Sunday, 9 July 2017

hi !! everyone

Its second week after the First evaluation and yes !! :) I have passed the First evaluation and can official continue my GSoC journey. A lot thanks to my mentors without whom it won't have been possible.

In my last blog I told you about a plug-in that extracts low-level features from an audio file using Essentia binaries. This week I have added high-level features extractor to the plug-in. For high-level extractor we need to build essentia with gaia to be able to run high-level models. Each model (a *.history file) is basically a transformation history that maps a pool (a feature vector) of such lower-level descriptors produced by low-level extractor into probability values of classes on which the model was trained.

"essentia_streaming_extractor_music_svm" extractor was used to extract high -level feature and "streaming_extractor_music" extractor binary was used to extract low-level features.

The following are the high-level features that can be extracted now:
  1. Mood
  2. Genre
  3. Danceability
  4. Voice or Instrumental
  5. Gender

In addition to this, I together with my mentor Botanic worked to improve travis-ci that now uses docker container to run tests.


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