Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

A Framework for Music Similarity and Cover Song
Identification
Roberto Piassi Passos Bodo1 , Emmanouil Benetos2 , and Marcelo Queiroz1
1

Institute of Mathematics and Statistics, University of São Paulo, Brazil{rppbodo,mqz}@ime.usp.br
2
Centre for Digital Music, Queen Mary University of London, UK
emmanouil.benetos@qmul.ac.uk

Abstract. This paper presents a framework for music information retrieval tasks
which relate to music similarity. The framework is based on a pipeline consisting of audio feature extraction, feature aggregation and distance measurements,
which generalizes previous work and includes hundreds of similarity models notpreviously considered in the literature. This general pipeline is subjected to acomprehensive benchmark of analogously defined music similarity models over
the task of cover song identification. Experimental results provide scientific evidence for certain preferred combined choices of features, aggregations and distances, while pointing towards novel combinations of such elements with the potential to improve the performance of music similarity models on specific MIR
tasks.

1

Introduction

Using Music Information Retrieval (MIR) techniques to deal with large sets of music
files has become an increasingly common practice. Working directly with audio andmusical contents has several advantages. MIR methods can provide users the ability tohum in order to retrieve a melody or to clap to fetch a rhythm, and to use an audio fileas query in a search for similar tracks. The goal of MIR is to make music content moreaccessible and in a more intuitive way [1].
Music similarity plays a central role in several MIR tasks. It is often desirable todefine and calculate similarity measures for pairs of music recordings, based on audio
contents and also (derived or annotated) metadata. The use of music similarity measures
on a music dataset provides a solid foundation for navigation, organization, recommendation, and search [2,3].
Since there is no universally agreed-upon formalized concept of general musicalsimilarity, a fair solution is to look for similarity models which deal with individual aspects of music, such as pitch, rhythm, dynamics and timbre, providing tools for melodic,
harmonic, rhythmic, dynamic and timbre-related retrieval tasks, among others. It is important to state explicitly that the notion of music similarity completely depends on thecontext of the retrieval task at hand, which is usually established by the type of dataset
annotations available.
This study was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nı́vel Superior- Brasil (CAPES) - Finance Code 001, and CNPq Grant 307389/2019-7

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The literature on audio-based music similarity presents several approaches, including the use of traditional information retrieval methods, such as extracting features fromaudio recordings and computing their distances within a vector space [4,5,6,7,8,9],modeling the extracted feature distributions and comparing the corresponding statistical models [10,11,12,13,14,15], feature learning [16,17], metric learning [18,19], anddeep neural networks [20,21].
The framework presented in this paper, which generalizes the first two approaches
above, is based on a conceptual pipeline [3] that breaks down a generic music similarity
model into three components (feature extraction, aggregation and distance computing),
completely specified by the choices of techniques employed in each component. Its
implementation allows the user to freely combine virtually any techniques within eachcomponent, thus providing a direct way of experimenting with a large number of similarity models at once.
This possibility is explored in the context of music similarity tasks, including CoverSong Identification (CSI) [22], an application which involves identifying songs3 which
are versions (covers) of each other, assuming that versions of a song should have somecommon music trait captured by a music similarity model. This paper presents, to thebest of the authors’ knowledge, the first attempt to comprehensively benchmark music
similarity models in music similarity tasks, where hundreds of models not previously
considered in the literature are tested.
The main goal of the experiments here presented is to identify which music similarity models lead to best results for the annotated datasets considered, which have been
compiled for melodic similarity tasks, rhythmic similarity tasks, genre classification
and CSI. Another contribution of this paper is a modular open-source framework4 formusic similarity offering numerous alternatives for feature extraction, aggregation anddistances.
The remainder of the paper is organized as follows: Section 2 presents the music
similarity models considered; Section 3 presents the metrics considered to assess thediscriminating power of the music similarity models; Section 4 presents the experiments, including the selected datasets, the experimental design, the results and their
discussion; Section 5 outlines the conclusions and directions for future work.

