=Paper=
{{Paper
|id=Vol-2283/MediaEval_18_paper_30
|storemode=property
|title=MediaEval 2018 AcousticBrainz Genre Task: A CNN Baseline Relying on Mel-Features
|pdfUrl=https://ceur-ws.org/Vol-2283/MediaEval_18_paper_30.pdf
|volume=Vol-2283
|authors=Hendrik Schreiber
|dblpUrl=https://dblp.org/rec/conf/mediaeval/Schreiber18
}}
==MediaEval 2018 AcousticBrainz Genre Task: A CNN Baseline Relying on Mel-Features==
https://ceur-ws.org/Vol-2283/MediaEval_18_paper_30.pdf
MediaEval 2018 AcousticBrainz Genre Task:
A CNN Baseline Relying on Mel-Features
Hendrik Schreiber
tagtraum industries incorporated, USA
hs@tagtraum.com
ABSTRACT Dataset Number of Parameters
These working notes describe a relatively simple baseline for the AllMusic 918,646
MediaEval 2018 AcousticBrainz Genre Task. As classifier it uses Discogs 685,479
a fully convolutional neural network (CNN) based on only the Last.fm 691,683
lowlevel AcousticBrainz melband features as input. tagtraum 675,656
Subtask 2 1,258,315
1 INTRODUCTION Table 1: Number of network parameters per dataset.
We present a baseline approach for the MediaEval 2018 Acoustic-
Brainz Genre Task. The task is defined as follows:
Based on provided track-level features, participants have to es-
timate genre labels for four different datasets (AllMusic, Discogs, samples. Each of the 40-dimensional feature vectors is scaled so
Last.fm, and tagtraum), featuring four different label namespaces. that its maximum is 1.
Subtask 1 asks participants to train separately on each of the datasets
and their respective labels and predict those labels for separate test 2.2 Neural Network
sets. Subtask 2 allows training on the union of all four training We choose to use the fully convolutional network (FCN) architecture
datasets, but still requires predictions for the four test sets in their depicted in Figure 1. In essence, the network consists of four similar
respective label spaces. For more details about the tasks see [1]. feature extraction blocks, each formed by a one-dimensional con-
volutional layer, an ELU activation function [2], a dropout layer [9]
2 APPROACH with dropout probability 0.2, an average pooling layer (omitted in
Our baseline approach explores how well a convolutional neural the last extraction block), and lastly a batch normalization layer [4].
network (CNN) performs that has been trained on a relatively small From block to block the number of filters is increased from 64 to
subset of the available pre-computed features. For this purpose we 512 as the length of the input decreases from 40 to 5 due to average
have chosen to train only on Mel-features. The complete code is pooling with a pool size of 2. The feature extraction blocks are
available on GitHub1 . followed by a classification block consisting of a one-dimensional
convolution, an ELU activation function, a batch normalization
2.1 Feature Selection layer, a global average pooling layer and sigmoid output units. The
Traditionally, music genre recognition (MGR) has often relied on sigmoid activation function for the output is used, because the task
Mel-based features—in fact, one of the most often cited MGR publi- is a multi-label multi-class problem. Note that the number of output
cations uses Mel-frequency cepstral coefficients (MFCCs) [10]. Mel- dimensions depends on the number of different labels in the dataset.
based approaches attempt to capture the timbre of a track, thus We therefore refer to it with the placeholder OUT. The total number
allowing conjectures about its instrumentation and genre. They do of parameters in each networks is listed in Table 1.
not necessarily take temporal properties into account and therefore
often ignore an important aspect of musical expression, which can 2.3 Training
also be used for genre/style classification, see e.g., [8]. But since For subtask 1 we train the network using the provided training and
we are only interested in finding a baseline for more sophisticated validation sets with binary cross-entropy as loss function, Adam [5]
systems, using just the provided melbands features is a reasonable with a learning rate of 0.001 as optimizer, and a batch size of 1,000.
approach. Lowlevel AcousticBrainz2 data offers nine different Mel- To avoid overfitting we employ early stopping with a patience of
features (global statistics: min, max, mean, ...) with 40 bands each, 50 epochs and use the last model that still showed an improvement
resulting in a total of 360 values per track. Because Mel-bands have in its validation loss.
a spatial relationship to each other, we organize the data into nine Because the training data is very unbalanced, we experimented
different channels, each featuring a 40-dimensional vector result- with balancing the training data with respect to the main genre
ing in a (N , 40, 9)-dimensional tensor with N being the number of labels via oversampling. As this led to worse results, balancing is
1 https://github.com/hendriks73/melbaseline not part of this submission.
2 https://acousticbrainz.org/
For subtask 2 we gently normalize the provided labels by con-
verting them to lowercase and removing all non-alphanumeric
Copyright held by the owner/author(s).
