Proceedings ArticleDOI
Environmental sound recognition using Gaussian mixture model and neural network classifier
S. P. Mohanapriya,E. P. Sumesh,R. Karthika +2 more
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TLDR
This paper deals with the prototype modeling for environmental sound recognition and shows a better efficiency than the already existing method.Abstract:
Environmental sound recognition is an audio scene identification process in which a person's location is found by analyzing the background sound. This paper deals with the prototype modeling for environmental sound recognition. Sound recognition involves the collection of audio data, extraction of important features, clustering of similar features and their classification. The Mel frequency cepstrum co-efficients are extracted. These features are used for clustering by a Gaussian mixture model which is a probabilistic model. Neural Network classifier is used for classification of the features and to identify the environmental audio scene. The implementation is done with the help of MATLAB. Five major environmental sounds which include the sound of car, office, restaurant, street, subway are considered. This shows a better efficiency than the already existing method. The efficiency achieved in this method is 98.9%.read more
Citations
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Proceedings ArticleDOI
Deep Convolutional Neural Network with Transfer Learning for Environmental Sound Classification
TL;DR: Wang et al. as mentioned in this paper proposed a new convolutional neural network (CNN) model using transfer learning technology for ESC task, which represented sound as RGB image, where the red channel correspond to the Log-Mel spectrogram, the green channel corresponds to the scalogram, and the blue channel corresponding to the Mel frequency cepstrum coefficient (MFCC).
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Environmental Noise Monitoring Using Distributed IoT Sensor Nodes
TL;DR: A system to be highly scalable, easy to use, low-cost, and low-powered to encourage its widespread adoption and has an average classification accuracy of 72% when subjected to four common environmental noise sources.
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Automatic detection of tree cutting in forests using acoustic properties
TL;DR: In this paper , the authors proposed an algorithm for automatic detection of tree cutting in forest, which is based on distance between parameters, along with K-means clustering, GMM and PCA for comparison.
Book ChapterDOI
Automated Industrial Sound Power Alert System
TL;DR: In this paper, the authors proposed an automated sound power alert system at an industrial level that displays the decibel value of noise around heavy machinery and if it exceeds the threshold value of 80 dB, it notifies the authority by delivering an automated text message.
References
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Proceedings ArticleDOI
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TL;DR: This survey will offer a qualitative and elucidatory survey on recent developments of environmental sound recognition, and includes three parts: i) basic environmental sound processing schemes, ii) stationary ESR techniques and iii) non-stationary E SR techniques.
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