2

Music Similarity Framework

The music similarity framework considered here implements the following pipeline:
1. extract audio features; 2. aggregate local features into global features; and 3. computethe similarities of every pair of audio recordings within a dataset. A triple {extractori ,aggregatorj , distancek } defines a music similarity model, and our main goal is tobenchmark music similarity models, identifying which models lead to best results foreach annotated dataset. Additionally, the models are also applied to datasets designed
for Cover Song Identification (CSI), another similarity-based music retrieval task.
3

in the CSI literature, song is often taken as a synonym of audio recording, regardless of containing singing voice or not.
4
The source code can be found at https://github.com/rppbodo/
music-similarity-framework.

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Papers addressing music similarity related tasks, including CSI, often derive theirsimilarity measurements from tonal features, such as chromagrams [23,24,7,25], tonnetz [26], and symbolic melodic sequences [27,28,29,30,31,32]. Also used in music
similarity retrieval tasks are timbre features (e.g., Mel-Frequency Cepstral Coefficients(MFCC) [4,10,11,13,12,15]), spectral features (e.g., spectral centroid, bandwidth, contrast, flatness, etc) [5], rhythmic features (e.g., Rhythm Pattern) [33,6], and amplitude/energy features (e.g., Root Mean Square (RMS)) [34,35].
Among aggregation methods applied in music similarity are simple statistics (suchas mean, standard deviation, skewness and kurtosis, see e.g. [15]), computed from thefeatures themselves and their 1st order differences, Gaussian Mixture Models [11,12,13],
Vector Quantization [10], Markov Chains [36], Octave and Interval Abstractions [32],and Pitch Contour (using 3-levels [27] and 5-levels [31]).
The computation of the similarity between two audio recordings is based on a chosen distance applied to the (possibly aggregated) features. Distances relevant for music
similarity are Manhattan [6,8], Euclidean [4,5,7], Cosine [4,9], Longest Common Subsequence based distances [37,38], Levenshtein [39,38], Kullback-Leibler [13,15], EarthMover [10,14], and Monte Carlo distances [11,12].
The detailed analysis of the techniques proposed in the music similarity literatureallows us to observe that several papers do not explicitly argue as for why a particular
extractor (or aggregator, or distance) is selected to solve a particular problem. Even lessfrequent are arguments about why a specific set of techniques are used in combination(instead of many other plausible alternatives). This prompted us to try to explore hundreds of combinations of extractors, aggregators, and distances that are not consideredin the literature. It was thus natural to look at this problem as a benchmark, exhaustively
experimenting with a large number of music similarity models.
The current list of music similarity models considered in the implemented framework started out from a large set of features, aggregators and distances appearing in
the related literature, which has been modified by including and collecting techniques,but also by discarding techniques by many criteria, including the availability of opensource implementations. The rationale for this specific criterion is to avoid producing
implementations that might substantially differ from their original implementations due
to ambiguous or insufficiently detailed descriptions. A survey of open-source libraries(such as LibROSA5 , Essentia6 , and RP extract7 ) led us to include techniques not previously considered in the music similarity literature. The same criteria were applied toaggregator and distance techniques, but in a softer way, since they are usually much
simpler to implement.
Due to compatibility issues, not all available features, aggregators and distances canbe combined. Framewise numerical features may be aggregated using any statistical aggregation methods, GMM and VQ. Symbolic melodic features use only specific aggregators (octave/interval abstractions, pitch contours and Markov chains). Numerical aggregations (single and multivariate Gaussians, GMM, vector quantization, and Markov
chains) can be compared using spatial distances (Euclidean, Manhattan, Chebyshev,
5

http://librosa.github.io/
http://essentia.upf.edu/
7
https://github.com/tuwien-musicir/rp_extract
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and cosine). Statistical models can be compared using Kullback Leibler, Earth Mover’s
and Monte Carlo distances, and all symbolic global features can be compared using
LCS-based and Levenshtein distances.
All the compatible combinations of features, aggregators, and distances consideredresult in a total of 690 music similarity models; the complete list is available at https://rppbodo.github.io/phd/music-similarity-models.html, along withdescriptions of each function.

