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
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.
Ormeño-Arriagada P +4 more
europepmc +2 more sources
SSM2Mel: State Space Model to Reconstruct Mel Spectrogram from the EEG [PDF]
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Decoding speech from brain signals is a challenging research problem that holds significant importance for studying speech processing in the brain. Although breakthroughs have been made in reconstructing the mel spectrograms of audio stimuli perceived by
Cun-Hang Fan +4 more
semanticscholar +5 more sources
Voice pathology identification using mel spectrogram features and deep learning
Signal, Image and Video ProcessingVoice pathology is very important in the identification of vocal disorders. Traditional methods of diagnosing voice disorders using voice pathology are expensive, time-consuming, and subjective.
R. Bashir +6 more
semanticscholar +3 more sources
Mel-Refine: A Plug-and-Play Approach to Refine Mel-Spectrogram in Audio Generation
Automatic Speech Recognition & UnderstandingMainstream Text-to-Audio (TTA) models that rely on Mel-spectrograms often struggle to generate audio with rich content, leading to blurred or incoherent outputs. This stems from an inability to model intricate spectral details and textures.
Hongming Guo +10 more
semanticscholar +2 more sources
A Machine Learning Approach to Voice-Based Parkinson Disease Screening Using Multiview Spectrogram and Speech Recognition Features: Diagnostic Study. [PDF]
JMIR Med InformBackgroundParkinson disease frequently manifests early vocal impairment, motivating the development of noninvasive and scalable digital screening tools.
Zahir A +5 more
europepmc +2 more sources
Methods in Ecology and EvolutionPassive acoustic monitoring (PAM) is an important tool for wildlife monitoring. Deep learning, particularly convolutional neural networks (CNNs), has become the standard approach for developing bioacoustic classifiers.
Lorène Jeantet +4 more
doaj +2 more sources
Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
Applied SciencesAcoustic data inherently contain a variety of information, including indicators of abnormal symptoms. In this study, we propose a method for detecting abnormal symptoms by converting acoustic data into spectrogram representations and applying a deep ...
Seong-Yoon Kim +3 more
doaj +2 more sources
Cough Recognition Based on Mel-Spectrogram and Convolutional Neural Network. [PDF]
Front Robot AI, 2021 In daily life, there are a variety of complex sound sources. It is important to effectively detect certain sounds in some situations. With the outbreak of COVID-19, it is necessary to distinguish the sound of coughing, to estimate suspected patients in the population.
Zhou Q +7 more
europepmc +6 more sources
AMSVT: audio Mel-spectrogram vision transformer for spoken Arabic digit recognition [PDF]
Indonesian Journal of Electrical Engineering and Computer ScienceThis work presents a novel model to recognize spoken digits in the Arabic language. Due to the transformer-based models' tremendous success in natural language processing (NLP), several attempts have been made to extend transformer-based designs to other
Omayma Mahmoudi +2 more
semanticscholar +2 more sources