Data Augmentation Using Empirical Mode Decomposition on Neural Networks to Classify Impact Noise in Vehicle

Nam, Gue-Hwan; Bu, Seok-Jun; Park, Namu; Seo, Jae-Yong; Jo, Hyeon-Cheol; Jeong, Wontae

doi:10.1109/icassp40776.2020.9053671

Cited by 9 publications

(3 citation statements)

References 12 publications

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“…Guided Warping [48] SFM [56] Hybrid GAN [97] EMD [103] Flipping [41] Random Warping [21] Time Stretching [54] STFT [52] FT Perturbation [61] Linear Model [82] Markov Chain [83] Autoencoder [88] Time Aligned Averaging Fig. 2.…”

Section: Interpolationmentioning

confidence: 99%

“…These features can either be used independently, recombined, or perturbed for generating new data for augmentation. Empirical Mode Decomposition (EMD) [102] is a method of decomposing nonlinear and non-stationary signals and it has shown to improve classification by using it as a decomposition method for data augmentation of noisy automobile sensor data in a CNN-LSTM [103]. Another example of a decomposition method used for data augmentation was proposed in [27], where the use of Independent Component Analysis (ICA) [104] was combined with a dynamical-functional artificial neural network (D-FANN) for filling gaps in time series.…”

Section: Time Series Decompositionmentioning

confidence: 99%

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An empirical survey of data augmentation for time series classification with neural networks

2021

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In recent times, deep artificial neural networks have achieved many successes in pattern recognition. Part of this success can be attributed to the reliance on big data to increase generalization. However, in the field of time series recognition, many datasets are often very small. One method of addressing this problem is through the use of data augmentation. In this paper, we survey data augmentation techniques for time series and their application to time series classification with neural networks. We propose a taxonomy and outline the four families in time series data augmentation, including transformation-based methods, pattern mixing, generative models, and decomposition methods. Furthermore, we empirically evaluate 12 time series data augmentation methods on 128 time series classification datasets with six different types of neural networks. Through the results, we are able to analyze the characteristics, advantages and disadvantages, and recommendations of each data augmentation method. This survey aims to help in the selection of time series data augmentation for neural network applications.

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Section: Interpolationmentioning

confidence: 99%

Section: Time Series Decompositionmentioning

confidence: 99%

An empirical survey of data augmentation for time series classification with neural networks

2021

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“…In summary, obtaining high-quality audio data to satisfy data-driven supervised or semi-supervised learning is particularly challenging, as it requires careful control of multiple factors to ensure data quality and prevent model overfitting. Therefore, data augmentation plays a crucial role in optimizing the dataset and reducing model overfitting [20].…”

Section: Introductionmentioning

confidence: 99%

Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event

Song,

Xiong,

Wang

et al. 2024

Applied Sciences

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Indoor abnormal sound event identification refers to the automatic detection and recognition of abnormal sounds in an indoor environment using computer auditory technology. However, the process of model training usually requires a large amount of high-quality data, which can be time-consuming and costly to collect. Utilizing limited data has become another preferred approach for such research, but it introduces overfitting issues for machine learning models on small datasets. To overcome this issue, we proposed and validated the framework of combining the offline augmentation of raw audio and online augmentation of spectral features, making the application of small datasets in indoor anomalous sound event identification more feasible. Along with this, an improved two-dimensional audio convolutional neural network (EANN) was also proposed to evaluate and compare the impacts of different data augmentation methods under the framework on the sensitivity of sound event identification. Moreover, we further investigated the performance of four combinations of data augmentation techniques. Our research shows that the proposed combined data augmentation method has an accuracy of 97.4% on the test dataset, which is 10.6% higher than the baseline method. This demonstrates the method’s potential in the identification of indoor abnormal sound events.

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