Improving Audio Anomalies Recognition Using Temporal Convolutional Attention Networks

Huang, Qiang; Hain, Thomas

doi:10.1109/icassp39728.2021.9414611

Cited by 8 publications

(3 citation statements)

References 13 publications

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“…We used an attention mechanism for the classification tasks. This attention mechanism has been successfully demonstrated for a wide range of tasks (Huang andHain 2021, Jung et al 2021). By allocating weights, the attention mechanism extracts more significant features and increases their significance in the model class process.…”

Section: Attention Mechanismmentioning

confidence: 94%

Pilot study of contactless sleep apnea detection based on snore signals with hardware implementation

Lin

et al. 2023

Physiol. Meas.

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Objective. Sleep apnea has a high incidence and is a potentially dangerous disease, and its early detection and diagnosis are challenging. Polysomnography (PSG) is considered the best approach for sleep apnea detection, but it requires cumbersome and complicated operations. Thus, it cannot satisfy the family healthcare needs. Approach. To facilitate the initial detection of sleep apnea in the home environment, we developed a sleep apnea classification model based on snoring and hybrid neural network, and implemented the well trained model in an embedded hardware platform. We used snore signals from 32 patients at Shenzhen People’s Hospital. The Mel-Fbank features were extracted from snore signals to build a sleep apnea classification model based on Bi-LSTM with attention mechanism. Main results. The proposed model classified snore signals into four types: hypopnea, normal condition, obstructive sleep apnea, and central sleep apnea, with 83.52% and 62.31% accuracies, corresponding to the subject-dependence and subject-independence validation, respectively. After pruning and model quantization, at the cost of 0.81% and 0.95% accuracy loss of the subject dependence and subject independence classification, respectively, the number of model parameters and model storage space were reduced by 32.12% and 60.37%, respectively. The model exhibited accuracies of 82.71% and 61.36% based on the subject dependence and subject independence validations, respectively. When the well trained model was successfully porting and running on an STM32 ARM-embedded platform, the model accuracy was 58.85% for the four classifications based on leave-one-subject-out validation. Significance. The proposed sleep apnea detection model can be used in home healthcare for the initial detection of sleep apnea.

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Section: Attention Mechanismmentioning

confidence: 94%

Pilot study of contactless sleep apnea detection based on snore signals with hardware implementation

Lin

et al. 2023

Physiol. Meas.

View full text Add to dashboard Cite

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“…This reduces the amount of calculation and simplifies the network structure. In view of the traditional neural networks problem that time-series data modeling can only be extended by linearly stacking multi-layer convolutions, TCN uses dilated convolution to increase the receptive field of a layer to reduce the number of convolutional layers [27]. The network structure for a convolution kernel number of four and an expansion coefficient of one is shown in Figure 2.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

An Efficient Malware Classification Method Based on the AIFS-IDL and Multi-Feature Fusion

Song

2022

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In recent years, the presence of malware has been growing exponentially, resulting in enormous demand for efficient malware classification methods. However, the existing machine learning-based classifiers have high false positive rates and cannot effectively classify malware variants, packers, and obfuscation. To address this shortcoming, this paper proposes an efficient deep learning-based method named AIFS-IDL (Atanassov Intuitionistic Fuzzy Sets-Integrated Deep Learning), which uses static features to classify malware. The proposed method first extracts six types of features from the disassembly and byte files and then fuses them to solve the single-feature problem in traditional classification methods. Next, Atanassov’s intuitionistic fuzzy set-based method is used to integrate the result of the three deep learning models, namely, GRU (Temporal Convolutional Network), TCN (Temporal Convolutional Network), and CNN (Convolutional Neural Networks), which improves the classification accuracy and generalizability of the classification model. The proposed method is verified by experiments and the results show that the proposed method can effectively improve the accuracy of malware classification compared to the existing methods. Experiments were carried out on the six types of features of malicious code and compared with traditional classification algorithms and ensemble learning algorithms. A variety of comparative experiments show that the classification accuracy rate of integrating multi-feature, multi-model aspects can reach 99.92%. The results show that, compared with other static classification methods, this method has better malware identification and classification ability.

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“…To address the problem of linear stacking of multiple convolutions in traditional neural networks to dilate the modeling of time series, TCN reduces the number of convolutional layers by using dilated convolutions to increase the range of the receptive field of each layer [37]. The difference between dilated convolutions and ordinary convolutions is that dilated convolution allows interval sampling of input during convolution, and the sampling rate depends on dilation factors.…”

Section: Dilated Convolutionsmentioning

confidence: 99%

Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

2021

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The prerequisite for victory in war is the rapid and accurate identification of the tactical intention of the target on the battlefield. The efficiency of manual recognition of the combat intention of air targets is becoming less and less effective with the advent of information warfare. Moreover, if the traditional method of combat intention of air targets is based only on data from a single moment in time, the characteristic information on the time-series data is difficult to capture effectively. In this context, we design a new deep learning method attention mechanism with temporal convolutional network and bidirectional gated recurrent unit (Attention-TCN-BiGRU) to improve the recognition of the combat intent of air targets. Specifically, suitable characteristics are selected based on the combat mission and air posture to construct a characteristic set of air target intentions and encode them into temporal characteristics. Each characteristic in the characteristic set is given an appropriate weight through the attention mechanism. In addition, temporal convolutional network (TCN) is used to mine the data for latent characteristics and bidirectional gated recurrent unit (BiGRU) is used to capture long-term dependencies in the data. Experiments comparing with other methods and ablation demonstrate that Attention-TCN-BiGRU outperforms state-of-the-art methods in terms of accuracy in recognizing target intent in the air.

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Improving Audio Anomalies Recognition Using Temporal Convolutional Attention Networks

Cited by 8 publications

References 13 publications

Pilot study of contactless sleep apnea detection based on snore signals with hardware implementation

Pilot study of contactless sleep apnea detection based on snore signals with hardware implementation

An Efficient Malware Classification Method Based on the AIFS-IDL and Multi-Feature Fusion

Attention-TCN-BiGRU: An Air Target Combat Intention Recognition Model

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