Source code for the article "Data Driven and Discriminative Projections for Large-Scale Cover Song Identification" by Eric J. Humphrey, Oriol Nieto, and Juan P. Bello. ISMIR, Curitiba, Brazil, 2013.
This project includes three main tasks:
- Binary Task: Analyze 500 tracks as described in (Thierry et al. 2012).
- Cover Song ID in Second Hand Song Training Set (~12,000 songs on themselves).
- Cover Song ID in Second Hand Song Test Set (~5,000 songs on 1,000,000).
This task compares one given track against two others and decides which one of
the two is actually a cover of the given track. The list of tracks for this task
is found in the file SHS/list_500queries.txt.
To run the task without any dimensionality reduction (i.e. each track is represented by the full 12x75 vector), type:
./binary_task.py path_to_MSD
The result, as reported in (Thierry et al. 2012), should be 82.0%.
This package already provides a previously learned Principal Component Analysis
transformation (models/pca_250Kexamples_900dim_nocovers.pkl). To run the task
with a PCA of 50 components:
./binary_task.py path_to_MSD -pca models/pca_250Kexamples_900dim_nocovers.pkl 50
This should result in 82.2%, as reported in (Thierry et al. 2012).
To run the test using a dictionary to project the data into a (hopefully) more
separable space, we include a dictionary
(models/BasisProjection2_kE2045_actEdot_shkE0x200_anormETrue.pk) that can be
used like this:
./binary_task.py path_to_MSD -dictfile models/BasisProjection2_kE2045_actEdot_shkE0x200_anormETrue.pk
The result should be 81.2%.
To add Linear Discriminative Analysis to the new projected space:
./binary_task.py path_to_MSD -dictfile models/BasisProjection2_kE2045_actEdot_shkE0x200_anormETrue.pk -lda models/lda-kE2045-shkE0x200.pk n
where n is the index number of the model. In the given model (models/lda-kE2045-shkE0x200.pk), n = 0 represents 50 dimensions, n = 1 is 100 dimensions, and
n = 2 is 200 dimensions. As an example, with 200 dimensions (n = 2) the result
should be 94.4%.
This task computes the Mean Average Precision (MAP) and the Average Rank (AR) of 12,960 tracks from the Second Hand Song dataset. These tracks are the ones selected
for training, and are listed in SHS/shs_dataset_train.txt.
To run the task without any dimensionality reduction (i.e. each track is represented by the full 12x75 vector), type:
./cover_id_train.py path_to_MSD
The result should be exactly the same as the one reported by (Thierry et al. 2012): AR = 3096.7, MAP = 8.91%.
To apply PCA of 200 on these full features:
./cover_id_train.py path_to_MSD -pca models/pca_250Kexamples_900dim_nocovers.pkl 200
which should result in AR = 3005.1, MAP = 9.475%, as reported in (Thierry et al. 2012).
To apply a dictionary of k = 2045:
./cover_id_train.py path_to_MSD -dictfile models/BasisProjection2_kE2045_actEdot_shkE0x200_anormETrue.pk
This should result in AR = 3026, MAP = 5.51%, as reported in (Humphrey et al. 2013).
To apply LDA of 200 dimensions:
./cover_id_train.py path_to_MSD -dictfile models/BasisProjection2_kE2045_actEdot_shkE0x200_anormETrue.pk -lda
models/lda-kE2045-shkE0x200.pk 2
Which should result in a similar result than AR = 1880, MAP = 28.33%, as reported in (Humphrey et al. 2013).
This task compares the 5236 tracks from the test SHS dataset against all the 1,000,000 tracks from the MSD. Since computing the features for the entire MSD takes a long time, the code is written to be run in N different threads, so that the work can be split in N different processors.
To compute all the 1,000,000 codes with 10 processors, type:
./cover_id_test.py path_to_MSD -dictfile models/BasisProjection2_kE2045_actEdot_shkE0x200_anormETrue.pk -lda models/lda-kE2045-shkE0x200.pk -outdir msd_feats -N 10
Once all the features are computed inside a dir (e.g. msd_feats), we can
compute the score by:
./cover_id_test.py path_to_MSD -codes msd_feats/ n
where n is the index number of the LDA model. In the given model (models/lda-kE2045-shkE0x200.pk),
n = 0 represents 50 dimensions, n = 1 is 100 dimensions, and
n = 2 is 200 dimensions. For this script to optimally run, we will need as
much RAM memory as possible. The script works fine with 20GB of RAM. We also
tried to run it with 6GB, but it took around 2 hours to load all the features,
instead of a couple of minutes.
Note that in this task we can't compute the entire full features code (12x75, or, if projected to a higher dimensional space, 2045), since it would take too long to run.
Million Song Dataset: http://labrosa.ee.columbia.edu/millionsong/
Numpy: http://www.numpy.org/
Scipy: http://www.scipy.org/
PyLab: http://wiki.scipy.org/PyLab
Scikit-Learn: http://scikit-learn.org/stable/
Multi-core processor. At least 10 units are recommended.
And as much RAM as possible. 16GB is recommended.
Humphrey, E. J., Nieto, O., & Bello, J. P. (2013). Data Driven and Discriminative Projections for Large-Scale Cover Song Identification. In Proc. of the 14th International Society for Music Information Retrieval Conference. Curitiba, Brazil.
Bertin-Mahieux, T., & Ellis, D. P. W. (2012). Large-Scale Cover Song Recognition Using The 2D Fourier Transform Magnitude. In Proc. of the 13th International Society for Music Information Retrieval Conference (pp. 241-246). Porto, Portugal.
Thanks to Thierry Bertin-Mahieux (tb2332@columbia.edu) for sharing his code and letting us publish it under the LGPL.
This code is distributed under the GNU LESSER PUBLIC LICENSE (LGPL, see www.gnu.org).
Copyright (c) 2012-2013 MARL@NYU. All rights reserved.
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