Sex differences in vocal learning ability in songbirds are linked with differences in flexible rhythm pattern perception. [PDF]
Rouse AA, Patel AD, Wainapel S, Kao MH.
europepmc +1 more source
This work presents a deep learning model to autonomously recognize and classify the secretion retention into three levels for patients receiving invasive mechanical ventilation, achieving 89.08% accuracy. This model can be implemented to ventilators by edge computing, whose feasibility is approved.
Shuai Wang +6 more
wiley +1 more source
The pale spear-nosed bat: A neuromolecular and transgenic model for vocal learning. [PDF]
Vernes SC +12 more
europepmc +1 more source
Review of Memristors for In‐Memory Computing and Spiking Neural Networks
Memristors uniquely enable energy‐efficient, brain‐inspired computing by acting as both memory and synaptic elements. This review highlights their physical mechanisms, integration in crossbar arrays, and role in spiking neural networks. Key challenges, including variability, relaxation, and stochastic switching, are discussed, alongside emerging ...
Mostafa Shooshtari +2 more
wiley +1 more source
Evidence for maintenance of key components of vocal learning in ageing budgerigars despite diminished affiliative social interaction. [PDF]
Moussaoui B +4 more
europepmc +1 more source
Evaluating Features and Metrics for High-Quality Simulation of Early Vocal Learning of Vowels [PDF]
Branislav Gerazov +5 more
openalex +1 more source
Adaptive multi‐indicator contrastive predictive coding is introduced as a self‐supervised pretraining framework for multivariate EHR time series. An adaptive sliding‐window algorithm and 2D convolutional neural network encoder capture localized temporal patterns and global indicator dependencies, enabling label‐efficient disease prediction that ...
Hongxu Yuan +3 more
wiley +1 more source
Publisher Correction: Machine learning and statistical classification of birdsong link vocal acoustic features with phylogeny [PDF]
Moises Rivera +3 more
openalex +1 more source
Robust Dysarthric Speech Recognition with GAN Enhancement and LLM Correction
This study tackles dysarthric speech recognition by combining generative adversarial network (GAN)‐generated synthetic data with large language model (LLM)‐based error correction. The approach integrates three key elements: an improved CycleGAN to generate synthetic dysarthric speech for data augmentation, a multimodal automatic speech recognition core
Yibo He +3 more
wiley +1 more source
Tonality over a broad frequency range is linked to vocal learning in birds. [PDF]
Faiß M, Riede T, Goller F.
europepmc +1 more source