3

Music Similarity and Cover Song Identification metrics

The most common way to represent a particular music similarity model applied to aparticular dataset is the similarity matrix. The i, j position of this matrix contains thesimilarity between the i-th and the j-th tracks in the dataset. It can be defined from anormalized distance measure as sim(ti , tj ) = 1 − dist(ti , tj ).
Intra-Inter Class Similarity Ratios (IICSR) When the dataset partitions its recordings into labeled classes (e.g. genres, composers, melodic or rhythmic patterns), we
may define the quality of a music similarity model using the intra-inter-class similarity
ratio, computed from the similarity matrix according to the following formula:
IICSR(c) =t2 ∈Tc ,t1 =t2 sim(t1 ,t2 )(|Tc |2 +|Tc |),sim(t1 ,t2 )
t ∈Tc
t ∈T
t1 ∈Tc

1

2

(1)

Cc

(|TCc ||Tc |)where Tc is the set of all recordings in class c and TCc is the complement of Tc . Thismeasure compares the average similarity within the class c (weighted by the number ofrecordings in this class) with the average similarity for pairs of recordings in different
classes (with one member of the pair in class c). If these ratios are greater than 1, thesimilarity model may be used to classify pairs of recordings as belonging to the sameclass or to different classes. Intra-inter-class similarity ratios may be summarized bytheir weighted average:
weighted mean IICSR = 1c∈C |Tc | c∈C

IICSR(c) × |Tc |,(2)

where each class is weighted by its size (number of recordings).
Mean Rank (MR) The Mean Rank is broadly used in the CSI literature [9,40], where
queries return ranked lists of cover candidates. MR corresponds to the average position(rank) where the first cover appears in the resulting list.
Mean Reciprocal Rank (MRR) The reciprocal rank (inverse of a rank) [41] converts
index positions to the [0, 1] range, where higher values represent covers higher up in
the list (topmost ranks). MRR corresponds to the average of the reciprocal ranks, andits inverse may be viewed as the harmonic mean of the original ranks.

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

datasetntracks nclasses annotations
Ballroom698
10
dance styles names
GTZAN
1000 10musical genres
IOACAS-QBH
1057 298
ground-truth melody id
Panteli’s melody dataset 3000 30
original melody id
Panteli’s rhythm dataset 3000 30
original rhythm id
MAST3104 40
ground-truth rhythm id
1517-Artists
3180 19
musical genres
MIR-QBSH
4479 48
ground-truth melody id
FMA-Small
8000 8musical genres
Covers80
160
80
original song
YouTubeCovers
350
50
original song
Covers1000
1000 395
original song
Mazurkas
2741 49
mazurka id
SHS9K
9286 143
original song
Table 1. Datasets selected to experiment Music Similarity models.

Median Rank (MDR) The MDR is a robust statistic based on the positions of the first
retrieved cover, obtained as the median of the ranks for all queries.
Mean Average Precision (MAP) Kim Falk [42] defines Mean Average Precision in
the context of recommender systems, in which users perform queries, and each query
returns a list of ranked items. Precision at K (P@k) is thenumber of relevant items
m1
found in the first k items; Average Precision (AP) = m
k=1 P @k(u), where m is
the length of the ranked
list, and u is the user performing the query; Mean Average
Precision (MAP) = |U1 | u∈U AP (u), where U is the set of all users.

