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Majulah
Majulah
 
 
Embed_audio_data_callable.py
Embed_audio_data_callable.py
 
 
Evaluate-LSTM-on-test.py
Evaluate-LSTM-on-test.py
 
 
Forecast-in-latent-space-KNN.py
Forecast-in-latent-space-KNN.py
 
 
Forecast-in-latent-space-LSTM.py
Forecast-in-latent-space-LSTM.py
 
 
Forecast-in-latent-space-MLP.py
Forecast-in-latent-space-MLP.py
 
 
Majulah.wav
Majulah.wav
 
 
Numerical-solution-divergence-Plots.ipynb
Numerical-solution-divergence-Plots.ipynb
 
 
Numerical-solution-divergence.py
Numerical-solution-divergence.py
 
 
Play_embedding.py
Play_embedding.py
 
 
Plot-with-ZForecast.py
Plot-with-ZForecast.py
 
 
Plot_compare_embeddings.py
Plot_compare_embeddings.py
 
 
README.md
README.md
 
 
Save-dataset_2sec-window.py
Save-dataset_2sec-window.py
 
 
Save-dataset_half-sec-window.py
Save-dataset_half-sec-window.py
 
 
Train_model_2sec-window.py
Train_model_2sec-window.py
 
 
Train_model_half-sec-window.py
Train_model_half-sec-window.py
 
 
train-latent-space-LSTM.py
train-latent-space-LSTM.py
 
 
train-latent-space-MLP.py
train-latent-space-MLP.py
 
 

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Deep-Audio-Embedding

Visualizing the structure of audio in 3D using deep convolutional autoencoders. The code ultimately transforms .wav audio files into a timeseries in 3 dimensions.

See my blog post for a detailed description of this project.

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Visualizing the structure of audio in 3D using deep convolutional autoencoders

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  • Python 58.1%
  • Jupyter Notebook 41.9%