The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878239 | PMC |
| http://dx.doi.org/10.3390/s22041535 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Revolutionizing colorectal cancer detection: A breakthrough in microbiome data analysis.
PLoS One
January 2025
Department of Computer Science, Faculty of Computing, Federal University of Lafia, Lafia, Nasarawa State, Nigeria.
Article Synopsis
- The emergence of Next Generation Sequencing (NGS) technology has transformed clinical diagnostics, providing extensive microbiome data for personalized medicine.
- Despite its potential, microbiome data's complexity and variability pose challenges for traditional statistical and machine learning approaches, including deep learning.
- The paper presents a novel feature engineering technique that combines two data feature sets, significantly improving the Deep Neural Network's performance in colorectal cancer detection, raising the Area Under the Curve (AUC) from 0.800 to 0.923, thus enhancing microbiome data analysis and disease detection capabilities.
Hypercapnic warm-up and re-warm-up-A novel experimental approach in swimming sprint.
PLoS One
January 2025
Department of Physiology and Biochemistry, Faculty of Physical Education and Sport Science, Wroclaw University of Health and Sport Sciences, Wroclaw, Poland.
The purpose of this study was to determine the effective warm-up protocol using an added respiratory dead space (ARDS) 1200 ml volume mask to determine hypercapnic conditions, on the swimming velocity of the 50 m time trial front crawl. Eight male members of the university swimming team, aged 19-25, performed three different warm-up protocols: 1) standardized warm-up in water (WUCON); 2) hypercapnic warm-up in water (WUARDS); 3) hypercapnic a 20-minute transition phase on land, between warm-up in water and swimming test (RE-WUARDS). The three warm-up protocols were implemented in random order every 7th day.
View Article and Find Full Text PDFscHNTL: single-cell RNA-seq data clustering augmented by high-order neighbors and triplet loss.
Bioinformatics
January 2025
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Sichuan 611756, China.
Motivation: The rapid development of single-cell RNA sequencing (scRNA-seq) has significantly advanced biomedical research. Clustering analysis, crucial for scRNA-seq data, faces challenges including data sparsity, high dimensionality, and variable gene expressions. Better low-dimensional embeddings for these complex data should maintain intrinsic information while making similar data close and dissimilar data distant.
View Article and Find Full Text PDFRadiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.
Med Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
View Article and Find Full Text PDFIntroducing an integrated approach for fire safety assessment in healthcare facilities by interval valued neutrosophic-AHP and Fuzzy Inference System.
Heliyon
January 2025
Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Fire safety in healthcare facilities is extremely important due to limited evacuation capacity of occupants. Therefore, poor fire safety precautions lead to more fatalities and financial losses. This study introduces an effective fire risk management approach for healthcare buildings utilizing an interval valued neutrosophic-fuzzy framework.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!