4

Experiments and Results

4.1

Datasets

In this Section we present the datasets used to benchmark models within our music
similarity framework. The first part of Table 1 presents datasets designed for various
music similarity tasks, and the second part presents the datasets designed specificallyfor Cover Song Identification.
Three datasets are designed for melodic similarity tasks: MIQ-QBSH8 and IOACASQBH9 are designed for the query-by-humming task (classes are composed of a reference melody and a set of recordings of people trying to hum it), and Maria Panteli’s
melody dataset10 uses synthesis to test similarity models against several melodic transformations.
8

http://mirlab.org/dataSet/public/MIR-QBSH-corpus.rar
http://mirlab.org/dataSet/public/IOACAS_QBH.rar
10
https://archive.org/details/panteli_maria_melody_dataset
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Three other datasets – Ballroom11 , MAST12 , and Maria Panteli’s rhythm dataset13— are designed for tasks related to rhythm similarity. The Ballroom dataset is composedof recordings from distinct dance styles; MAST has recordings of students successfully
reproducing rhythmic patterns; Maria Panteli’s rhythm dataset is composed of different
synthesized rhythms subjected to several transformations.
The three remaining datasets in the first part of Table 1 — GTZAN14 , 1517-Artists15 ,and FMA-Small16 -– are annotated with music genres assigned to each recording. Several papers in the literature claim that there is a relationship between genre and timbre [43,44,3], and under this assumption, these datasets could be used to test timbre
similarity models.
The second part of Table 1 presents datasets designed for CSI: Covers8017 , YouTubeCovers18 , Covers100019 , Mazurkas20 , and SHS9K21 . The latter is a sub-set of the SHS100K22
dataset crafted by the authors by selecting the original songs that have from 50 to 100
covers.
4.2

Experiment Design

Two experiments are proposed. The goal of the first experiment is to check which music
similarity models lead to best results for the selected datasets. In order to accomplish
this we run each one of the 9 datasets considered through our music similarity framework, compute the Intra-Inter Class Similarity Ratio (IICSR) for every annotated classwithin the dataset, and finally compute the weighted mean IICSR for each one of the690 considered models.
The second experiment has a similar goal to the previous one – to check which music similarity models lead to the best results – but now with CSI datasets considering thespecific metrics used in this task. We compute similarity matrices using all 690 models
for the 5 CSI datasets, and then calculate the Mean Rank (MR), Mean Reciprocal Rank
(MRR), Median Rank (MDR), and Mean Average Precision (MAP).
4.3

Results

The results of the first experiment are organized as follows: the best weighted mean
IICSR values for each dataset are presented in Table 2, and the entire list of IICSR
values computed in this experiment is published in https://rppbodo.github.
io/phd/experiment_1.html.
11

http://mtg.upf.edu/ismir2004/contest/tempoContest/node5.html
https://zenodo.org/record/2620357
13
https://archive.org/details/panteli_maria_rhythm_dataset
14
http://marsyas.info/downloads/datasets.html
15
http://www.seyerlehner.info/index.php?p=1_3_Download
16
https://github.com/mdeff/fma/17
https://labrosa.ee.columbia.edu/projects/coversongs/covers80/
18
https://sites.google.com/site/ismir2015shapelets/data
19
http://www.covers1000.net/
20
http://www.mazurka.org.uk/
21
https://rppbodo.github.io/phd/shs9k.html
22https://github.com/NovaFrost/SHS100K
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datasetmean IICSR extractor
aggregator
distance
Ballroom
1.33824spectral bandwidth vector quant.
cosineGTZAN
1.74945
spectral contrast vector quant.
cosineIOACAS-QBH 1.13599
pitch cont. seg.
octave abst.
lcs circular min
Panteli’s melody 3.12167
pitch cont. seg.
interval abst.
levenshtein max
Panteli’s rhythm 2.82936
chroma cens
vector quant.
manhattan
MAST1.47572mfcc
vector quant.
cosine1517-Artists
1.21584mfcc
vector quant.
cosineMIR-QBSH
1.29421
pitch cont. seg.
octave abst.
levenshtein circular max
FMA-Small
1.21128mfcc
vector quant. default cosine
Table 2. Results obtained with Music Similarity datasets.
datasetMR
MRR MDR MAP extractor
aggregator distanceCovers80
41.575 0.19359 31.0 0.19359 chroma stft
diff stats 1 cosine oti
YouTubeCovers 7.97143 0.6942 1.0 0.36114 pitch cont. seg. octave abst. lcs circular mean
Covers1000
144.041 0.25731 35.0 0.19159 pitch cont. seg. octave abst. lcs circular mean
Mazurkas
4.15724 0.95774 1.0 0.82286 pitch cont. seg. octave abst. levenshtein circular max
SHS9K
47.57883 0.40387 6.0 0.05102 pitch cont. seg. octave abst. lcs circular mean
Table 3. Results obtained with Cover Song Identification datasets.

