Results 41 to 50 of about 1,688 (176)
IEEE Access, 2018 The end-to-end learning approaches were proposed for an arithmetic expression recognition task in the Baidu Meizu Deep Learning Competition by a deep convolutional neural network (DCNN) with parallel dense layers and component-connection-based detection ...
Yuxiang Jiang +2 more
doaj +1 more source
Improved Multi-Model Classification Technique for Sound Event Detection in Urban Environments
Applied Sciences, 2022 Sound event detection (SED) plays an important role in understanding the sounds in different environments. Recent studies on standardized datasets have shown the growing interest of the scientific community in the SED problem, however, these did not pay ...
Muhammad Salman Khan +7 more
doaj +1 more source
Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM‐Now
Journal of Forecasting, EarlyView.ABSTRACT We propose PRISM‐Now, a novel ensemble forecasting system for near‐term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante.
Beomseok Seo, Hyungbae Cho, Dongjae Lee
wiley +1 more source
Main Melody Estimation with Source-Filter NMF and CRNN [PDF]
, 2018 Estimating the main melody of a polyphonic audio recording remains a challenging task. We approach the task from a classification perspective and adopt a convolutional recurrent neural network (CRNN) architecture that relies on a particular form of pretraining by source-filter nonnegative matrix factorisation (NMF).
Dogac Basaran +2 more
openaire +2 more sources
CRNN: Collaborative Representation Neural Networks for Hyperspectral Anomaly Detection
Remote Sensing, 2023 Hyperspectral anomaly detection aims to separate anomalies and backgrounds without prior knowledge. The collaborative representation (CR)-based hyperspectral anomaly detection methods have gained significant interest and development because of their interpretability and high detection rate.
Yuxiao Duan +2 more
openaire +2 more sources
Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions
Applied Sciences, 2020 As people communicate with each other, they use gestures and facial expressions as a means to convey and understand emotional state. Non-verbal means of communication are essential to understanding, based on external clues to a person’s emotional state ...
Chang-Min Kim +3 more
doaj +1 more source
Salivary Extracellular Vesicles: Paradigm Shift in Liquid Biopsy Diagnostics
Journal of Extracellular Biology, Volume 5, Issue 7, July 2026.ABSTRACT Extracellular vesicles (EVs), lipid bilayer nanoparticles released by virtually all cells, serve as essential messengers for intercellular communication. Due to their involvement in several pathophysiological processes, EVs have recently gained considerable attention as potentially diagnostic and prognostic biomarkers for various illnesses ...
Kwanele Xulu +3 more
wiley +1 more source
Dilated-Convolutional Recurent Neural Network untuk Klasifikasi Genre Musik
JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)Dalam era digital, pemanfaatan teknologi untuk mengelompokkan genre musik secara otomatis menjadi sangat penting, terutama untuk aplikasi seperti rekomendasi musik, analisis tren musik, dan pengelolaan perpustakaan musik digital.
Mochammad Rizqul Fatichin +3 more
doaj +1 more source
On the Optimal Selection of Mel‐Frequency Cepstral Coefficients for Voice Deepfake Detection
Expert Systems, Volume 43, Issue 5, May 2026.ABSTRACT The continuous evolution of techniques for generating manipulated audio, known as voice deepfakes, and the widespread availability of tools that produce convincing forgeries have created an urgent need for reliable detection methods. This work considers the dimensionality of Mel‐Frequency Cepstral Coefficients (MFCCs) as a core design variable
Sergio A. Falcón‐López +3 more
wiley +1 more source
Convolutional Recurrent Neural Network for Fault Diagnosis of High-Speed Train Bogie
Complexity, 2018 Timely detection and efficient recognition of fault are challenging for the bogie of high-speed train (HST), owing to the fact that different types of fault signals have similar characteristics in the same frequency range.
Kaiwei Liang +3 more
doaj +1 more source