Installed and configured tensorflow on personal desktop computer. Ran into a few issues with enabling tensorflow to use GPU, but managed to get it up and running with some debugging.
Analyzed and dissected the repository for tensorflow implementation of wavenet. Researched about the concepts and features of tensorflow in order to understand the basic implementation. Looked into the functionality of the audio library librosa that was used in the project.
Attempted to download the MAPS piano dataset, but failed to do so since the FTP authorization details were incomplete. Contacted the sysadmins and authors, but they have responded that the dataset is unavailable and it hasn’t been updated in a while.
Gave up on downloading the MAPS dataset since the process was complicated and they didn’t respond back to me. Researched about downloading piano MIDI files and writing a script to convert MIDI note files to WAV music files, but I would need to download individual files since there was no download option for downloading all of them.
Thought of making a scraping scripts to download the files, but opted for a different site to collect aggregate music. Managed to download a few gigs of piano music collection. Started looking into how to train a model on the custom dataset.
Trained and generated sample music with the model. Researched about exporting the trained model graph so that it can be used on the mobile.
Designed and broke down the architecture for the project and the parts needed for the Android application, which will generate music based on the model. Found an audio library that could be used for processing the audio files on Android. Generated illustrations for the presentation on project implementation.