14th International Society for Music Information Retrieval Conference (ISMIR 2013)

TRANSFER LEARNING IN MIR: SHARING LEARNED LATENT
REPRESENTATIONS FOR MUSIC AUDIO CLASSIFICATION AND
SIMILARITY
Philippe Hamel, Matthew E. P. Davies, Kazuyoshi Yoshii and Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST), Japan
hamelphi@google.com, {matthew.davies, k.yoshii, m.goto}@aist.go.jp

ABSTRACT  This paper discusses the concept of transfer learningand its potential applications to MIR tasks such as music
audio classiﬁcation and similarity.
In a traditional supervised machine learning setting, asystem can only use labeled data from a single dataset tosolve a given task. The labels associated with the dataset
deﬁne the nature of the task to solve. A key advantage oftransfer learning is in leveraging knowledge from related
tasks to improve performance on a given target task. One
way to transfer knowledge is to learn a shared latent representation across related tasks. This method has shown
to be beneﬁcial in many domains of machine learning, but
has yet to be explored in MIR.
Many MIR datasets for audio classiﬁcation present a semantic overlap in their labels. Furthermore, these datasets
often contain relatively few songs. Thus, there is a strong
case for exploring methods to share knowledge between
these datasets towards a more general and robust understanding of high level musical concepts such as genre andsimilarity.
Our results show that shared representations can improve classiﬁcation accuracy. We also show how transfer
learning can improve performance for music similarity.

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Figure 1: Schema of our transfer learning approach. In theﬁrst step, we learn a latent representation in a supervised
way using a source dataset. In the second step, we solve
the target task by ﬁrst mapping the features to the learned
latent space. In this example, the target task is a classiﬁcation task.

The equivalent concept in machine learning is called
transfer learning. It has been applied successfully in many
domains such as visual object recognition [13] and webpage classiﬁcation [8].
The performance of a supervised machine learning system is limited by the quantity and the quality of available
labeled data. Obtaining such data can be expensive. As aconsequence, many datasets in the MIR community havea relatively small number of labeled examples. Some ofthese datasets have been built to solve the same task, orsimilar tasks. For example, there exist many datasets forgenre classiﬁcation, and these datasets exhibit semantic
overlap in their labels. However, each individual dataset
contains a relatively small number of examples. In thiscontext, it would make sense to try to leverage the information from all these datasets to improve the overall performance. Transfer learning might allow us to do just that.
In this paper, we investigate how transfer learning applied to genre classiﬁcation, automatic tag annotation andmusic similarity can be beneﬁcial. We hypothesize thattransferring latent representations learned on related tasks
can improve the performance of a given task when compared with the original features. Our intuition is that thelearned representation will retain some knowledge of the

1. INTRODUCTION
As human beings, we are constantly learning to solve newtasks every day. The way we learn to perform new tasks is
inﬂuenced by what we know about similar tasks [17].
For instance, let’s think of a pianist that wants to learn toplay guitar. The musician already has some knowledge ofmusic theory, and knows how to use his motor skills to play
the piano. When he learns to play guitar, he will not startfrom scratch but rather use his prior knowledge on music
and motor skills and build on top of it. We can see it as if
the musician transfers knowledge between tasks by sharing
a common abstract internal representation of music.
Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are
not made or distributed for proﬁt or commercial advantage and that copies
bear this notice and the full citation on the ﬁrst page.
c 2013 International Society for Music Information Retrieval.


914th International Society for Music Information Retrieval Conference (ISMIR 2013)

Table 1: Characteristics of the genre classiﬁcation andautomatic tag annotation datasets.

original task and that this knowledge should make thegiven task easier to solve.
The paper is divided as follows. We begin with an
overview of transfer learning in Section 2. We describe thedifferent MIR tasks that are relevant to our experiments in
Section 3. In Section 4 we give details about how we handle our features. The representation learning algorithm ispresented in Section 5. We describe our experimental results in Section 6. Finally, we conclude in Section 7.

Dataset1517-Artists [11]GTZAN [14]Homburg [4]Unique [12]Magnatagatune [5]# of excerpts
3180
1000
1886
3115
22787

# of classes
19
10
9
14
160

Audio length
full
30s
10s
30s
30s

Table 2: Genre classes for the datasets. In bold are theterms which are also tags in the Magnatagatune dataset [5].

