Proceedings of the ICMC

July 25th-31st, 2021, Santiago, Chile

Joint Beat and Downbeat Tracking Based on CRNN Models and a Comparisonof Using Different Context Ranges in Convolutional Layers
Tian Cheng, Satoru Fukayama, Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST), Japan{tian.cheng, s.fukayama, m.goto}@aist.go.jp
ABSTRACTIn this paper, we address joint beat and downbeat tracking by using Convolutional-Recurrent Neural Networks(CRNNs). The model consists of four convolutional layers
and four bi-directional recurrent layers. In order to deal
with music in various styles, we propose to increase theconvolution filter sizes in the convolutional layers, whichhelps obtain more context information. We compare four
different filter sizes (covering 3 to 9 frames) to analyse thecontext effect on ten individual datasets. The mean crossvalidation results of eight datasets show that using context ranges of 5 and 7 frames perform better on downbeat
tracking than other context ranges. The comparison results on two testing-only datasets (an in-house pop dataset
and the SMC dataset) show the proposed CRNN model outperforms a previous state-of-the-art method with a context
range of 7 frames.
1. INTRODUCTION
Beat tracking and downbeat tracking are two fundamental
tasks for defining the metrical structure of a music piece[1]. They detect a hierarchical beat structure in two rhythmic levels: beat-level and bar-level [2]. Beat tracking
is ‘to determine the periodic sequence of beat positions’from a music piece [3], while downbeat tracking is to detect the first beat of each bar. Beats are basic time units
for analysing many other musical contents, and downbeats
are often related to changes of chords or rhythm patterns.
Therefore, beat and downbeat tracking is useful for various tasks, such as music transcription [4], chord estimation[5, 6], structure analysis [7, 8], and so on.
In the current decade, Recurrent Neural Networks(RNNs) have been used for beat tracking with the ability of modelling data in sequences, in which bi-directional
Long Short-Term Memory (LSTM) is usually used [9,10, 11]. For downbeat tracking, a similar RNN modelwith Gated Recurrent Units (GRUs, a simplified version of
the LSTMs) has been used to detect downbeats on beatsynchronised percussive and harmonic features in [12].
Convolutional Neural Networks (CNNs) have also beenused for downbeat tracking, with a Hidden Markov Model(HMM) detecting the downbeat positions from various
Copyright: c 2020 Tian Cheng, Satoru Fukayama, Masataka Goto et al.
This is an open-access article distributed under the terms of the Creative
Commons Attribution License 3.0 Unported, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original
author and source are credited.

(a) A pop music clip.

(b) A classical music clip.

Figure 1:

Spectral representations (as referred to Section 2.1) of two music clips from the RWC pop and RWCclassic datasets.
features learned from CNNs [13]. A combination of
CNNs and RNNs, Convolutional-Recurrent Neural Networks (CRNNs), have been compared with RNNs for
downbeat tracking in [14]. The results show that CRNNs
generally work better than RNNs, and provide more robust results on unseen data. Böck et al. propose a joint
beat and downbeat tracking model, which applies RNNs
to model beat and downbeat probabilities, and uses a Dynamic Bayesian Network (DBN) for detecting beat anddownbeat times from the probabilities [1]. There are alsomodels for jointly estimating beats (and downbeats) as wellas drums with CRNNs in a multi-task training [15, 16].
With previous work considered, we address joint beat anddownbeat tracking by using CRNNs in this paper. Firstly,
the CNN layers are used to detect the local events, suchas onsets, drums, harmonic changes, and so on. Then,RNN layers work on the CNN output to estimate beats anddownbeats globally by modelling the whole sequence.
Despite the advantages of CRNNs, one difficulty of beatand downbeat tracking is to deal with music in different
styles. It is easier to address dance music (the Ballroom
dataset [17, 18]) or pop music (the RWC pop dataset [19]),with percussive onsets and drums, as shown in Fig 1a.
However, it is more difficult when dealing with music with
soft and blurring onsets, such as music in the RWC classic
dataset [19], as shown in Fig 1b. In order to tracking beatsand downbeats of music in various styles, [10] proposesa multi-model approach, which trains an individual modelon each dataset, and chooses the final result by comparing
the results of the individual models to that of a reference
model (trained on all datasets). Because of multiple models, this method is more computationally expensive and notextendable.