The results of the second experiment are displayed as follows: the best models foreach dataset are presented in Table 3, and the entire list of metrics computed in this experiment is published in https://rppbodo.github.io/phd/experiment_
2.html.
4.4

Discussion

Analysing the models that achieved the best weighted mean IICSR for MIQ-QBSH,IOACAS-QBH, and Maria Panteli’s melody dataset, it is possible to verify that all ofthem have Pitch Contour Segmentation as their feature, which matches the hypothesisthat melodic features lead to better results for melodic datasets. Regarding the aggregators, two models have Octave Abstraction and one has Interval Abstraction. This issomehow expected, since the alternative abstractions (3-level and 5-level Pitch Contours) are relatively weaker due to their simplistic representations of the original pitch
sequences.
The models that performed best for the Ballroom, MAST, and Maria Panteli’s rhythm
dataset are relatively surprising, not only because none of the features are specificallydesigned for rhythmic similarity tasks, but also because they are very different fromeach other: Spectral Bandwidth is related to the spectrum spread, MFCC is usuallyassociated with timbre, and Chroma Energy Normalized Statistics (CENS) is a tonal
feature.
Regarding the highest weighted mean IICSR obtained for GTZAN, 1517-Artists,
and FMA-Small, two out of three best performing models have MFCC as their feature,and the other one has Spectral Contrast. MFCC is a feature usually related to timbre, so
this matches the initial hypothesis. According to Jiang et al. [45], Spectral Contrast is211

Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

reported to have a better discriminating power for different music types than MFCC, so
it is noteworthy that this feature has also emerged here.
The best models that lead to the lowest Mean Rank values for the five CSI datasets
are shown in Table 3. Four models out of five share the same feature (Pitch Contour
Segmentation) and the same aggregator (Octave Abstraction), which is a very good
indication of the relevance of these methods, while the remaining model uses a Chromagram as feature. All features from the best models encode tonal information, whichmatches the observation in the literature that tonal differences are the less frequent between versions [22,46,47].

5

Conclusions

In this paper we introduced a modular music similarity framework designed to benchmark 690 music similarity models applied to specific music information retrieval tasks.
Our experiments compared these models under several datasets compiled for tasks requiring different music similarity perspectives, showing that the choices of features,
aggregators and distances not only have a significant impact on the performance ofthe corresponding models, but also that many useful techniques and combinations havebeen largely overlooked by the music similarity literature, corroborating the importanceof comparative studies such as the present one.
As future work, we consider expanding the lists of features (HPCP, crema-PCP,
Onset Patterns, Scale Transform, Pitch Bihistogram, Intervalgram, etc), aggregators(Dynamic Time Warping (DTW), Self-Organizing Map (SOM), vector quantization using tree-based clustering, n-grams, etc), and distances (Mahalanobis, Jensen-Shannon,
Smith- Waterman, Mongeau-Sankoff, etc) in the music similarity framework, as wellas incorporating alternative approaches to music similarity that not necessarily follow
the current pipeline, such as feature learning [16,17], metric learning [18,19], and deepneural networks [20,21].

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