2. TRANSFER LEARNING

1517-Artists
Alternative & Punk
Blues
Childrens’s
Classical
Comedy & Spoken Word
Country
Easy Listening & Vocals
Electronic & Dance
Folk
Hip-Hop
Jazz
Latin
New Age
R&B & Soul
ReggaeReligious
Rock & Pop
Soundtracks & More
World

Transfer learning is a machine learning problem that focuses on reusing knowledge learned on one problem inorder to help solve another. More formally, we will distinguish between the target task, which is the task that we
ultimately want to solve, and the source task which is therelated task that will help us in solving the target task. It is
worth noting that there could be more than one source ortarget task.
Transfer learning is an active ﬁeld of research, andmany approaches have been proposed [2, 8, 13]. Pan etal. [6] describe four transfer learning approaches: i) instance transfer, ii) feature representation transfer, iii) parameter transfer and iv) relational knowledge transfer. In
this work, we will focus on the feature representation transfer approach, which consists of learning a common feature
space between the source and target tasks. More speciﬁcally, we will use a supervised approach to construct a feature space using labeled data from a source task, and thenuse this feature space to help solve the target task. Thistransfer learning process is illustrated in Figure 1.
Although transfer learning has been applied successfully in many domains, only a few applications can befound in the MIR domain. In [8], self-taught learning,which is an extension of semi-supervised learning, is applied to many tasks, including a 7-way music genre classiﬁcation. However, very few details are provided on the nature of the music data. In [3], a deep representation learnedon genre classiﬁcation is used for automatic tag annotation. Although the transferred representation is compared
to a set of audio features, there is no comparison to theoriginal spectral features that were used to build the deep
representation. Thus, it is difﬁcult to assess the impact of
the transfer of representation. In [10], a learned automatic
tag annotation system is used to produce features to help
solve a music similarity task. In [15], a method that attempts to capture the semantic similarities between audio
features, tags, and artists names is presented. This multitask approach consists of embedding the different concepts
in a common low-dimensional space. This learned space
can then be used to solve many MIR related tasks. In ourwork, we use a similar approach to build a shared latent
representation.

GTZAN
blues
classical
country
disco
hiphop
jazz
metal
pop
reggaerock

Homburg
alternative
blues
electronic
folkcountry
funksoulrnb
jazz
pop
raphiphoprock

Uniqueblues
country
dance
electronica
hip-hopjazz
klassikreggaerockschlagersoul rnb
volksmusik
world
wort

timation and automatic tag annotation. Even though thesetask all use music audio as input data, they differ in theirgoal and in the way performance is evaluated.
3.1 Genre Classiﬁcation
Genre classiﬁcation consists of choosing the genre thatbest describes an audio excerpt given a set of genre labels. We consider 4 different datasets for genre classiﬁcation: 1517-Artists [11], GTZAN [14] Homburg [4], andUnique [12]. These datasets each contain between 1000
and 3180 audio excerpts, from 10 seconds in length to fullsongs, classiﬁed in 9 to 19 genres. In the case where fullsongs are provided, we use only the ﬁrst 30 seconds of eachsong. Further details about the datasets are in Table 1. The
genre labels have strong semantic overlap across datasets
as can be seen in Table 2. For simplicity, we will sometimes refer to the 1517-Artists dataset as artists.
To evaluate the performance of a genre classiﬁcation
system, we use the classiﬁcation accuracy, which is simply the percentage of correctly classiﬁed excerpts in thetest set.
3.2 Music Similarity
Music similarity systems seek to obtain a measure of similarity between audio excerpts.
One issue with this task is that the meaning of similarity
is ill-deﬁned. What is considered similar by one listenermight not be the same for another. Another issue is that

3. TASKS AND DATASETSIn this paper, we investigate transfer learning over three related MIR tasks: genre classiﬁcation, music similarity es-

1014th International Society for Music Information Retrieval Conference (ISMIR 2013)

However, frame level features have been shown to be
suboptimal for genre classiﬁcation [1]. To obtain a betterclassiﬁcation performance, we aggregate features on windows of 64 frames (about 1.5s), computing the mean, variance, maximum and minimum of each feature. We can apply this aggregation process to the Mel-spectrum features
as well as to the frame-level latent representations. We willrefer to aggregated features as window-level features.

