HTDemucs保持原来模型中encoder前4层和decoder后4层,替换中间的两层为包含局部注意力机制和双向LSTM的跨域Transformer Encoder。它会并行处理来自频域分支的2维特征和来自时域分支的1维特征。原始的Hybrid Demucs需要调整模型参数(STFT帧长和帧移,卷积步长,padding等参数)来对齐时频域的特征维度,但是跨域Transformer Encoder...
Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately disce... F Harby,M Alohali,A Thaljaoui,... - 《Compu...
In this paper, an efficient network based on a lightweight hybrid Vision Transformer (LH-ViT) is proposed to address the HAR accuracy and network lightweight simultaneously. This network combines the efficient convolution operations with the strength of the self-attention mechanism in ViT. Feature ...
Among all datasets, the Inception and LSTM models showed poorer performance. The performance metrics of the proposed monitoring method indicate that it achieved the smallest RMSE, MAE, and MAPE, and the highest R². Specifically, for the ICEEMDAN-Inception-Transformer model: RMSE is 18.46 tCO2,...
Among them, the LSTM approach shows the worst performance compared with other approaches, indicating that the CNN or ViT plays an essential role in cervical lesion classification. The system with ResNet-101 performs better than VGG-16 and AlexNet. It suggests that the complex backbone has a stro...
It suggests utilising deep learning approaches such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) to improve the ability to detect malware in an environment where threats constantly evolve. The malware detection system that has been developed en...
注意力最初是作为序列模型的循环神经网络的附加组件引入的,通过Transformer模型的出现,注意力导致NLP的突破,这种模型仅依赖于一种特殊的注意力,self-attention。这些模型在大型数据集上预训练时的强大性能很快导致基于Transformer的方法成为默认选择,而不是LSTMs等循环模型。
CNN-Bi-LSTM architectural model for epilepsy seizure detection Full size image Table 2 Model structure of CNN- Bi-LSTM Full size table The hyperparameter configuration is shown in Table3. During the model training process, we employed the Adam optimizer, setting the learning rate to 0.001. After...
To tackle this challenge, our proposed framework introduces a novel hybrid model, IChOA-CNN-LSTM, which leverages Convolutional Neural Networks (CNNs) for precise image feature extraction, Long Short-Term Memory (LSTM) networks for sequential data analysis, and an Improved Chimp Optimization ...
The encoder network in a neural transducer uses a long short-term memory (LSTM) (Hochreiter and Schmidhuber, 1997), Transformer, or Conformer (Gulati et al., 2020) network. Show abstract Exploiting beam search confidence for energy-efficient speech recognition 2024, Journal of Supercomputing End-...