GeometricCoverSongs

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GeometricCoverSongs

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Geometry features for block window cover song identification (a continuation of my ISMIR 2015 paper)

GeometricCoverSongs 최신버젼 다운로드

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# GeometricCoverSongs

Code from ``[Early MFCC And HPCP Fusion for Robust Cover Song Identification]'' by [Chris Tralie].  This code demonstrates how it is possible to combine complementary features using Similarity Network Fusion (a graph cross-diffusion algorithm) before alignment to improve accuracy.

## Getting Started
First, you will need to install the dependencies
* numpy/scipy/matplotlib
* [librosa]: Used for audio reading, dynamic programming beat tracking, MFCCs
* [Essentia] with Python bindings.  Used for HPCP features
* [Madmom] (optional): State of the art beat tracking, best if only one beat level is used

Once you have the dependencies installed, you can checkout the code
~~~~~ bash
git clone https://github.com/ctralie/GeometricCoverSongs.git
cd GeometricCoverSongs
~~~~~

You will also need to compile the C files used for Smith Waterman sequence alignment

~~~~~ bash
cd SequenceAlignment
python setup.py build_ext --inplace
~~~~~

## Quick Comparison of Two Songs with Detailed Plots
The file *SongComparator.py* is a quickstart for running the pipeline on a pair of songs.  The code will run and output cross-similarity matrices and Smith Waterman matrices for HPCPs, MFCCs, MFCC SSMs, and similarity fusion on all of the above.  Right now, it is set to compare an example from the [Covers80] dataset (see below), but you can modify the __main__ function to load in any two songs of your choosing.  One of the example plots for a song in the [Covers80] dataset is shown below

![Covers80 example](https://raw.githubusercontent.com/ctralie/GeometricCoverSongs/master/Covers80_4.png)

## Quick Comparison of Two Songs with GUI
The file *CSMViewer/CrossSimilarityExtractor.py* can be used to compare two songs and view their alignment using different feature sets in a GUI.  For example, the following run will compare a song called "MJBad.mp3" to a file called "AAFBad.mp3" and save the result in "SmoothCriminal.json," using the artist names "Michael Jackson" and "Alien Ant Farm"

~~~~~ bash
python CrossSimilarityExtractor.py --filename1 MJBad.mp3 --filename2 AAFBad.mp3 --jsonfilename SmoothCriminal.json --artist1 "Michael Jackson" --artist2 "Alien Ant Farm"
~~~~~

For a full list of options, please type
~~~~~ bash
python CrossSimilarityExtractor.py --help
~~~~~

Once you have run this script, you can open the generated JSON file in *CSMViewer/CrossSimilarityGUI.html* (or at http://www.covers1000.net/demo.html).  When you hit "play" in this GUI, you can jump back and forth between two songs by left clicking and right clicking on different pixels on the cross-similarity matrix.  You can try this with different feature sets to see which features pick up on which types of musical expressions more effectively (e.g. Chroma will work well for songs that are melodic, but not for hip hop songs, and similarity network fusion (SNF) will usually work better than all of them individually).

## [Covers80] Dataset

[click here] to download the [Covers80] dataset (164MB).  Extract the *covers32k* directory at the root of this repository.  Then you can run the file *Covers80.py* to replicate the experiments reported in the [paper].  The file will output an HTML table with mean rank, mean reciprocal rank, median rank, and covers80 score (see [paper] for more details).

NOTE: By default in *Covers80.py*, the *TempoLevels* variable is set as
~~~~~ bash
TempoLevels = [60, 120, 180]
~~~~~

which are the three tempo biases used in the dynamic programming beat tracker from [librosa].  If instead, you change it to
~~~~~ bash
TempoLevels = [0]
~~~~~
It will use just one level from [Madmom] (which requires the library to be installed).  The latter is state of the art if only one beat level is being used, but the overall performance is inferior to trying all three levels with the dynamic programming beat tracker (though the code is much faster).

## [Covers1000] Dataset

It is also possible to run the new [Covers1000] dataset that comes along with this paper using the *Cover1000.py* file.  Since computation takes so long, this file is designed to be run on a compute cluster.  First, you need to download all of the .zip files from http://www.covers1000.net/dataset.html and extract them all to a folder named *Covers1000* at the root of this repository.  Then, you need to run *Covers1000.py* many different times; first to extract block features, then to do pairwise comparisons.  To extract the features, run the file as follows

~~~~~ bash
python covers1000.py 1 0 <songIdx> <Kappa> <BeatsPerBlock> <doMadmom>"
~~~~~

where *<songIdx>* is the song number for which to compute the features (0-999), Kappa is the fraction of nearest neighbors when making binary cross-similarity matrices (usually 0.1), BeatsPerBlock is the number of beats per block for all of the blocked features (usually 20), and doMadmom is 1 if doing Madmom beats only and 0 if doing 3 levels in dynamic programming beat tracker.

Once you have computed blocked features for all 999 songs, it's time to compare them all in batches.  Run the file as follows

~~~~~ bash
python covers1000.py 0 <NPerBatch> <BatchNum> <Kappa> <BeatsPerBlock> <doMadmom>
~~~~~

Where NPerBatch gives the size of a patch in the all pairs similarity score matrix, and BatcNum gives the batch to compute.  For instance, if NPerBatch = 20, then compare 20 songs with 20 other songs, and since there are 1000 songs total, there are (1000/20)x(1000/20) = 2500 total of these batches to compare.  Once all batches have been completed, you can run the file *MIREX.py* (see below) with the collection and query list "covers1000collection.txt" and the scratch directory "Covers1000Scratch" to fuse all of them together in one matrix of all pairs of similarities (NOTE: This will take no time once covers1000.py has been run; it just saves code to use *MIREX.py* to fuse all of the blocks into one batch since that file also runs all pairs of comparisons in batch blocks).

## MIREX / General Batch Tests
There is a file *MIREX.py* for making batch tests convenient on all pairs of songs.  The command calling format is the same as specified in the [cover song identification wiki].  Namely, run the following command at the root of the directory:

~~~~~ bash
python MIREX.py <collection_list_file> <query_list_file> <working_directory> <output_file> <num threads>
~~~~~

For example
~~~~~ bash
python MIREX.py collections.list queries.list ScratchDir Results.txt 8
~~~~~

will run all pairs comparisons using songs in collections.list and queries.list and using "ScratchDir" as the scratch directory, using 8 threads for parallel computation.  After it's finished, 'Results.txt' will contain the table of scores between songs, formatted to specification.

[Chris Tralie]: <http://www.ctralie.com>
[Early MFCC And HPCP Fusion for Robust Cover Song Identification]: <http://www.covers1000.net/ctralie2017_EarlyMFCC_HPCPFusion.pdf>
[paper]: <http://www.covers1000.net/ctralie2017_EarlyMFCC_HPCPFusion.pdf>
[librosa]: <http://librosa.github.io/librosa/install.html>
[Essentia]: <http://essentia.upf.edu/documentation/installing.html>
[Madmom]: <http://madmom.readthedocs.io/en/latest/>
[Covers80]: <https://labrosa.ee.columbia.edu/projects/coversongs/covers80/>
[Covers1000]: <http://www.covers1000.net>
[Click here]: <https://labrosa.ee.columbia.edu/projects/coversongs/covers80/covers80.tgz>
[cover song identification wiki]: <http://www.music-ir.org/mirex/wiki/2017:Audio_Cover_Song_Identification>

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