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2.4 Postprocessing

July 25th-31st, 2021, Santiago, Chile
2.1 Signal Pre-Processing

Dynamic Bayesian
Network

DownbeatBeat

2.2 CRNN
networkDense layer

2.3 Training

RNN block
CNN block

We process audio signals to obtain Mel-spectrograms forthe input of the CRNN models. Firstly, we read each audio
signal into a monotrack waveform sequence with 44100
Hz sampling rate. To obtain the Mel-spectrograms, thesignal is segmented into frames of window sizes of 1024,
2048 and 4096 samples with a hop size of 441 samples[1]. For each window size, we compute a magnitude Melspectrogram of 36 Mel bins within a frequency range from
30 Hz to 17000 Hz. Then we rescale the magnitude Melspectrogram M into a log scale X, as follows:
X = log(1 + M ).

Concatenate

2.1 Signal preprocessing
Mel-spectrograms

Spectrogram differences

Figure 2: High-level architecture of the CRNN model.

In order to propose a more compact model, we increase
the filter sizes in the convolutional layers of the CRNN
models to cover a larger context range, and compare four
different context ranges (covering 3, 5, 7 and 9 frames,
respectively) on ten datasets to analyse the effect of thecontext range for different music styles. There was work
using different context ranges for drum transcription [15],but to the best of our knowledge, there is no such workfor beat and downbeat tracking. From the beat tracking
results, we find that for music in the RWC classic and
SMC datasets [20] with blurring onsets, a context rangeof 9 frames works best. Using different context ranges
has a bigger influence on downbeat tracking than on beattracking. In general (according to the mean results of eightdatasets), context ranges of 5 and 7 frames work better on
downbeat tracking than other context ranges.
The proposed models are adapted from the model usedin [1], with a similar signal pre-processing and the same
post-processing as in [1]. The main difference is that weapply the CRNN and [1] applied the RNN. Besides, we
also compare different choices of CNN filters in this paper. In comparison to the method [1] on the testing-only
datasets, the proposed models obtain better results on the
Songle dataset [21] (an in-house pop music dataset) with acontext range of 7 frames, and better results on the SMC
dataset with all context ranges. Although the idea of ourmodels is rather simple, we believe that it is worth sharing
results of intensive experiments achieving the state-of-theart performance with our research community to advancebeat and downbeat tracking research.

2. CRNN MODEL
We tackle joint beat and downbeat tracking by using
CRNNs in this section, with the high-level architecture of
the model shown in Figure 2.

(1)

We also compute the first order difference of the Melspectrograms (DX):DX = max(Xt − Xt−1 , 0).

(2)

We concatenate the three Mel-spectrograms and their differences into 6 channels, providing an input with a shapeof [36, T , 6], where T is the number of frames in a sequence. We show two input examples in Figure 1 with
spectrograms in 6 channels stacked together.
2.2 CRNN Network
The CRNN models consist of 4 convolutional layers,
4 bidirectional recurrent layers with GRUs and a fullconnected dense layer, with the architecture shown in Table 1. We build the model with tensorflow 1.13.1 [22] andKeras 2.2.4 [23].
The CNN block consists of four convolutional layers,
with a maxpooling layer stacked after each of the first twolayers, as shown in Table 1a. For the first three layers, we
compare four CNN settings with different context ranges.
For a context range of X frames, denoted by cnX, the widthof the convolution filters is X frames, while the heights are
set by considering the feature dimensions (36, 18 and 9) of
the first three convolutional layers. The detailed parameters are shown in Table 1b.
After the CNN layers, we reshape the output into a tensor
of [T , 64] as the input of the RNN layers.
In the preliminary test, we compare RNN architectures oflayer number ∈ {3, 4, 5, 6} and GRU number ∈ {32, 64}.
Finally we use 4 bidirectional layers with 64 GRUs per
layer in each direction because this architecture generally
works for all above CNN settings.
We stack a dense layer at the end of RNN layers with an
output dimension of 3, for the ‘no-beat’, ‘beat’ and ‘downbeat’ labels, respectively.
2.3 Training
Because of the sparse occurrence of the beats and downbeats, we extend the beat and downbeat range for an efficient training [24]. We use an extending range of one
frame, meaning that one frame before and one frame after abeat/downbeat are also labelled the same as beat/downbeat.
We train the CRNN models in 8-fold cross validation
with random splits by using the datasets in Table 2 withoutmark ∗. For each split, we use 75% for training, 12.5%
for validation and the rest 12.5% for testing. We compare three optimisers (the RMSprop [25], the Adam [26]

