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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
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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
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Mel-Weighted Single Frequency Filtering Spectrogram for Dialect Identification
IEEE Access, 2020 In this study, we propose Mel-weighted single frequency filtering (SFF) spectrograms for dialect identification. The spectrum derived using SFF has high spectral resolution for harmonics and resonances while simultaneously maintaining good time ...
Sudarsana Reddy Kadiri +2 more
exaly +3 more sources
Applied Sciences (Switzerland), 2023 Predicting the remaining time before the next earthquake based on seismic signals generated in a laboratory setting is a challenging research task that is of significant importance for earthquake hazard assessment.
Tao Xu, Wen Chen, Zhang Bo
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Acoustic Signatures of Hive: Detecting Queen Bee Absence Through Machine Learning of Short Audio Segments [PDF]
InsectsHoneybee population decline poses a serious threat to global biodiversity and agricultural productivity, underscoring the need for continuous and non-invasive hive monitoring solutions.
Pablo Ormeño-Arriagada +4 more
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A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study [PDF]
JMIR Medical InformaticsBackgroundParkinson disease frequently manifests early vocal impairment, motivating the development of noninvasive and scalable digital screening tools.
Arifa Zahir +5 more
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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
Heng Siong Lim +2 more
exaly +3 more sources
Applied Sciences, 2022 The performance of speaker recognition systems is very well on the datasets without noise and mismatch. However, the performance gets degraded with the environmental noises, channel variation, physical and behavioral changes in speaker.
Wondimu Lambamo +2 more
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Research on Speech Synthesis Based on Mixture Alignment Mechanism
Sensors, 2023 In recent years, deep learning-based speech synthesis has attracted a lot of attention from the machine learning and speech communities. In this paper, we propose Mixture-TTS, a non-autoregressive speech synthesis model based on mixture alignment ...
Yan Deng +4 more
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Micromachines, 2021 This study compared popular Deep Learning (DL) architectures to classify machining surface roughness using sound and force data. The DL architectures considered in this study include Multi-Layer Perceptron (MLP), Convolution Neural Network (CNN), Long ...
Binayak Bhandari
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