Results 101 to 110 of about 8,304 (223)

Stressed Speech Emotion Recognition using feature fusion of Teager Energy Operator and MFCC

, 2022
In this paper, a novel feature fusion of Teager Energy Operator (TEO) and Mel Frequency Cepstral Coefficients (MFCC), as Teager-MFCC (T-MFCC) feature extraction technique, is used to recognize the stressed emotions from speech signal. TEO is a non-linear
Bandela, Surekha Reddy, Kumar, T. Kishore   +1 more
core  

Optimizing Integrated Features for Hindi Automatic Speech Recognition System

Journal of Intelligent Systems, 2018
An automatic speech recognition (ASR) system translates spoken words or utterances (isolated, connected, continuous, and spontaneous) into text format.
Dua Mohit, Aggarwal Rajesh Kumar, Biswas Mantosh   +2 more
doaj   +1 more source

Transfer learning based feature selection for feedforward neural network for speech emotion classifier

Системный анализ и прикладная информатика
This work discusses speech emotion recognition via custom feature engineering and feature selection techniques using mel-frequency cepstral coefficients as initial audio features. Proposed transfer learning approach consist in employing the backward-step
D. V. Krasnoproshin, M. I. Vashkevich
doaj   +1 more source

Audio Signal Classification using Mel-Frequency Cepstrum Coefficients and Deep Neural Network for Noon Saakin or Tanween Tajweed Rule Dataset

JOIV: International Journal on Informatics Visualization
The Al-Qur’an serves as a fundamental guide for Muslims, requiring both comprehension and practice. Accurate recitation according to tajweed rules is essential for a deeper understanding of its meaning.
Genta Hayindra Irawan, Husni Mubarok, Irani Hoeronis   +2 more
doaj   +1 more source

MAP prediction of pitch from MFCC vectors for speech reconstruction

, 2008
This work proposes a method of predicting pitch and voicing from mel-frequency cepstral coefficient (MFCC) vectors. Two maximum a posteriori (MAP) methods are considered.
Xu Shao, Ben Milner
core  

IMPLEMENTASI MFCC & CNN PADA GENDER VOICE RECOGNITION

Penelitian mengembangkan sistem Gender Voice Recognition menggunakan kombinasi Mel-Frequency Cepstral Coefficients (MFCC) untuk ekstraksi fitur dan Convolutional Neural Networks (CNN) untuk klasifikasi suara berdasarkan gender.
NUGROHO, HARDI TRI
core  

MFCC global features selection in improving speech emotion recognition rate

, 2015
Feature selection is one of the important aspects that contribute most to the emotion recognition system performance as well as the database and the classification technique used.
Salam, Md. Sah, Zaidan, Noor Aina
core  

Music Instrument Identification Using MFCC: Erhu as an Example

, 2012
[[abstract]]In the analysis of musical acoustics, we usually use the power spectrum to describe the difference between timbres from two music instruments.
Chih-Wen Weng;Cheng-Yuan Lin;Jyh-Shing Roger Jang
core  

Algorithm 2: Similarity Score of Normalized Training and Testing MFCC.

, 2014
Algorithm 2: Similarity Score of Normalized Training and Testing MFCC.
Jiping Sun (521543), Zhigang Luo (474835), Xianglilan Zhang (521542)   +2 more
core   +1 more source

Using Reversed MFCC and IT-EM for Automatic Speaker Verification

, 2012
This paper proposes text independent automatic speaker verification system using IMFCC (Inverse/ Reverse Mel Frequency Coefficients) and IT-EM (Information Theoretic Expectation Maximization).
Sania Bhatti, Tariq Jamil Saifullah Khanzada, Sheeraz Memon   +2 more
core