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Identifying Markers for Human Emotion in Breath Using Convolutional Autoencoders on Movie Data

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  • data

    • audio: contains all the audio data for the 6 movies, as numpy matrices
    • labels: contains all the labels for the 6 movies, in the original CSV format and transposed
    • pixels: contains all the video data for the 12 movies the autoencoder is trained over, as numpy matrices
    • vocs: contains all the vocs for all 46 screenings of the 6 movies, in ARFF format

  • models

    • labels_audio: classification network on audio data, trained over 1000 epochs
    • video_autoencoder: trained over 50 epochs
    • video_autoencoder_with_bn: trained over 50 epochs with batch normalization
    • vocs_audio_oso: prediction network on audio data, leaving one screening out for validation, trained over 1000 epochs
    • vocs_audio_l20: prediction network on audio data, using the last 20% of each movie for validation, trained over 1000 epochs

  • convert_vae_to_nn.py

    Used to create either a classification network or a prediction network from an autoencoder.

    Usage: python(3) convert_vae_to_nn.py autoencoder_version model_version output [-y y_test_structure] [-bn]

    • output: should be either labels or vocs
    • -y y_test_structure: oso: One Screening Out or omo: One Movie Out or l20: Last 20% (if vocs)
    • -bn: if batch normalization was enabled

  • extract_audio.py

    Used to create a numpy matrix from the audio data of a movie. The sub_path containing the files needs to be set first.

    Usage: python(3) extract_audio.py film

  • extract_pixels.py

    Used to create a numpy matrix from the video data of a movie. The sub_path containing the files needs to be set first.

    Usage: python(3) extract_pixels.py film nb_frames offset

    • offset: frame number to start with (in case the first few frames are black)

  • labels_audio.py

    Used to train a classification network on audio data. If the model does not exist, it is created.

    Usage: python(3) labels_audio.py model_version [-b batch_size] [-e epochs]

    • -b batch_size: default is 64
    • -e epochs: default is 100

  • labels_video.py

    Used to train a classification network on video data. The model needs to be created from an autoencoder.

    Usage: python(3) labels_video.py model_version [-b batch_size] [-e epochs]

    • -b batch_size: default is 16
    • -e epochs: default is 10

  • video_autoencoder.py

    Used to train a video autoencoder. If the model does not exist, it is created.

    Usage: python(3) video_autoencoder.py model_version [-b batch_size] [-e epochs] [-bn]

    • -b batch_size: default is 16
    • -e epochs: default is 10
    • -bn: enables batch normalization

  • vocs_audio.py

    Used to train a prediction network on audio data. If the model does not exist, it is created.

    Usage: python(3) vocs_audio.py model_version y_test_structure [-b batch_size] [-e epochs] [-f film_tested]

    • y_test_structure: oso: One Screening Out or omo: One Movie Out or l20: Last 20%
    • -b batch_size: default is 64
    • -e epochs: default is 100
    • -f film_tested: film left out (if omo)

  • vocs_video.py

    Used to train a prediction network on video data. The model needs to be created from an autoencoder.

    Usage: python(3) vocs_video.py model_version y_test_structure [-b batch_size] [-e epochs] [-f film_tested]

    • y_test_structure: oso: One Screening Out or omo: One Movie Out or l20: Last 20%
    • -b batch_size: default is 16
    • -e epochs: default is 10
    • -f film_tested: film left out (if omo)