MediaEval’18, 29-31 October 2018, Sophia Antipolis, France characters. Based on these transformed labels we create a unified
training set.
MediaEval’18, 29-31 October 2018, Sophia Antipolis, France Hendrik Schreiber
Average per Dataset
AllMusic tagtraum Last.fm Discogs
Input 9x40
Track P 0.292 0.3587 0.3707 0.3659
(all labels) R 0.4669 0.5074 0.4692 0.5436
F 0.306 0.3918 0.374 0.3972
Conv1D 64x3, ELU Track P 0.6013 0.6149 0.5617 0.6937
Dropout 0.2 (genre labels) R 0.6777 0.6772 0.6318 0.7522
AvgPool1D 2 F 0.6072 0.6271 0.5738 0.6902
BatchNorm
Track P 0.2031 0.256 0.228 0.2049
Conv1D 128x3, ELU (subgenre R 0.3116 0.4135 0.3284 0.3662
Dropout 0.2 labels) F 0.216 0.2922 0.2461 0.236
Feature Extraction
AvgPool1D 2
BatchNorm Label P 0.1141 0.1753 0.1993 0.1624
(all labels) R 0.1447 0.2213 0.2261 0.2115
Conv1D 256x3, ELU F 0.1148 0.1824 0.1977 0.1656
Dropout 0.2
AvgPool1D 2
Label P 0.3213 0.3444 0.3645 0.4523
BatchNorm (genre labels) R 0.3384 0.3627 0.3812 0.4519
F 0.3239 0.3467 0.3661 0.4466
Conv1D 512x3, ELU Label P 0.1083 0.1555 0.1826 0.1479
Dropout 0.2 (subgenre R 0.1392 0.2047 0.2105 0.1995
BatchNorm
labels) F 0.1089 0.1632 0.1807 0.1515
Conv1D OUTx1 , ELU
Table 2: Precision, recall and F-scores for subtask 1.
Classification
BatchNorm
GlobalAvgPool1D
Sigmoid OUT
Average per Dataset
Figure 1: Schematic architecture of the neural network. AllMusic tagtraum Last.fm Discogs
Track P 0.285 0.359 0.3411 0.3563
(all labels) R 0.4713 0.5079 0.4773 0.544
2.4 Prediction F 0.3041 0.385 0.354 0.3877
The output of the last network layer consists of as many values Track P 0.5923 0.594 0.5068 0.6801
in the range [0, 1] as we have different labels in the dataset (OUT). (genre labels) R 0.6762 0.678 0.6434 0.7484
F 0.6011 0.6127 0.5413 0.6802
If one of these values is greater than a predefined threshold, we
assume that the associated label is applicable for the track. In order Track P 0.2071 0.2486 0.2021 0.1959
to optimize the tradeoff between precision and recall, we choose (subgenre R 0.3198 0.4131 0.3282 0.37
this threshold individually for each label based on the maximum labels) F 0.2205 0.2825 0.2261 0.229
F-score for predictions on the validation set [6], also known as Label P 0.1127 0.1662 0.1703 0.1526
plug-in rule approach [3]. In case the threshold is not crossed by (all labels) R 0.1466 0.2272 0.2176 0.2138
any prediction for a given track, we divide all predictions by their F 0.1141 0.1797 0.1763 0.1619
thresholds and pick the label corresponding to the largest value. Label P 0.3174 0.3291 0.3304 0.4491
Since we are using one unified training set for subtask 2, we need (genre labels) R 0.3395 0.363 0.3874 0.4454
to reduce its output to labels that are valid in the context of a specific F 0.3191 0.3398 0.3484 0.4407
test dataset. We do so by reverting the applied normalization and Label P 0.1069 0.1471 0.1541 0.1378
dropping all labels not occurring in the test dataset. (subgenre R 0.1412 0.2114 0.2005 0.2022
labels) F 0.1083 0.161 0.1589 0.1479
3 RESULTS AND ANALYSIS Table 3: Precision, recall and F-scores for subtask 2.
We evaluated a single run for both subtask 1 and 2. Results are listed
in Tables 2 and 3. As expected, all results are well below last year’s
winning submission [6], which used a much larger network and
2,646 features. But the achieved scores are competitive with last
year’s second ranked submission [7], which used a similar number 4 DISCUSSION AND OUTLOOK
of features, though very different ones. Somewhat unexpected, the We have shown that using a relatively small and simple convolu-
network trained for subtask 2 was not able to benefit from the addi- tional neural network (CNN) trained only on global Mel-features
tional training material and reaches generally slightly lower results can achieve respectable scores in this task. Adding temporal fea-
than the networks trained on individual datasets for subtask 1. tures may improve the results further.
AcousticBrainz Genre Task MediaEval’18, 29-31 October 2018, Sophia Antipolis, France
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