Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features

Gong, Cihun-Siyong Alex; Su, Chih-Hui Simon; Chao, Kuo-Wei; Chao, Yi-Chu; Su, Chin-Kai; Chiu, Wei-Hang

doi:10.1371/journal.pone.0259140

Cited by 8 publications

(5 citation statements)

References 52 publications

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“…𝑥 (𝑘) is the prediction sample, and 𝛼 is determined by 𝑒(𝑘) to minimize the mean square error (MSE). The equation is: Instead of using the MEMS microphone array, addressing these problems requires the use of appropriate filtering methods, and methods such as the Mel-scale frequency cepstral coefficient (MFCC) [17,18], Fast Fourier transform (FFT) [19,20], order-tracking technology [16] and wavelet transform [21] have been applied to machinery fault diagnosis. Linear prediction coefficients (LPC) have been applied to many modern speech processing systems for applications including coding, synthesis, analysis and recognition [22]; the initial model is constructed using historical data, and new data testing and verification can be used to predict the associated outcomes of audio signal data.…”

Section: Linear Predictive Coding Methodsmentioning

confidence: 99%

Deep Learning with LPC and Wavelet Algorithms for Driving Fault Diagnosis

Gong

Liu

et al. 2022

Sensors

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Vehicle fault detection and diagnosis (VFDD) along with predictive maintenance (PdM) are indispensable for early diagnosis in order to prevent severe accidents due to mechanical malfunction in urban environments. This paper proposes an early voiceprint driving fault identification system using machine learning algorithms for classification. Previous studies have examined driving fault identification, but less attention has focused on using voiceprint features to locate corresponding faults. This research uses 43 different common vehicle mechanical malfunction condition voiceprint signals to construct the dataset. These datasets were filtered by linear predictive coefficient (LPC) and wavelet transform(WT). After the original voiceprint fault sounds were filtered and obtained the main fault characteristics, the deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) architectures are used for identification. The experimental results show that the accuracy of the CNN algorithm is the best for the LPC dataset. In addition, for the wavelet dataset, DNN has the best performance in terms of identification performance and training time. After cross-comparison of experimental results, the wavelet algorithm combined with DNN can improve the identification accuracy by up to 16.57% compared with other deep learning algorithms and reduce the model training time by up to 21.5% compared with other algorithms. Realizing the cross-comparison of recognition results through various machine learning methods, it is possible for the vehicle to proactively remind the driver of the real-time potential hazard of vehicle machinery failure.

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Section: Linear Predictive Coding Methodsmentioning

confidence: 99%

Deep Learning with LPC and Wavelet Algorithms for Driving Fault Diagnosis

Gong

Liu

et al. 2022

Sensors

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“…The results showed that the method in [59] could accurately detect the lane-changing intention of a side car, so as to realize the advanced braking of the target vehicle when the side car changed lanes. The authors of [60] collected sound signals of vehicle tires in 12 different situations, and used SVM and MLP (multilayer perceptron) to classify and identify fault conditions. The main experimental procedure of [61] was divided into two parts.…”

Section: Svm (Support Vector Machine)mentioning

confidence: 99%

“…If the failure factor of the rotating element is not detected early, it can be a great threat to driving safety. As far as the field of acoustics research is concerned, the application of voiceprint recognition in automobile fault prediction is a very novel technology, but there are still many technical obstacles to overcome [60], such as complex sound field feature filtering, voiceprint data collection, digital filtering algorithms, and the selection of machine learning training models. Acoustic sensors are used to measure abnormal vehicle signals and thereby determine the abnormal characteristics of vehicle parts.…”

Section: Overview Of Vehicle Transmission Acoustic Signal Processingmentioning

confidence: 99%

“…In the process of acoustic signal filtering, the spectrum analysis is a fault feature diagnosis method commonly used for acoustic feature identification. After collecting the acoustic signals of various vehicle fault conditions, the acoustic signals can be imported into various digital filtering algorithms such as LPC [90], MFCC [60,61], and wavelet transform (WT). The time-domain signal is converted into a spectrum and the background noise is removed, where the key features are preserved.…”

Section: Overview Of Vehicle Transmission Acoustic Signal Processingmentioning

confidence: 99%

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How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme

Gong

Chen

et al. 2022

Micromachines

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The necessity of vehicle fault detection and diagnosis (VFDD) is one of the main goals and demands of the Internet of Vehicles (IoV) in autonomous applications. This paper integrates various machine learning algorithms, which are applied to the failure prediction and warning of various types of vehicles, such as the vehicle transmission system, abnormal engine operation, and tire condition prediction. This paper first discusses the three main AI algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, and compares the advantages and disadvantages of each algorithm in the application of system prediction. In the second part, we summarize which artificial intelligence algorithm architectures are suitable for each system failure condition. According to the fault status of different vehicles, it is necessary to carry out the evaluation of the digital filtering process. At the same time, it is necessary to preconstruct its model analysis and adjust the parameter attributes, types, and number of samples of various vehicle prediction models according to the analysis results, followed by optimization to obtain various vehicle models. Finally, through a cross-comparison and sorting, the artificial intelligence failure prediction models can be obtained, which can correspond to the failure status of a certain car model and a certain system, thereby realizing a most appropriate AI model for a specific application.

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“…Feature dimensionality reduction can enhance the performance of radiomics models [155]. Feature selection and dimensionality reduction serve a common purpose: addressing high dimensionality in data processing, where the number of features in a sample tends to increase linearly with the amount of data to be processed.…”

mentioning

confidence: 99%

Radiomics and Its Feature Selection: A Review

Zhang,

Guo,

Jin

2023

Symmetry

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Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes quantitative features extracted from medical images to describe underlying pathologies, genetic information, and prognostic indicators. The integration of radiomics with artificial intelligence presents innovative avenues for cancer diagnosis, prognosis evaluation, and therapeutic choices. In the context of oncology, radiomics offers significant potential. Feature selection emerges as a pivotal step, enhancing the clinical utility and precision of radiomics. It achieves this by purging superfluous and unrelated features, thereby augmenting model performance and generalizability. The goal of this review is to assess the fundamental radiomics process and the progress of feature selection methods, explore their applications and challenges in cancer research, and provide theoretical and methodological support for future investigations. Through an extensive literature survey, articles pertinent to radiomics and feature selection were garnered, synthesized, and appraised. The paper provides detailed descriptions of how radiomics is applied and challenged in different cancer types and their various stages. The review also offers comparative insights into various feature selection strategies, including filtering, packing, and embedding methodologies. Conclusively, the paper broaches the limitations and prospective trajectories of radiomics.

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Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features

Cited by 8 publications

References 52 publications

Deep Learning with LPC and Wavelet Algorithms for Driving Fault Diagnosis

Deep Learning with LPC and Wavelet Algorithms for Driving Fault Diagnosis

How to Implement Automotive Fault Diagnosis Using Artificial Intelligence Scheme

Radiomics and Its Feature Selection: A Review

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