Msaf Versions Save

• Better README documentation in terms of installation
• Updated badges in README
• Compatible with more modern scipy and numpy versions
• Using GitActions instead of Travis

• Compatibility with Librosa 0.6.0 when computing CQTs.
• Added VMO algorithm.
• Modified quick-guide with clearer example for evaluation.

v0.1.61

• Fixed hierarchical boundaries, which didn't work on JAMS 0.3.0.

v0.1.6

• Added framesync parameter to compute_features.py script example
• joblib was repeated in the requirements. Fixed
• Updated PyPi docs strings such that it shows that MSAF if Python 3.56 compatible
• Making configparser compatible with Python 2.7
• Using latest scluster method, from McFee's code here
• Adopted code to JAMS 0.3.0.

• JAMS bug supposedly fixed in v0.1.5 was not really fixed. Fixed now. Alrighten.

• Fixed bug tha threw a TypeError if multiple algorithms were run in a single JAMS file with None and other label_ids in it
• Added new vmo oracle segmentation method (by Cheng-i Wang, thanks!)
• Adapting sonify function to latest numpy
• Using KMeans from sklearn instead of scipy for 2D-FMC. Results are better
• Making sure we are never using more number of clusters than number of segments for 2D-FMC
• Added new parameter 2dfmc_offset in the 2D-FMC method
• Using np.inf normalization for 2D-FMC now, since it seems to yield better results (at least for Beatles TUT)
• Padding beat-sync features now, seems to fix potential misalignment of boundaries. Some algorithms (2D-FMC, CNMF) seem to yield better results now
• Modified features file: two new fields may be addeed: est_beatsync_times and ann_beatsync_times.
• The member variable _framesync_times in the Features was never updated. Fixed it

• Included Python 3.5 in the metadata
• Removed old functions from i/o module that nobody should be using
• cleanued up code for reading/writing estimations (just a tiny bit)
• Unit tested i/o module

• Fixed bug of selecting framesync features
• OLDA and Scluster hierarchies are consistent now (first element in hierarchy in the highest in both algorithms; this was also true for Scluster before this fix)
• Warning message is displayed if jams file exists but can't be read during features computation
• If two algorithms used at the same time have the same name, and AssertionError is raised
• Fixed normalization problem: now algorithms have independent normalization parameters
• Adding out_file variable in main process function of the eval module
• Reporting proper weighted F-measure for the perceptual Hit Rate
• Fixed bug of annotator id not correctly passed to hierarchical evaluation function
• More unit tests
• Added script to upload to Pypi

• Adapting code to librosa 0.5
• Improved coveralls (starting testing plots)
• Allowing computation of label algorithms for each layer of hierarchical boundaries
• Removed old notebooks that were either useless or belonged to msaf-data
• Removed old ds-name references, improving code health

Fixed some plotting related bugs.