for Music Similarity, Music Classification and Music Recommendation
created by:
TU Wien
Music Information Retrieval Group
Institute of Software Technology and Interactive Systems
Vienna University of Technology, Austria
Main Authors: Thomas Lidy (audiofeature), Alexander Schindler (slychief)
Analyze all audio files in a folder and store the extracted features:
python rp_extract_batch.py <input_path> <feature_file_name>
This will
- search for MP3 or WAV files in input_path
- extract a standard set of audio features (RP, SSD, RH - see http://ifs.tuwien.ac.at/mir/audiofeatureextraction.html )
- write them in a CSV like manner to feature_file_name (one for each feature type: .rp, .ssd, .rh)
Optionally specify the type of features you want:
python rp_extract_batch.py -rp -mvd -tssd <input_path> <feature_file_name>
Use the following to get a list of possible feature options:
python rp_extract_batch.py -h
rp_extract.py is the main feature extractor. It can be imported and used in your code to do segment-level audio feature analysis.
For a step-by-step tutorial open „RP_extract Tutorial.ipynb“ in iPython Notebook or view the tutorial here:
http://nbviewer.ipython.org/github/tuwien-musicir/rp_extract/blob/master/RP_extract_Tutorial.ipynb
It also includes examples on how to compute music similarity, e.g. for music recommendation or creating playlists of coherent music.
rp_classify.py will analyze audio files and categorize them into a high-level concept (such as genre, style or mood)
given a pre-trained classifier model that was created based on training data.
It can be used on a single audio file (wav or mp3) to recognize its genre like this:
python rp_classify.py music/BoxCat_Games_-_10_-_Epic_Song.mp3
will output:
music/BoxCat_Games_-_10_-_Epic_Song.mp3: pop
You can also use a folder as an input, to analyze and predict all wav and mp3 files contained. The full syntax is:
python rp_classify.py input_path model_file output_filename
input_path can be: a folder, wav or mp3 file, or txt file containing a line-wise list of wav or mp3 files
The pre-trained model included in this code repository (models/GTZAN) can predict these 10 genres:
blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, rock
output_filename is an optional file in which predictions will be written in a tab-separated way
(if omitted, result will be printed to stdout).
You can train your own model like this:
python rp_classify.py -t input_path model_file
In this case files from input_path will be read and analyzed and a model will be trained and stored in model_file.
Note: For training a model, files in input_path must be organized in sub-folders which are named after the categories to be trained.
(this limitation/requirement might be relieved in future through the provision of groundtruth class files)
Once you trained a model, you can do predictions given the syntax above (without the -t parameter).