IEEE

Deep Learning Driven Automated System for Speech Emotion Recognition

Abstract: Speech Emotion Recognition (SER) plays a significant role in many applications such as psychology and speech therapy, customer service, as well as human-computer interaction. We serve this ...
Scientific Research Publishing

Cybersecurity and Forensic Audio Analysis: Deepfake Detection Based on MFCC, Audio-Text Disconsistency, and Prosodic Features ()

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 ...
Frontiers

Noninvasive prescreening of pediatric adenoid hypertrophy using quantified MFCC statistics and clinical features: development and external validation

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 ...
IEEE

Classroom Acoustic Event Detection System

Abstract: Classroom environments often struggle with maintaining optimal acoustic conditions for effective learning. Traditional monitoring methods are manual, time-consuming, and unreliable. This ...
GitHub

Continuous speech recognition with ESP32 for numbers (0--9) using a 1D CNN / MFCC

With this project I want to try out if its possible to detect the spoken numbers "zero" to "nine" by using an ESP32 processor and MEMS microphone and ML 1D CNN network While there are a lot examples ...
C&EN

Protein–Ligand Interaction Energies from Quantum-Chemical Fragmentation Methods: Upgrading the MFCC-Scheme with Many-Body Contributions

You are free to share (copy and redistribute) this article in any medium or format and to adapt (remix, transform, and build upon) the material for any purpose, even commercially within the parameters ...
Frontiers

Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning

Previous studies have classified major depression and healthy control groups based on vocal acoustic features, but the classification accuracy needs to be improved. Therefore, this study utilized deep ...