similarity is a pair-wise relative measure. Thus, it is complicated and costly to obtain enough ground truth information from human listeners to fully evaluate music similarity
systems. In order to circumvent these issues, music similarity systems often use genre labels as a proxy for similarity evaluation [7, 9, 12]. In this context, we consider that
two excerpts within the same genre must be more similar
than two excerpts from different genres. On this basis, wewill use the same datasets as for genre classiﬁcation in ourmusic similarity experiments.
Even though genre classiﬁcation and music similarity
use the same data, the tasks differ on how we use the dataand on how we evaluate performance. Typically, in the music similarity literature [7, 9, 12], the labels are not used fortraining. Thus, the task must be solved by signal processing, unsupervised learning, or, in our case, by transferring
supervised learning from external datasets.
The evaluation of music similarity systems typically use
precision at k as a performance measure. Precision at kgives the ratio of excerpts of the same class in the k nearest
neighbors of a given excerpt. In this work we use k = 10.
Approaches to solve this task typically consist of measuring distances in a feature space to obtain a distance matrix. The type of features and the distance measure used
can vary. In [7], distance is computed using the JensenShannon divergence on a Gaussian representation of thefeatures. In [12], an L1 -distance is computed over aggregated block-level features. In [9], an L1 -distance is computed on features extracted in an unsupervised fashion.
In this work, we use the L1 -distance on our different
feature sets in order to obtain a similarity matrix. We also
tested Euclidian distance and Cosine distance and obtained
similar results.

5. LEARNING A LATENT REPRESENTATION
In order to transfer knowledge between tasks, we aim
to learn a latent representation that will be shared acrosstasks. To learn this representation, we use the linear embedding method described in [16]. This method consists ofembedding both the features and the labels via linear transformations in a common space. This algorithm is built tohandle a large number of labels in a multi-label problem,
such as in the case of automatic tag annotation. However,
the model can trivially be adapted to multi-class problems
with a small number of classes such as genre recognition.
The model has also been extended to multi-task learning in
MIR in [15].
The algorithm seeks to map both the features and thelabels in a common latent space, as illustrated in Figure 2.
Given a feature representation x ∈ Rd and a set of labelsi ∈ Y = {1, ..., Y }, we seek to jointly learn a featureembedding transform that will map the feature space to asemantic space RD
Φx (x) : Rd → RD
and a label embedding transform that will map labels tothe same semantic spaceΦy (i) : {1, ..., Y } → RD .

3.3 Tag annotation

Thus, in this latent space, it is possible to measure distances between different concepts such as between two feature vectors, a feature vector and a label, or between twolabels.
Since we use linear maps, we have Φx (x) = V x where
V is a D × d matrix and Φy (i) = Wi where Wi is thei-th column of a D × Y matrix. We can obtain an afﬁnity
measure between a feature vector and a given label with

The automatic tag annotation task consists of assigningwords to describe an audio excerpt. It is a multi-label
problem, meaning that many labels can be applied to asingle example. In this paper, we use the Magnatagatune
dataset [5] which contains more than 22,000 30-seconds
excerpts and 160 tags. Tag labels include musical genre(rock, blues, jazz), instrumentation (guitar, piano, vocals),mood (sad, mellow), other descriptors (fast, airy, beat), etc.
There is high semantic overlap with the genre labels fromthe four genre datasets. We illustrate this, in Table 2, by
putting in bold the genres which are also tags in the Magnatagatune dataset.

fi (x) = Φy (i) Φx (x) = Wi V x.
Each training example has positive and negative labels
associated to it. Given a feature vector, an optimal representation would yield high afﬁnities for positive labels
and low afﬁnities for negative labels. In other words, if
we rank the afﬁnities of the labels to the feature vector, thepositive labels should be ranked low (i.e. in the ﬁrst few
positions), and the negative labels should be ranked high.
Computing the exact ranking of the labels becomes expensive when there are many labels. Thus, following [16] weuse a stochastic method that allows us to compute an approximate ranking.
The training procedure is as follows. For a given training example x , we randomly pick a positive label j. Then,

4. AUDIO FEATURES
In our experiments, we extract Mel-spectrum features fromaudio. We compute the Discrete Fourier Transform (DFT)
on frames of 46ms (1024 samples at 22kHz sampling rate)with half frame overlap. We then pass the magnitude spectrum through 200 triangle Mel-scaled ﬁlters and take thelog-amplitude to obtain the Mel-spectrum features. These
are what we will refer to as frame-level features.

