1 Department of Computer and Instructional Technologies Education, Gazi Faculty of Education, Gazi University, Ankara, Türkiye. 2 Department of Forensic Informatics, Institute of Informatics, Gazi ...

MFCC feature extraction with librosa Multiple model architectures (CNN, LSTM, CNN-LSTM hybrid) Support for datasets: RAVDESS, TESS, EMO-DB Real-time emotion prediction ...

Abstract: In the field of speech-based emotion recognition, Mel-Frequency Cepstral Coefficients (MFCCs) are widely used as representative acoustic features. Recent approaches have transformed MFCCs ...

In a dual-center cross-sectional study (N = 202), Center 1 (Capital Center for Children’s Health, Capital Medical University, n = 161) served as the development cohort and Center 2 (College of ...

This study examines an important question regarding the developmental trajectory of neural mechanisms supporting facial expression processing. Leveraging a rare intracranial EEG (iEEG) dataset ...

Every week, Kristopher Pollard from Milwaukee Film and Radio Milwaukee’s Dori Zori talk about movies — because that’s what you do when you’re Cinebuds. We’ve got a real value-for-your-money episode ...

A research team from Tsinghua University has developed an optical computing system that dramatically reduces latency in feature extraction - one of the most critical stages in real-time data ...

日本語音声の感情認識を行うAPIです。産総研（AIST）が開発したHuBERT-largeベースのKushinadaモデルを使用し、JTES（Japanese Twitter-based Emotional Speech）データセットで学習された感情分類を実行します。

Words may be simple, fleeting thoughts. But typography gives them weight. In that moment, they’re no longer just text—they become feeling. In the fast, flat world of online content, typography was ...