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Found 5 papers, 0 papers with code
Fully Dense Neural Network for the Automatic Modulation Recognition
Nowadays, we mainly use various convolution neural network (CNN) structures to extract features from radio data or spectrogram in AMR.
A Radio Signal Modulation Recognition Algorithm Based on Residual Networks and Attention Mechanisms
To solve the problem of inaccurate recognition of types of communication signal modulation, a RNN neural network recognition algorithm combining residual block network with attention mechanism is proposed.
Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification
In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the research on the regularization for dynamic ensemble methods.
Multi-layer Attention Mechanism for Speech Keyword Recognition
The key idea is that, in addition to the conventional attention mechanism, information of layers prior to feature extraction and LSTM are introduced into attention weights calculations.
Ranked #4 on
Keyword Spotting
on Google Speech Commands
(Google Speech Commands V2 20 metric)
Scalar Quantization as Sparse Least Square Optimization
Furthermore, to compute quantization results with a given amount of values/clusters, this paper designed an iterative method and a clustering-based method, and both of them are built on sparse least square.
