Environmental Noise Classification using LDA, QDA and ANN Methods

Tanweer, Safdar; Mobin, Abdul; Alam, Afshar

doi:10.17485/ijst/2016/v9i33/95628

Cited by 10 publications

(5 citation statements)

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“…Several studies have been conducted to classify environmental noise sources by using ESC. Tanweer et al [28] classified bus, market, and industrial noise using machine learning algorithms. Maijala et al [19] proposed a smart sensor system using Gaussian mixture models and artificial neural networks (ANNs) to classify the noise from a rock-crushing plant.…”

Section: Related Workmentioning

confidence: 99%

“…To overcome this limitation, environmental sound classification (ESC) has recently received considerable attention [6]. By using the ESC to classify the context of noise sources, manual work can be reduced, and effective decision-making for noise mitigation policies can be facilitated [19,28,30]. Moreover, since sound carries rich contextual information, ESC is widely used for intelligent urban sound monitoring in smart cities.…”

Section: Introductionmentioning

confidence: 99%

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Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network

Park,

Yoo,

Lee

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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In recent years, the application of deep learning to environmental sound classification (ESC) has received considerable attention owing to its powerful ability to recognize the context of urban sounds. In general, deep learning models with high accuracy require substantial computing and memory resources. Consequently, to apply complex deep learning models to ESC in the real world, model inference has been performed on cloud servers with powerful computing resources. However, heavy network traffic and security issues occur when inferences are performed on a cloud server. In addition, deploying a deep learning model trained on a single public ESC dataset may not be sufficient for classifying various classes of urban noise and emergency-related sounds. To address these problems, we propose an on-device, real-time urban sound monitoring system that can classify various urban sounds at low system construction costs. The proposed system consisted of an edge artificial intelligence (AI) node and a FIWARE-based server. To enable the real-time inference on a resource-constrained edge AI node, we developed a lightweight convolutional neural network (CNN) by adjusting the input and model configurations to achieve high accuracy with a low number of parameters. The model achieved 94.9% classification accuracy using only 331 K parameters on an integrated dataset that included 17 classes of urban noises and emergencies. Furthermore, a prototype of the proposed system was developed and evaluated to verify its feasibility. The prototype system was built at a cost of less than USD 50 and could perform the entire monitoring process every 3 s. We also visualized the sound monitoring data using Grafana on a FIWARE-based server.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network

Park,

Yoo,

Lee

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…Sub-band periodicity, sub-band entropy, and sub-band energy ratio are the typical band classification features applied for audio classification. Rule-based classification, minimum distance-based classification, and statistical model-based classification methods such as threshold methods, k-nearest neighbor (KNN), support vector machine (SVM), Gaussian mixed models (GMM), hidden Markov models (HMM), and artificial neural networks (ANNs), have been adopted for audio classification problem [6][7][8]. For hearing auxiliary equipment, such as hearing aids and cochlear applications classification algorithms have been improved and optimized according to their excellent accuracy.…”

Section: Introductionmentioning

confidence: 99%

Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information

Jin

Fan

2022

Information

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Audio classification algorithms for hearing aids require excellent classification accuracy. To achieve effective performance, we first present a novel supervised method, involving a spectral entropy-based magnitude feature with a random forest classifier (SEM-RF). A novel-feature SEM based on the similarity and stability of band signals is introduced to improve the classification accuracy of each audio environment. The random forest (RF) model is applied to perform the classification process. Subsequently, to resolve the problem of decreasing classification accuracy of the SEM-RF algorithm in mixed speech environments, an improved algorithm, ImSEM-RF, is proposed. The SEM features and corresponding phase features are fused on multiple time resolutions to form a robust multi-time resolution magnitude and phase (multi-MP) feature, which improves the stability of the feature with which the speech signal interferes. The RF model is improved using the linear discriminant analysis (LDA) method to form a linear discriminant analysis-random forest (LDA-RF) joint classification model, which performs model acceleration. Through experiments on hearing aid research data sets for acoustic environment recognition, the effectiveness of the SEM-RF algorithm was confirmed on a background audio signal dataset. The classification accuracy increased by approximately 7% compared with the background noise classification algorithm using an RF tree classifier. The validity of the ImSEM-RF algorithm in speech-interference environments was confirmed using the speech in the background audio signal dataset. Compared with the SEM-RF algorithm, the classification accuracy was improved by approximately 2%. The LDA-RF reduced the program’s running time by >80% with multi-MP features compared with RF.

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“…To make sense out of this data, medical practitioners and service providers can apply various data mining algorithm, to discover various patterns and useful insights. Such insights can be very useful on understanding various trends during epidemics, such as Malaria, Dengue, Chikungunya and other such outbreaks [4][5][6][7][8][9][10] .…”

Section: Introductionmentioning

confidence: 99%

Novel Algorithm of CPU-GPU hybrid system for health care data classification

Tanweer¹,

Rao²

2019

J. Drug Delivery Ther.

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Due to advancements in portable health monitoring technology, such systems have become more and more economical & efficient. This in turn has resulted in a huge amount of data being generated every moment by millions of users of such portable devices. Such voluminous data may include audio, video, and image, and text representing blood pressure, temperature, vocal activity, ECG, sugar level etc. In the Proposed algorithm, first step is assignment, where clusters are assigned to a patient data and the second step is update, which takes the mean of the coordinates of all the data in its cluster. Medical practitioners and service providers can use such data to discover various patterns and useful insights. Such insights can be very useful on understanding various trends during epidemics, such as Malaria, Dengue, Chikungunya and other such outbreaks. A faster and economical way to get such insights is of paramount importance. Keywords: health monitoring; GPU; ECG; epidemics; data mining

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Environmental Noise Classification using LDA, QDA and ANN Methods

Cited by 10 publications

References 0 publications

Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network

Urban Noise Analysis and Emergency Detection System using Lightweight End-to-End Convolutional Neural Network

Audio Classification Algorithm for Hearing Aids Based on Robust Band Entropy Information

Novel Algorithm of CPU-GPU hybrid system for health care data classification

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