AIMC Topic: Sound

Clear Filters Showing 101 to 110 of 137 articles

Wet and dry cough classification using cough sound characteristics and machine learning: A systematic review.

International journal of medical informatics
BACKGROUND: Distinguishing between productive (wet) and non-productive (dry) cough types is important for evaluating respiratory health, assisting in differential diagnosis, and monitoring disease progression. However, assessing cough type through th...

Using tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and computing costs limit the field's adoption. Generalizable pretrained networks can overcome these costs, but...

Rapid detection of fish calls within diverse coral reef soundscapes using a convolutional neural networka).

The Journal of the Acoustical Society of America
The quantity of passive acoustic data collected in marine environments is rapidly expanding; however, the software developments required to meaningfully process large volumes of soundscape data have lagged behind. A significant bottleneck in the anal...

Scene-dependent sound event detection based on multitask learning with deformable large kernel attention convolution.

PloS one
Sound event detection (SED) and acoustic scene classification (ASC) are closely related tasks in environmental sound analysis. Given the interrelationship between sound events and scenes, some previous studies have proposed using the multitask learni...

[Bowel Sounds Detection Method Based on ResNet-BiLSTM and Attention Mechanism].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Bowel sounds can reflect the movement and health status of the gastrointestinal tract. However, the traditional manual auscultation method has subjective deviation and is time-consuming and labor-intensive. In order to better assist doctors in diagno...

Decision Tree Versus Linear Support Vector Machine Classifier in the Screening of Medial Speech Sounds: A Quest for a Sound Rationale.

Studies in health technology and informatics
This paper describes the latest development in the classification stage of our Speech Sound Disorder (SSD) Screening algorithm and presents the results achieved by using two classifier models: the Classification and Regression Tree (CART)-based model...

A Multimodal Dataset for Automatic Edge-AI Cough Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while ...

Biodiversity assessment using passive acoustic recordings from off-reef location-Unsupervised learning to classify fish vocalization.

The Journal of the Acoustical Society of America
We present the quantitative characterization of Grande Island's off-reef acoustic environment within the Zuari estuary during the pre-monsoon period. Passive acoustic recordings reveal prominent fish choruses. Detailed characteristics of the call emp...

Neural network for multi-exponential sound energy decay analysis.

The Journal of the Acoustical Society of America
An established model for sound energy decay functions (EDFs) is the superposition of multiple exponentials and a noise term. This work proposes a neural-network-based approach for estimating the model parameters from EDFs. The network is trained on s...

A Cough-based deep learning framework for detecting COVID-19.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
This paper presents a deep learning framework for detecting COVID-19 positive subjects from their cough sounds. In particular, the proposed approach comprises two main steps. In the first step, we generate a feature representing the cough sound by co...