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)
Either download as ZIP from https://github.com/tuwien-musicir/rp_extract/archive/master.zip , or:
git clone https://github.com/tuwien-musicir/rp_extract.git
On Linux Ubuntu many dependencies can be installed from the Software Center or repository like this:
sudo apt-get install python-numpy python-scipy python-pandas python-scikits-learn python-matplotlib
Use this to install the remaining dependencies on Ubuntu, respectively all dependencies on Mac and Windows:
sudo pip install -r requirements.txt
Note that some of the requirements are only needed for specific parts of the library. If all you want to use is the
audio analysis part with rp_extract.py, numpy and scipy are the only requirements.
If you want to use MP3, M4A, or AIF(F) files as input, you need to have one of the following decoders installed in your system:\n",
- Linux: install ffmpeg, mpg123, or lame from your Software Install Center or package repository (how to install ffmpeg on Ubuntu 14.04: http://fcorti.com/2014/04/22/ffmpeg-ubuntu-14-04-lts )
- Mac: FFMPeg for Mac: http://ffmpegmac.net or Lame for Mac: http://www.thalictrum.com/en/products/lame.html
- Windows: FFMpeg.exe is already included (nothing to install)
Note: use ffmpeg for mp3, m4a and aiff (lame and mpg123 only support mp3).
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 WAV, MP3, M4A or AIFF 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 CSV format to
feature_file_name(don't specify a file extension, it will create 3 files, 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 audio files contained. The full syntax is:
python rp_classify.py input_path model_file output_filename
input_path can be: a folder, wav, mp3, m4a or aif(f) file, or txt file containing a line-wise list of audio 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.
In this default case, files in input_path must be organized in sub-folders named like the categories to be used for training (e.g. one folder named 'pop', one for 'electronic', one for 'classical' etc.)
Alternatively, you can provide the class labels for training a new model in two alternative ways, adding an additional parameter to the command line:
-c classfile: expects the name of a TAB-separated file, where each line contains<audiofilename>TAB<class_label>-m multiclassfile: also a TAB-separated file with<audiofilename>in the first column, and additional columns with the class labels in the header (1st line) and an 'x' for each file belonging to the class, an empty TAB position otherwise.
Once you trained a model in one of these ways, you can do predictions like above:
python rp_classify.py input_path model_file output_filename
Note: Specify the model_file always without file extension as 3 files will be generated with different extensions.