Hi !! everyone

The second evaluation has just started, so let me just summarize, what have I done after the First evaluation.

In my last Blog I told you about the plugin that extracts the low level and highlevel features from the given audio file. Now this plugin together with the other existing ones form a part of the project(peragro whole project) which is called as Analyzers. So what an analyzer module does is, is it finds the right plugin for analyzing a given file of a particular mimetype(basically an audio file in our case). Then that plugin will output the various assests found in the given file and their relavent metadata. Then there is another module named as Transcoder that help you to convert a files assest of a particular mimetype to other relevant mimetypes. for more information on analyzers and transcoders see this (http://peragro.github.io/peragro-at/)

So this week I read the transcoder's code and learned how it works and fixed some of its bugs. In addition to that there were lots of landscape errors in the whole project, I had fixed the majors ones and will try to fix others also.I have also learned about how a cyclic import can cause a big problem to a project.

sagar-kohli

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.

sagar-kohli