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convolutional-lstm

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In this project we have explored the use of imaging time series to enhance forecasting results with Neural Networks. The approach has revealed itself to be extremely promising as, both in combination with an LSTM architecture and without, it has out-performed the pure LSTM architecture by a solid margin within our test datasets.

  • Updated Jul 6, 2023
  • Python

This is a solution to Cinnamon AI Challenge (https://drive.google.com/drive/folders/1Qa2YA6w6V5MaNV-qxqhsHHoYFRK5JB39) using convolutional, attention, bidirectional LSTM, achieving CER 0.081 WER 0.188 and SER 0.89

  • Updated Jan 29, 2020
  • Python

This repository introduces Deep Particulate Matter Network with a Separated Input model based on deep learning by using ConvGRU, which can simultaneously analyze spatiotemporal information to consider the diffusion of particulate matter.

  • Updated Oct 10, 2022
  • Jupyter Notebook

his is a Speech Emotion Recognition system that classifies emotions from speech samples using deep learning models. The project uses four datasets: CREMAD, RAVDESS, SAVEE, and TESS. The model achieves an accuracy of 96% by combining CNN, LSTM, and CLSTM architectures, along with data augmentation techniques and feature extraction methods.

  • Updated Nov 22, 2024
  • Jupyter Notebook

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