Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

Parathai, Phetcharat; Tengtrairat, Naruephorn; Woo, Wai Lok; Abdullah, Mohammed A. M.; Rafiee, Gholamreza; Alshabrawy, Ossama S.

doi:10.3390/s20164368

Cited by 12 publications

(5 citation statements)

References 40 publications

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“…Finally, the support vector regression is an extension of the support vector machine for solving regression problems. The objective function of SVR is to minimize the coefficients by using the l 2 -norm of the coefficient vector [ 29 , 30 ] instead of the squared error, as expressed in Equation (12). The constraint called the maximum error (

) is represented by the absolute error in Equation (13).…”

Section: Methodsmentioning

confidence: 99%

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models

Tengtrairat

Woo

Parathai

et al. 2022

Sensors

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Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight of Tilapia fish in turbid water environments. Additionally, the proposed method avoids the issue of using high-cost stereo cameras and instead uses only a low-cost video camera to observe the underwater life through a single channel recording. An in-house curated Tilapia-image dataset and Tilapia-file dataset with various ages of Tilapia are used. The proposed method consists of a Tilapia detection step and Tilapia weight-estimation step. A Mask Recurrent-Convolutional Neural Network model is first trained for detecting and extracting the image dimensions (i.e., in terms of image pixels) of the fish. Secondly, is the Tilapia weight-estimation step, wherein the proposed method estimates the depth of the fish in the tanks and then converts the Tilapia’s extracted image dimensions from pixels to centimeters. Subsequently, the Tilapia’s weight is estimated by a trained model based on regression learning. Linear regression, random forest regression, and support vector regression have been developed to determine the best models for weight estimation. The achieved experimental results have demonstrated that the proposed method yields a Mean Absolute Error of 42.54 g, R2 of 0.70, and an average weight error of 30.30 (±23.09) grams in a turbid water environment, respectively, which show the practicality of the proposed framework.

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) is represented by the absolute error in Equation (13).…”

Section: Methodsmentioning

confidence: 99%

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models

Tengtrairat

Woo

Parathai

et al. 2022

Sensors

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“…Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems inspired by the biological neural networks of the human brain processes information [ 33 , 34 , 35 , 36 , 37 ]. ANN consists of a first input layer, then hidden layers, and lastly an output layer [ 38 ].…”

Section: Methodsmentioning

confidence: 99%

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Tengtrairat

Woo

Parathai

et al. 2021

Sensors

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Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.

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“…This effect can be used efficiently for pattern classification purposes. Examples of sound classification events can be found in [21], where different sources are separated and classified. Considering the present problem of acoustic localization, a supervised approach is considered, where the network will be trained taking into account observations of an acoustic model, and the corresponding coordinates in a predefined search-space.…”

Section: Introductionmentioning

confidence: 99%

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

Correia

Tomic

Beko

2021

JSAN

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The localization of an acoustic source has attracted much attention in the scientific community, having been applied in several different real-life applications. At the same time, the use of neural networks in the acoustic source localization problem is not common; hence, this work aims to show their potential use for this field of application. As such, the present work proposes a deep feed-forward neural network for solving the acoustic source localization problem based on energy measurements. Several network typologies are trained with ideal noise-free conditions, which simplifies the usual heavy training process where a low mean squared error is obtained. The networks are implemented, simulated, and compared with conventional algorithms, namely, deterministic and metaheuristic methods, and our results indicate improved performance when noise is added to the measurements. Therefore, the current developed scheme opens up a new horizon for energy-based acoustic localization, a field where machine learning algorithms have not been applied in the past.

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Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM

Cited by 12 publications

References 40 publications

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models

Non-Intrusive Fish Weight Estimation in Turbid Water Using Deep Learning and Regression Models

Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

A Feed-Forward Neural Network Approach for Energy-Based Acoustic Source Localization

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