Results 61 to 70 of about 3,617,625 (210)

Mel Frequency Cepstral Coefficients: An Evaluation Of Robustness Of Mp3 Encoded Music.

, 2006
[TODO] Add abstract here.
Sigurdsson, Sigurdur, Petersen, Kaare Brandt, Lehn-Schiøler, Tue   +2 more
openaire   +3 more sources

Hardware Accelerator Design of DCT Algorithm With Unique-Group Cosine Coefficients for Mel-Scale Frequency Cepstral Coefficients

IEEE Access, 2022
This study presents a compact $L$ -points discrete cosine transform (DCT) hardware accelerator for $M$ -points Mel-scale Frequency Cepstral Coefficients (MFCC).
Shin-Chi Lai, Ying-Hsiu Hung, Yi-Chang Zhu, Szu-Ting Wang, Qi-Xian Huang, Ming-Hwa Sheu, Wen-Ho Juang   +6 more
doaj   +1 more source

Efficient Masked Autoencoder for Birdsong Representation with Applications on Wild Bird Species Classification

Integrative Zoology, EarlyView.
Research on mosquito feeding preferences and the malaria parasites they transmit is essential for understanding the interactions between hosts, vectors, and parasites. In this study, vertebrate hosts were identified in 72 mosquitoes. Most blood meals (58.7%) came from birds, representing 25 species, while 40.0% came from mammals (13 species), and 1.3 ...
Qin Zhang, Shipeng Hu, Hengrui Wang, Jizheng Yi, Guoxiong Zhou, Xiangyu Shen, Weixiong Peng, Zhishu Xiao, Aibin Chen   +8 more
wiley   +1 more source

Mel Frequency Cepstral Coefficients For Music Modeling.

, 2000
[TODO] Add abstract here.
openaire   +2 more sources

Analysis of mel-frequency cepstral coefficients as features used for automatic speaker recognition [PDF]

, 2014
Рад је окренут ка анализи мел-фреквенцијских кепстралних коефицијената као обележја говорника која се користе при аутоматском препознавању говорника. Испитан је утицај промене облика чујних критичних опсега као и модификације енергије у њима на тачност ...
Jokić, Ivan
core   +6 more sources

Acoustic lung signals analysis based on Mel frequency cepstral coefficients and self-organizing maps

Revista Facultad de Ingeniería, 2016
This study analyzes acoustic lung signals with different abnormalities, using Mel Frequency Cepstral Coefficients (MFCC), Self-Organizing Maps (SOM), and K-means clustering algorithm. SOM models are known as artificial neural networks than can be trained
Álvaro David Orjuela-Cañón, Hugo Fernando Posada-Quintero   +1 more
doaj   +1 more source

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

Advanced Electronic Materials, Volume 12, Issue 19, 12 October 2026.
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

Palmprint recognition based on Mel frequency Cepstral coefficients feature extraction [PDF]

, 2010
Palmprint identification is a measurement of palmprint features for recognizing the identity of a user. Palmprint is universal, easy to capture and it does not change much across time.
Maged M.M. Fahmy, Fahmy, Maged M.M.
core   +1 more source

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques

Health Care Science, Volume 5, Issue 5, Page 514-531, October 2026.
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent difficulties in social communication, social interaction, and repetitive behaviors. Early diagnosis is often hindered by subjective clinical assessments and limited data availability.
Sahar Alkhaibari, Feng Dong
wiley   +1 more source

Speech Emotion Recognition Method Based on Support Vector Machine and Suprasegmental Acoustic Features

Доклады Белорусского государственного университета информатики и радиоэлектроники
The problem of recognizing emotions in a speech signal using mel-frequency cepstral coefficients using a classifier based on the support vector machine has been studied. The RAVDESS data set was used in the experiments.
D. V. Krasnoproshin, M. I. Vashkevich
doaj   +1 more source