Impact of Automatic Feature Extraction in Deep Learning Architecture

Shaheen, Fatma; Verma, Brijesh; Asafuddoula, Md.

doi:10.1109/dicta.2016.7797053

Cited by 93 publications

(49 citation statements)

References 7 publications

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“…Lately, thanks to the advent of deep learning, feature extraction is done automatically by the chosen architecture. For example, CNN takes a 2-D matrix as input and automatically extracts hidden features using spatial filters [ 39 ].…”

Section: Resultsmentioning

confidence: 99%

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

et al. 2021

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Background: Brain-Computer Interface (BCI) is becoming more reliable, thanks to the advantages of Artificial Intelligence (AI). Recently, hybrid Deep Learning (hDL), which combines different DL algorithms, has gained momentum over the past five years. In this work, we proposed a review on hDL-based BCI starting from the seminal studies in 2015. Objectives: We have reviewed 47 papers that apply hDL to the BCI system published between 2015 and 2020 extracting trends and highlighting relevant aspects to the topic. Methods: We have queried four scientific search engines (Google Scholar, PubMed, IEEE Xplore and Elsevier Science Direct) and different data items were extracted from each paper such as the database used, kind of application, online/offline training, tasks used for the BCI, pre-processing methodology adopted, type of normalization used, which kind of features were extracted, type of DL architecture used, number of layers implemented and which optimization approach were used as well. All these items were then investigated one by one to uncover trends. Results: Our investigation reveals that Electroencephalography (EEG) has been the most used technique. Interestingly, despite the lower Signal-to-Noise Ratio (SNR) of the EEG data that makes pre-processing of that data mandatory, we have found that the pre-processing has only been used in 21.28% of the cases by showing that hDL seems to be able to overcome this intrinsic drawback of the EEG data. Temporal-features seem to be the most effective with 93.94% accuracy, while spatial-temporal features are the most used with 33.33% of the cases investigated. The most used architecture has been Convolutional Neural Network-Recurrent Neural Network CNN-RNN with 47% of the cases. Moreover, half of the studies have used a low number of layers to achieve a good compromise between the complexity of the network and computational efficiency. Significance: To give useful information to the scientific community, we make our summary table of hDL-based BCI papers available and invite the community to published work to contribute to it directly. We have indicated a list of open challenges, emphasizing the need to use neuroimaging techniques other than EEG, such as functional Near-Infrared Spectroscopy (fNIRS), deeper investigate the advantages and disadvantages of using pre-processing and the relationship with the accuracy obtained. To implement new combinations of architectures, such as RNN-based and Deep Belief Network DBN-based, it is necessary to better explore the frequency and temporal-frequency features of the data at hand.

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Section: Resultsmentioning

confidence: 99%

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

et al. 2021

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“…The randomness of loadings hinders the use of statistical time domain and frequency domain features for distinguishing the healthy case from the delaminated cases as well as a distinction between different cases of delaminations (e.g., AL1, AL2, AL3, AM1, etc.). Deep learning offers a natural solution to the current problem due to its capability to automatically extract discriminative features from the input images [53]. Figure 3 shows a schematic of the deep learning-based methodology for the current problem.…”

Section: Proposed Methodologymentioning

confidence: 99%

A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

Khan

Shin

Lim

et al. 2020

Sensors

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Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

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“…The auxiliary module acts as a feature extractor to extract CWS and POS representations from raw text inputs, which are expected to provide more information for the main module. Inspired by [16,17], we proposed two auxiliary module structures -the dilated-CNN (DCNN) and the transformer encoder (TE). The DCNN model consists of 3 dilated CNN layers, with kernel size=5, filter number=128, and dilation rate=1,2,4 respectively.…”

Section: Auxiliary Modulementioning

confidence: 99%

A Unified Sequence-to-Sequence Front-End Model for Mandarin Text-to-Speech Synthesis

Pan

Yin

Zhang

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In Mandarin text-to-speech (TTS) system, the front-end text processing module significantly influences the intelligibility and naturalness of synthesized speech. Building a typical pipeline-based front-end which consists of multiple individual components requires extensive efforts. In this paper, we proposed a unified sequence-to-sequence front-end model for Mandarin TTS that converts raw texts to linguistic features directly. Compared to the pipeline-based front-end, our unified front-end can achieve comparable performance in polyphone disambiguation and prosody word prediction, and improve intonation phrase prediction by 0.0738 in F1 score. We also implemented the unified front-end with Tacotron and WaveRNN to build a Mandarin TTS system. The synthesized speech by that got a comparable MOS (4.38) with the pipeline-based front-end (4.37) and close to human recordings (4.49).

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Impact of Automatic Feature Extraction in Deep Learning Architecture

Cited by 93 publications

References 7 publications

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

Hybrid Deep Learning (hDL)-Based Brain-Computer Interface (BCI) Systems: A Systematic Review

A Deep Learning Framework for Vibration-Based Assessment of Delamination in Smart Composite Laminates

A Unified Sequence-to-Sequence Front-End Model for Mandarin Text-to-Speech Synthesis

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