Results 21 to 30 of about 28,361 (199)

The Mel-Frequency Cepstral Coefficients In The Context Of Singer Identification. [PDF]

, 2005
[TODO] Add abstract here.
Annamaria Mesaros, Jaakko Astola
openaire   +2 more sources

Linear versus mel frequency cepstral coefficients for speaker recognition [PDF]

2011 IEEE Workshop on Automatic Speech Recognition & Understanding, 2011
Mel-frequency cepstral coefficients (MFCC) have been dominantly used in speaker recognition as well as in speech recognition. However, based on theories in speech production, some speaker characteristics associated with the structure of the vocal tract, particularly the vocal tract length, are reflected more in the high frequency range of speech.
Xinhui Zhou, Daniel Garcia-Romero, Ramani Duraiswami, Carol Y. Espy-Wilson, Shihab A. Shamma   +4 more
openaire   +1 more source

Investigation of Lung Sounds Features for Detection of Bronchitis and COPD Using Machine Learning Methods

Vìsnik Nacìonalʹnogo Tehnìčnogo Unìversitetu Ukraïni Kììvsʹkij Polìtehnìčnij Ìnstitut: Serìâ Radìotehnìka, Radìoaparatobuduvannâ, 2021
The study is dedicated to the issue of investigation of the lung sounds digital analysis processing methods, searching for new informative features of pathological respiratory sounds and using machine learning methods for classifying the state of the ...
H. S. Porieva, K. O. Ivanko, C. I. Semkiv, V. I. Vaityshyn   +3 more
doaj   +1 more source

Sound Texture Generative Model Guided by a Lossless Mel-Frequency Convolutional Neural Network

IEEE Access, 2018
Rainstorms, insect swarms, and galloping horses produce “sound textures,”which are the resulting natural sounds of many similar acoustic events.
Weiwei Liu, Guangjie Liu, Xiaopeng Ji, Jiangtao Zhai, Yuewei Dai   +4 more
doaj   +1 more source

Predicting the Remaining Time before Earthquake Occurrence Based on Mel Spectrogram Features Extraction and Ensemble Learning

Applied Sciences, 2023
Predicting the remaining time before the next earthquake based on seismic signals generated in a laboratory setting is a challenging research task that is of significant importance for earthquake hazard assessment.
Bo Zhang, Tao Xu, Wen Chen, Chongyang Zhang   +3 more
doaj   +1 more source

Emotion Recognition using Mel-Frequency Cepstral Coefficients

Journal of Natural Language Processing, 2007
In this paper, we propose a new approach to emotion recognition. Prosodic features are currently used in most emotion recognition algorithms. However, emotion recognition algorithms using prosodic features are not sufficiently accurate. Therefore, we focused on the phonetic features of speech for emotion recognition.
Sato, Nobuo, Obuchi, Yasunari
openaire   +2 more sources

MFCC-Based Sound Classification of Honey Bees [PDF]

International Journal of Electronics and Telecommunications
—Smart beekeeping is a rapidly developing field. Automated detection and classification of honey bees opens many new opportunities for studies on their behavior.
Urszula Libal, Pawel Biernacki
doaj   +1 more source

Mel Frequency Cepstral Coefficients For Music Modeling. [PDF]

, 2000
[TODO] Add abstract here.
openaire   +1 more source

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

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

Feature selection for emotion recognition in speech: a comparative study of filter and wrapper methods [PDF]

PeerJ Computer Science
Feature selection is essential for enhancing the performance and reducing the complexity of speech emotion recognition models. This article evaluates various feature selection methods, including correlation-based (CB), mutual information (MI), and ...
Alaa Altheneyan, Aseel Alhadlaq
doaj   +2 more sources