A 1-D Deformable Convolutional Neural Network for the Quantitative Analysis of Capnographic Sensor

Bhagya, D; Suchetha, M.

doi:10.1109/jsen.2020.3042989

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…In the authors have developed a 1-D CNN-SVM model to analyze human knee movement mechanomyography signals. Most recently, Bhagya and Suchetha ( 2020 ) introduced a 1-D CNN with a deformable learning mechanism to analyze abnormal capnographic signals and the authors attained an average prediction accuracy of 92.9%. Even though the CNN architecture functions massively well, its operational performance can be additionally improved by making some changes in the original architecture.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism

Chirasani

Manikandan

2022

Soft Comput

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Electroencephalogram (EEG) is a common diagnostic tool for measuring the seizure activity of the brain. There are many deep learning techniques introduced to analyze EEG. These methods show phenomenal results, although they are limited to computational complexity. Our objective was to develop a novel algorithm that gives maximum classification accuracy with a minor computational complexity. In this view, we have introduced a novel convolutional architecture with an integration of a hierarchical attention mechanism. The model comprises three parts: Feature extraction layer, which uses to extract the convoluted feature map; hierarchical attention layer, which is used to obtain weighted hierarchical feature map; classification layer, which uses weighted features for classification of healthy and seizure subjects. The proposed model can extract significant information from the EEG signal to classify seizure subjects, and it is compared with a few existing deep convolutional algorithms through experimentation. The experimental outcomes show that the proposed model has higher accuracy with less computational time.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism

Chirasani

Manikandan

2022

Soft Comput

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“…The formulation of DD-Conv in this section is adapted from [17] and [18]. The D-Conv operation of kernel size P , dilation factor f and convolutional kernel weights for the gth channel of an input with G channels denoted yg ∈ R Lx at the ℓth frame is defined as…”

Section: Deformable Depthwise Convolution (Dd-conv)mentioning

confidence: 99%

“…In this work deformable depthwise convolutional layers [17][18][19] are proposed as a replacement for standard depthwise convolutional layers [5] in TCN based speech separation models for reverberant acoustic conditions. Deformable convolution allows each convolutional layer to have an adaptive RF.…”

Section: Introductionmentioning

confidence: 99%

Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation

Ravenscroft

Goetze

Hain

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Speech separation models are used for isolating individual speakers in many speech processing applications. Deep learning models have been shown to lead to state-of-the-art (SOTA) results on a number of speech separation benchmarks. One such class of models known as temporal convolutional networks (TCNs) has shown promising results for speech separation tasks. A limitation of these models is that they have a fixed receptive field (RF). Recent research in speech dereverberation has shown that the optimal RF of a TCN varies with the reverberation characteristics of the speech signal. In this work deformable convolution is proposed as a solution to allow TCN models to have dynamic RFs that can adapt to various reverberation times for reverberant speech separation. The proposed models are capable of achieving an 11.1 dB average scale-invariant signalto-distortion ratio (SISDR) improvement over the input signal on the WHAMR benchmark. A relatively small deformable TCN model of 1.3M parameters is proposed which gives comparable separation performance to larger and more computationally complex models.

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Sampling Rate Adaptive Speaker Verification from Raw Waveforms

Abrol,

Thakur,

Gupta

et al. 2024

Lecture Notes in Computer Science

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A 1-D Deformable Convolutional Neural Network for the Quantitative Analysis of Capnographic Sensor

Cited by 9 publications

References 17 publications

RETRACTED ARTICLE: A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism

RETRACTED ARTICLE: A deep neural network for the classification of epileptic seizures using hierarchical attention mechanism

Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation

Sampling Rate Adaptive Speaker Verification from Raw Waveforms

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