11

14th International Society for Music Information Retrieval Conference (ISMIR 2013)      



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

6. EXPERIMENTS    

We conduct several experiments to assess if transferringknowledge across datasets and task can be beneﬁcial. First,
we qualitatively evaluate the semantic similarity in a multidataset genre embedding. Then, we compare genre classiﬁcation performance between tag embedding, genre embedding and the base features. Finally, we use these feature
spaces for the music similarity task.





    

Figure 2: Illustration of the learning of the latent representation. Audio features and labels are mapped to a common
embedding space via the transformations Φx and Φy . In
this example, excerpt X  has jazz as a positive label andblues as a negative example. The black arrows illustrate
how the learning gradient will push the negative label embedding and the feature embedding away from each other,while pulling the positive example embedding and the feature embedding together.

6.1 Semantic similarity
In our ﬁrst experiment, we learn an embedding jointly on
the four genre datasets. The combination of the four label
sets gives us a total 52 labels. We then look at the nearest
neighbours of the class embedding and make a qualitativeevaluation. If the embedding process learns semantic information about the classes as expected, similar classes acrossdatasets should be close to each other.
To do this, we compute a distance matrix using an L1 distance on the embeddings of all the classes. Then, foreach class, we look at which classes are the closest and perform a qualitative evaluation. Some typical examples arepresented in Table 3. In general the similar classes acrossdatasets tend to be close to one another. For example, inTable 3, we see that the jazz classes all end up near one
another in the embedding space. However, there are also
some problematic classes. For instance, the blues classes
do not appear to all be clustered together. From these results, we can say that the embedding space indeed learns
some kind of semantic knowledge about the classes.

we iterate randomly through the negative labels until we
ﬁnd a label j  for which fj  (x ) > fj (x ) − 1. If we do
not ﬁnd such a negative label, we move to the next training
example. If we only need a few iterations to ﬁnd such anegative label, chances are that the rank of the positive label is high, we thus try to correct this by boosting the loss.
On the contrary, if we need many iterations to ﬁnd sucha negative label, the rank of the positive label is probablyquite low, so we do not need to change the representation
as much. We then minimize the loss given by
L = L(r)|1 − fj (x ) + fj  (x )|rwhere L(r) = k=1 1/k and r is the approximated rankof the label j and is given by


Y −1
r=
N

6.2 Genre Classiﬁcation
For this experiment, we consider three sets of features foreach genre dataset: base features, genre embedding andtag embedding. The base features are the window-level
aggregated Mel-spectrum features described in Section 4.
For a given genre dataset, the genre embedding islearned jointly on the 3 other genre datasets. It is learnedon frame-level Mel-spectrum features. The frame-level
embedded features are then aggregated in a similar fashion as the base features to obtain window-level features.
The tag embedding is learned on the Magnatagatune
dataset. Again, the embedding is learned on frame-levelfeatures and these are then aggregated to obtain windowlevel features. We then train a simple linear regression
classiﬁer on the window-level features. Finally to classify
a song, we average the output of the classiﬁer on the wholesong and pick the class with the highest output.
One of the key strengths of transfer learning compared
to standard learning is the ability to improve performanceusing fewer training examples [8]. To test this hypothesis,
we measure the accuracy of the classiﬁer across a rangeof training examples per class in the target dataset. Since
the number of examples per class is unbalanced in somedatasets, there are cases where there are fewer examples forthe less frequent classes. We ran a 10-fold cross-validationfor each experiment. The results are shown in Figure 3.

where N is the number of iterations needed to ﬁnd j  ,and · is the ﬂoor function. The loss L is known as
the Weighted Approximate-Rank Pairwise loss, or WARP
loss [16]. The L term increases as the approximate rank r
grows. The second term in the loss can be seen as a kind of
hinge loss, which tries to maximize the margin. For a morein depth description of the algorithm, see [16] and [15] .
In our experiments we used a batch method, meaningthat we average the gradient over a batch before updating
the parameters. We use 100 examples per batch. For thedimensionality of our latent space, we followed [16] and[15] and chose D = 100 as the latent dimensionality for
all our experiments.
To extend the model to a multi-dataset setting, we simply alternate between datasets after each batch. The featureembedding transformation is shared across all datasets, but
the label embedding transformations are independent. In
this way, we do not assume any semantic similarity between similar classes across datasets. In Section 6.1, weshow that the model naturally learns these semantic similarities.

