distinguish brain activities during four different mental tasks.
Jamshed Iqbal, Waqar Shahid Qureshi, Nasir Rashid   +2 more
exaly   +2 more sources

Speaker recognition using Mel frequency Cepstral Coefficients (MFCC) and Vector quantization (VQ) techniques

CONIELECOMP 2012, 22nd International Conference on Electrical Communications and Computers, 2012
This paper presents a fast and accurate automatic voice recognition algorithm. We use Mel frequency Cepstral Coefficient (MFCC) to extract the features from voice and Vector quantization technique to identify the speaker, this technique is usually used in data compression, it allows to model a probability functions by the distribution of different ...
Jorge Martínez, Héctor Pérez-Meana, Enrique Escamilla Hernández, Masahisa Mabo Suzuki   +3 more
exaly   +3 more sources

EKSTRAKSI CIRI EMOSI MANUSIA BERDASARKAN UCAPAN MENGGUNAKAN MEL-FREQUENCY CEPSTRAL COEFFICIENTS (MFCC)

Prosiding Sains Nasional dan Teknologi, 2018
Emosi merupakan perilaku manusia yang dapat diungkapkan dengan tingkah laku berupa raut wajah dan suara. Suara adalah suatu gelombang longitudinal yang merambat di udara. Pada kehidupan sehari-hari manusia berkomunikasi dengan menggunakan suara. Baik itu di dunia pendidikan, kantor, dan tempat umum lainnya.
Siti Helmiyah, Abdul Fadlil, Anton Yudhana   +2 more
openaire   +2 more sources

Mel Frequency Cepstral Coefficients (MFCC) based speaker identification in noisy environment using wiener filter

2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), 2014
Speech processing is now an emerging technology of signal processing. Some research areas of speech processing are recognition of speech, speaker identification (SI), speech synthesis etc. Speaker identification is important research area of speech processing. SI means identifying the speaker based on his spoken speech.
Nikita P Desai
exaly   +2 more sources

Cardiac Sound Classification Using Mel-Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN)

2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), 2018
Auscultation of heart sounds is usually used as an important way to identify symptoms of heart disease. In this case the experts need to concentrate on diagnosing heart sound abnormalities in humans. Finding various characteristics to classify heart sounds according to the group is a very important part.
Hanung Adi Nugroho, Noor Akhmad Setiawan
exaly   +2 more sources