Results 31 to 40 of about 3,612,587 (208)

Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

Applied Sciences, 2019
This work introduces a new approach for automatic identification of crickets, katydids and cicadas analyzing their acoustic signals. We propose the building of a tool to identify this biodiversity.
Juan J. Noda, Carlos M. Travieso-González, David Sánchez-Rodríguez, Jesús B. Alonso-Hernández   +3 more
doaj   +1 more source

Identification of infant crying Using Mel-Frequency Cepstral Coefficient (MFCC) and Artificial Neural Network (ANN) methods

Signal and Image Processing Letters, 2023
The crying of infants aged 0-3 months can be classified according to their needs, as identified by Dunstan Baby Language, which consists of specific sounds denoting different needs. These sounds include "eairh" for discomfort caused by fart, "neh" indicating hunger, "heh" representing general discomfort, "owh" signaling tiredness or sleepiness, and "eh"
Ahmad Azhari, Intan Destiyanti
openaire   +1 more source

Unsupervised Classification of Hydrophone Signals With an Improved Mel-Frequency Cepstral Coefficient Based on Measured Data Analysis

IEEE Access, 2019
Recently, passive detection technology has developed the ability to detect surface ships based on the noise emissions recorded by hydrophones, making it possible in some cases to classify surface ships.
Kunde Yang, Xingyue Zhou
doaj   +1 more source

Drone classification using RF signal based spectral features

Engineering Science and Technology, an International Journal, 2022
Drone detection and classification, important in military and civilian applications, are performed using different sensor signals. Proposed study handles this task using Radio Frequency (RF) signals utilizing basic machine learning methods.
Rabiye Kılıç, Nida Kumbasar, Emin Argun Oral, Ibrahim Yucel Ozbek   +3 more
doaj   +1 more source

Speech Emotion Recognition Using Cepstral Features Extracted With Gammatone Filter Banks Realized Based on ERB and Mel Frequency Scales

IEEE Access
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, Lakshmi Sutha Kumar, Nettimi Satya Sai Srinivas   +2 more
doaj   +1 more source

Ekstraksi Ciri Pelafalan Huruf Hijaiyyah Dengan Metode Mel-Frequency Cepstral Coefficients [PDF]

, 2019
Huruf hijaiyyah merupakan huruf penyusun ayat dalam Al Qur’an. Setiap hurufhijaiyyah memiliki karakteristik pelafalan yang berbeda. Tetapi dalam praktiknya,ketika membaca huruf hijaiyyah terkadang tidak memperhatikan kaidah bacaanmakhorijul huruf ...
INDRAWATY, YOULLIA, LUKMAN, RIZKI, DEWI, IRMA AMELIA   +2 more
core   +1 more source

Nonlinear acoustic analysis in the evaluation of occupational voice disorders

Medycyna Pracy, 2013
Background: Over recent years numerous papers have stressed that production of voice is subjected to the nonlinear processes, which cause aperiodic vibrations of vocal folds.
Ewa Niebudek-Bogusz, Jacek Grygiel, Paweł Strumiłło, Mariola Śliwińska-Kowalska   +3 more
doaj   +1 more source

Penerapan Metode Mel Frequency Cepstral Coefficients pada Sistem Pengenalan Suara Berbasis Desktop [PDF]

, 2023
Teknologi biometrik sedang menjadi tren teknologi dalam berbagai bidang kehidupan. Teknologi biometrik memanfaatkan bagian tubuh manusia sebagai alat ukur sistem yang memiliki keunikan disetiap individu. Suara merupakan bagian tubuh manusia yang memiliki
Islamuddin, Nur, Ajinurseto, Galih, Bakrim, La Ode   +2 more
core   +1 more source

Ion‐Gating Reservoir Computing for Preprocessing‐Free Speech Recognition from Throat Vibrations

Advanced Electronic Materials, EarlyView.
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, Akito Tateyama, Hina Kitano, Takashi Nakanishi, Kazuya Terabe, Takashi Tsuchiya   +5 more
wiley   +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