AIMC Topic: Sound Spectrography

Clear Filters Showing 1 to 10 of 74 articles

Acoustic analysis of bottlenose dolphin vocalizations for behavioral classification in controlled settings.

PloS one
Understanding how bottlenose dolphins adjust their vocal behavior in response to daily routines can provide insights into social communication and welfare assessment in managed care environments. This study presents a detailed analysis of bottlenose ...

Auto-Masked Audio Spectrogram Transformer for depression detection from speech.

Journal of affective disorders
BACKGROUND: Depression is a psychological disorder characterized by altered self-referential cognition and impaired emotional expression. Traditional diagnostic methods can be costly or intrusive, while Speech-based analysis offers an accessible alte...

A cough detection method based on the conformer-BiLSTM model.

Biomedical physics & engineering express
Cough is a common symptom of respiratory disease, and its detection is a basic step in cough sound analysis. Manual cough sound segmentation is tedious, subjective, and inefficient. Cough sounds from real-world scenarios can be collected in various e...

Listening deeper: neural networks unravel acoustic features in preterm infant crying.

Scientific reports
Early infant crying provides critical insights into neurodevelopment, with atypical acoustic features linked to conditions such as preterm birth. However, previous studies have focused on limited and specific acoustic features, hindering a more compr...

Multiclass CNN Approach for Automatic Classification of Dolphin Vocalizations.

Sensors (Basel, Switzerland)
Monitoring dolphins in the open sea is essential for understanding their behavior and the impact of human activities on the marine ecosystems. Passive Acoustic Monitoring (PAM) is a non-invasive technique for tracking dolphins, providing continuous d...

Pre-trained convolutional neural networks identify Parkinson's disease from spectrogram images of voice samples.

Scientific reports
Machine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the conve...

Endpoint-aware audio-visual speech enhancement utilizing dynamic weight modulation based on SNR estimation.

Neural networks : the official journal of the International Neural Network Society
Integrating visual features has been proven effective for deep learning-based speech quality enhancement, particularly in highly noisy environments. However, these models may suffer from redundant information, resulting in performance deterioration w...

Elephant Sound Classification Using Deep Learning Optimization.

Sensors (Basel, Switzerland)
Elephant sound identification is crucial in wildlife conservation and ecological research. The identification of elephant vocalizations provides insights into the behavior, social dynamics, and emotional expressions, leading to elephant conservation....

Prediction of Arteriovenous Access Dysfunction by Mel Spectrogram-based Deep Learning Model.

International journal of medical sciences
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. This prospecti...

Deep transfer learning-based bird species classification using mel spectrogram images.

PloS one
The 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, ...