urban-sound-classification:Urban sound source tagging from an aggregation of four second noisy audio clips via 1D and 2D CNN (Xception)
Real-time-Network-Traffic-Classifier-IDS:This project develops and deploys a robust, multi-class Network Intrusion Detection System (IDS) capable of identifying various attack types and normal network traffic. Leveraging a 1D Convolutional Neural Network (CNN) architecture, the system is trained on the comprehensive UNSW-NB15 dataset, which features a wide range of modern attacks.
MTC:Implementation of a multi-task model for encrypted network traffic classification based on transformer and 1D-CNN.
timeseries-rnn:Time-series forecasting with 1D Conv model, RNN (LSTM) model and Transformer model. Comparison of long-term and short-term forecasts using synthetic timeseries. Sequence-to-sequence formulation.
1D-Triplet-CNN:PyTorch implementation of the 1D-Triplet-CNN neural network model described in Fusing MFCC and LPC Features using 1D Triplet CNN for Speaker Recognition in Severely Degraded Audio Signals by A. Chowdhury, and A. Ross.
// repository documentation
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