1214th International Society for Music Information Retrieval Conference (ISMIR 2013)

Table 3: Nearest neighbouring classes in the genre embedding space for a few examples.
Seed
Hip-Hop(artists)
Rock & Pop(artists)
Electronic & Dance(artists)
country(gtzan)
jazz(homburg)
blues(unique)

5 Nearest neighbours (in order)
hip-hop(unique), raphiphop(homburg), schlager(unique), hiphop(gtzan), Electronic & Dance(artists)rock(unique) rock(homburg) Alternative & Punk(artists) metal(gtzan) alternative(homburg)
raphiphop (homburg) reggae(unique) electronica(unique) pop(gtzan) dance(unique)country(unique), folkcountry(homburg), rock(unique), Country(artists), Religious(artists)
jazz(unique), jazz(gtzan), Jazz(artists), world(unique), dance(unique)alternative(homburg), Alternative & Punk(artists), blues(gtzan), funksoulrnb(homburg), rock(homburg)

Table 4: Classiﬁcation accuracy and standard error on thefull training set using a 10-fold cross-validation.
DatasetArtists
GTZAN
Homburg
UniqueBase Features
0.323 +/- 0.010
0.748 +/- 0.010
0.580 +/- 0.012
0.651 +/- 0.006

Genre Embedding
0.310 +/- 0.005
0.671 +/- 0.014
0.561 +/- 0.009
0.634 +/- 0.005

than the base features for the genre classiﬁcation task goesagainst our hypothesis that classiﬁcation accuracy shouldbe improved by such a representation. This might be dueto the fact that the genre datasets are rather small, and thus
there was not enough data to learn a robust representation. Another reason might be that some of the semantic knowledge that was learned ended up in the label embedding transform rather than the feature embedding transform. Since we did not use the label embedding transformin the classiﬁcation task experiment, some of the learned
knowledge might have been lost in the transfer. To address
this problem in future work, we could try to impose a more
severe regularization on the label embedding transformation in the learning process. This could help to force thesemantic knowledge to go in the feature embedding transformation.
In this work, to focus on the simplest case ﬁrst, we limited ourselves to basic feature aggregation, a linear embedding method, and a linear classiﬁer. Each of these elements
could be improved further. Thus the performance measurespresented in this paper might not reﬂect the full power oftransfer learning. For the features, more complex blocklevel features as described in [12] could be constructedfrom the learned frame-level representation. For the representation learning, non-linear mappings could be used toobtain a more powerful representation. Finally, more complex classiﬁers, such as support vector machines or neuralnetworks could be used to improve classiﬁcation accuracy
on the learned features.
This work presents a ﬁrst analysis of the potential oftransfer learning in MIR. We hope that the results presented here will stimulate more research in the ﬁeld andmotivate the application of transfer learning in future MIR
applications.

Tag Embedding
0.338 +/- 0.007
0.754 +/- 0.015
0.584 +/- 0.008
0.666 +/- 0.006

Table 5: Precision at 10 for the music similarity task on
different feature spaces. The genre embedding is learned
using the 3 other genre datasets. The tag embedding islearned on the Magnatagatune dataset.
DatasetArtists
GTZAN
Homburg
UniqueBase Features
0.15
0.48
0.36
0.53

Genre Embedding
0.19
0.52
0.41
0.52

Tag Embedding
0.19
0.53
0.40
0.54

These results show that the tag embedding often significantly outperforms the base features. This conﬁrms our
hypothesis. However, the genre embedding does not perform as well, obtaining better accuracy only for the Homburg dataset.
We then measured the accuracy of the three feature setson the full training dataset. The results are in Table 4. We
see that the tag embedding tends to give slightly better results.
6.3 Music similarity
For this task, we used the same 3 feature sets as in
Section 6.2. We use precision at 10 as the performancemeasure. Results are shown in Table 5. We see that both
the genre and tag embedding features perform better thanthe base features, except for the Unique dataset where thethree feature sets perform about as well.

8. ACKNOWLEDGMENTS
This work was supported by OngaCREST, CREST, JST.

7. CONCLUSION
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