An automated environmental sound classification methods based on statistical and textural feature

Akbal, Erhan

doi:10.1016/j.apacoust.2020.107413

Cited by 31 publications

(7 citation statements)

References 26 publications

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“…Akbal [25] proposed a method for classifying environmental noises consisting of three basic stages and selects the feature generation, selection and classification. One dimensional native binary models, one dimensional quarterly model and statistical characterization production approaches are used for feature extraction.…”

Section: Related Workmentioning

confidence: 99%

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RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

Singh

Jaiswal

2020

SN Appl. Sci.

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Audio classification is the reason for the multimedia gratified examination which is the utmost significant and generally utilized application these days. For huge information bases, programmed classification procedure utilizing Artificial Intelligence (AI) is more viable than the manual classification. Various sorts of AI calculations have proposed in writing like K-Nearest Neighbors Principal Component Analysis, Gaussian Mixture Model, and Hidden Markov Model, etc. By utilizing the above methods, the audio classification can be done with no class related pre-information. However, they require huge training data with no real segregation results. To beat these insufficiencies, this paper proposed a general structure for audio classification. In this paper, another audio classification algorithm utilizing Support Vector Machine (SVM) in view of Whale Optimization Algorithm (WOA) is given where WOA-SVM utilizes the class mark of the info test as the real yield. WOA is utilized for conquering the inconvenience of SVM, for example, high computational multifaceted nature as a result of the explaining of enormous scale quadratic programming in parameter iterative learning methodology. The audio sign has shown up in huge volumes on account of its tendency. With the goal that we have utilized the MapReduce approach which is one of the sorts of big data investigation to play out the classification on the unstructured information. The proposed audio classification algorithm has contrasted with a few existing classification algorithms with demonstrating its productivity and the exactness.

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Section: Related Workmentioning

confidence: 99%

“…The presentation measures have been clarified as pursues. This setup is connected for RBFNN [22], PLMC [23], RARE [25], and the proposed MR-WOA-SVM calculations.…”

Section: Performance and Comparative Analysismentioning

confidence: 99%

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

Singh

Jaiswal

2020

SN Appl. Sci.

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“…Erhan Akbal [27] achieved a 90.25% prediction accuracy rate with his proposed system. In his study, he suggests a cognitive, lightweight, highly reliable, and low bandwidth form of online expansion.…”

Section: Related Workmentioning

confidence: 86%

Birds Sound Classification Based on Machine Learning Algorithms

Mehyadin

Abdulazeez

Hasan

et al. 2021

AJRCoS

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The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

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“…Texture features often refer to visual characteristics that do not depend on the color or brightness of the image and can reflect the homogeneous phenomenon of the image and describe the pixel distribution in the neighborhood space [ 19 , 20 , 21 ]. For a special object within an image, texture features often contain important information about the surface structure arrangement and thus can reflect its connection with the surrounding environment [ 22 , 23 ]. Texture analysis aims to select a unique method to describe the underlying characteristics, which generally consist of four types: statistical, modeling, signal processing, and structural methods [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline–Alkali Soil

Zhao

Zhang

Zhu

et al. 2022

IJERPH

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Desiccation cracking during water evaporation is a common phenomenon in soda saline–alkali soils and is mainly determined by soil salinity. Therefore, quantitative measurement of the surface cracking status of soda saline–alkali soils is highly significant in different applications. Texture features can help to determine the mechanical properties of soda saline–alkali soils, thus improving the understanding of the mechanism of desiccation cracking in saline–alkali soils. This study aims to provide a new standard describing the surface cracking conditions of soda saline–alkali soil on the basis of gray-level co-occurrence matrix (GLCM) texture analysis and to quantitatively study the responses of GLCM texture features to soil salinity. To achieve this, images of 200 field soil samples with different surface cracks were processed and calculated for GLCMs under different parameters, including directions, gray levels, and step sizes. Subsequently, correlation analysis was then conducted between texture features and electrical conductivity (EC) values. The results indicated that direction had little effect on the GLCM texture features, and that four selected texture features, contrast (CON), angular second moment (ASM), entropy (ENT), and homogeneity (HOM), were the most correlated with EC under a gray level of 2 and step size of 1 pixel. The results also showed that logarithmic models can be used to accurately describe the relationships between EC values and GLCM texture features of soda saline–alkali soils in the Songnen Plain of China, with calibration R2 ranging from 0.88 to 0.92, and RMSE from 2.12 × 10−4 to 9.68 × 10−3, respectively. This study can therefore enhance the understanding of desiccation cracking of salt-affected soil to a certain extent and can also help to improve the detection accuracy of soil salinity.

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An automated environmental sound classification methods based on statistical and textural feature

Cited by 31 publications

References 26 publications

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

Birds Sound Classification Based on Machine Learning Algorithms

Quantitative Response of Gray-Level Co-Occurrence Matrix Texture Features to the Salinity of Cracked Soda Saline–Alkali Soil

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