Summary Acoustic Based Emergency Vehicle Detection Using Ensemble of deep Learning Models - ScienceDirect pdf.sciencedirectassets.com
1,296 words - html page - View html page
One Line
The article explores the application of deep learning models in identifying and categorizing acoustic environments for emergency vehicle detection, with a focus on using Mel-frequency Cepstral Coefficient (MFCC) features and referencing studies on convolutional neural networks (CNNs).
Slides
Slide Presentation (12 slides)
Key Points
- Acoustic based emergency vehicle detection is done using an ensemble of deep learning models.
- The proposed ensemble model provides the highest accuracy of 98.7%.
- The dataset used for training the models is collected from Google Audioset ontology.
- Features are extracted using Mel-frequency Cepstral Coefficient (MFCC).
- Three deep neural network models (dense layer, Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN)) are investigated.
- Performance analysis of deep learning models is done with various machine learning models like Perceptron, SVM, decision tree, etc.
Summaries
35 word summary
This article discusses the use of deep learning models for acoustic-based emergency vehicle detection, analyzing and classifying sound environments using Mel-frequency Cepstral Coefficient (MFCC) features. The authors reference studies on convolutional neural networks (CNNs) for
81 word summary
This article discusses the use of acoustic-based emergency vehicle detection using an ensemble of deep learning models. The study focuses on analyzing and classifying acoustic environments using sound recordings. The authors extract features using Mel-frequency Cepstral Coefficient (MFCC) and
This summary discusses the use of deep learning models for acoustic-based emergency vehicle detection. The authors reference several studies that have utilized convolutional neural networks (CNNs) for sound classification, including environmental sound classification and speech recognition. They also mention the use of