Difference between revisions of "MIR workshop 2011 day5 lab"

Overview

This lab covers the construction of parts of a music recommender. Focus is placed on building a similarity matrix from data and querying that matrix based on cosine distance. Fast programmers should be able to accomplish considerably more.

• The basics (some Python code available to help).
  • Calculate acoustic features on CAL500 dataset (students should have already done this.)
  • Read in user tag annotations from same dataset provided by UCSD.
  • Build similarity matrix based on word vectors derived from these annotations.
  • Query similarity matrix with a track to get top hits based on cosine distance.
  • Build second similarity matrix using acoustic features.
  • Query this similarity matrix with track to get top hits based on cosine distance.

• Extra (I didn't write code for this, but can help students find examples).
  • Query the EchoNest for additional acoustic features and compare to yours.
  • Use the CAL500 user annotations as ground truth and evaluate your audio features (ROC curve or some precision measure).
  • Compare a 2D visualization of acoustic features versus UCSD user annotations.