EEG driving fatigue detection based on log-Mel spectrogram and convolutional recurrent neural networks [PDF]
Frontiers in Neuroscience, 2023 Driver fatigue detection is one of the essential tools to reduce accidents and improve traffic safety. Its main challenge lies in the problem of how to identify the driver's fatigue state accurately.
Dongrui Gao +4 more
doaj +3 more sources
IEEE Access, 2023 Speech emotion recognition aims to automatically identify and classify emotions from speech signals. It plays a crucial role in various applications such as human-computer interaction, affective computing, and social robotics. Over the years, researchers
Kah Liang Ong +2 more
exaly +4 more sources
Multi-Input Speech Emotion Recognition Model Using Mel Spectrogram and GeMAPS. [PDF]
Sensors (Basel), 2023 The existing research on emotion recognition commonly uses mel spectrogram (MelSpec) and Geneva minimalistic acoustic parameter set (GeMAPS) as acoustic parameters to learn the audio features.
Toyoshima I +4 more
europepmc +2 more sources
Estimation of Elbow Wall Thinning Using Ensemble-Averaged Mel-Spectrogram with ResNet-like Architecture [PDF]
Sensors, 2022 An elbow wall thinning diagnosis method by highlighting the stationary characteristics of the operating loop is proposed. The accelerations of curved pipe surfaces were measured in a closed test loop operating at a constant pump rpm, combined with curved
Jonghwan Kim +3 more
doaj +2 more sources
Research on automatic assessment of the severity of unilateral vocal cord paralysis based on Mel-spectrogram and convolutional neural networks. [PDF]
Biomed Eng OnlineThis study aims to develop an AI-powered platform using Mel-spectrogram analysis and convolutional neural networks (CNN) to automate the severity assessment of unilateral vocal fold paralysis (UVCP) through voice analysis, providing an objective basis ...
Ma S +8 more
europepmc +2 more sources
MelTrans: Mel-Spectrogram Relationship-Learning for Speech Emotion Recognition via Transformers. [PDF]
Sensors (Basel)Speech emotion recognition (SER) is not only a ubiquitous aspect of everyday communication, but also a central focus in the field of human–computer interaction.
Li H, Li J, Liu H, Liu T, Chen Q, You X.
europepmc +2 more sources
Deep transfer learning-based bird species classification using mel spectrogram images. [PDF]
PLoS OneThe classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and ...
Baowaly MK +7 more
europepmc +2 more sources
An improved ViT model for music genre classification based on mel spectrogram. [PDF]
PLoS OneAutomating the task of music genre classification offers opportunities to enhance user experiences, streamline music management processes, and unlock insights into the rich and diverse world of music.
Wu P +6 more
europepmc +2 more sources
Prediction of Arteriovenous Access Dysfunction by Mel Spectrogram-based Deep Learning Model. [PDF]
Int J Med SciBackground: The early detection of arteriovenous (AV) access dysfunction is crucial for maintaining the patency of vascular access. This study aimed to use deep learning to predict AV access malfunction necessitating further vascular management. Methods:
Chung TL +8 more
europepmc +2 more sources
Newborns' Language Discrimination May Not Reflect Sensitivity to Speech Rhythm: Evidence From Computational Modeling. [PDF]
Dev SciABSTRACT Human newborns are able to discriminate between certain languages but not others. This ability has long been attributed to sensitivity to rhythm—the temporal regularities in speech of different languages. Here, we demonstrate through a series of computational simulations that this discrimination behavior can be achieved using no temporal ...
Famularo RL +3 more
europepmc +2 more sources