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Layer

July 25th-31st, 2021, Santiago, Chile

Parameters

input
conv2d 1
max pooling2d 1
conv2d 2
max pooling2d 2
conv2d 3
conv2d 4
reshape 1
bidirectional 1
bidirectional 2
bidirectional 3
bidirectional 4
dense 1

(cnX)
(2,1)
(cnX)
(2,1)
(cnX)
(64, 9x1)
(64)
(64)
(64)
(64)
(3)

Output Shape

Dataset# files

total len(36, T , 6)(36, T , 32)(18, T , 32)(18, T , 32)(9, T , 32)(9, T , 32)(1, T , 64)(T , 64)(T , 128)(T , 128)(T , 128)(T , 128)(T , 3)max lenper piece

Ballroom [17, 18]
GTZAN [29, 30]
Hainsworth [31]
RWC classic [19]RWC genre [32]
RWC jazz [19]RWC pop [19]RWC royalty [19]Songle [21] ∗SMC [20] ∗

694
1000
222
50
100
50
100
15
228
217

30 s
30 s
60 s
360 s
360 s
360 s
360 s
180 s
240 s
40 s

5 h 47 m
8 h 20 m
3 h 16 m
4 h 12 m
6 h 31 m
3 h 34 m
6 h 38 m
32 m
13 h 47m
2 h 25 m(a) The architecture of CRNN models. T is the number of frame
in a sequence; and cnX indicates the parameters of convolutional
layers with a context range of X frames, as explained in Table 1b.

cn9

cn7

cn5

cn3

conv2d 1
conv2d 2
conv2d 3(32, 7x9) (32, 7x7) (32, 5x5) (32, 3x3)
(32, 5x9) (32, 5x7) (32, 5x5) (32, 3x3)
(32, 5x9) (32, 5x7) (32, 5x5) (32, 3x3)

para num

396,643

373,475

348,387

312,547(b) Parameters of convolutional layers with different context
ranges. ‘para num’ indicates the total number of trainable parameters in each model.
Table 1: The architecture of CRNN models.

Table 2: A list of datasets. Datasets marked with ∗ are heldout datasets for testing only.

sic, jazz and so on. There are two held-out datasets fortesting only, as marked with ∗ in Table 2. One is an inhouse dataset (the Songle dataset), with 228 songs registered on [21] and with the annotations manually checked.
The other is the SMC dataset, with only beat annotations
available, which is considered as a difficult dataset for beattracking.
For training, all pieces are segmented into 30 seconds,
with a 50% overlap for pieces longer than 30 seconds. For
testing either in 8-fold cross-validation or the testing-only
datasets, the whole pieces are processed to obtain beats anddownbeats.
3.2 Results

and stochastic gradient descent) in the preliminary test,
and choose the RMSprop with a learning rate of 10−3 tominimise the cross entropy error. We stop training if noimprovement is found on the validation set in 15 epochs.
Then, we fine-tune the models by using the RMSprop witha learning rate of 10−4 . The fine-tuning is also stopped
with a patient number of 15 epochs.
2.4 Post-Processing
We adapt the post-processing method in [1]. First a threshold of 0.05 is applied on the beat/downbeat activations(from the CRNN models) to delete small activations at the
beginning and end of a music piece. Then, a Dynamic
Bayesian Network is used to infer the metre, tempo andbeat phases jointly based on the observation distributions
converted from the beat/downbeat activations. Readers arereferred to [1, 27] for more details. In the experiment, we
restrict the bar lengths to 2, 3, or 4 beats.
3. EVALUATION
We evaluate beat and downbeat tracking results in Fmeasures, computed by [28]. A beat/downbeat is considered correct if it falls into a tolerance window of ±70 ms
from the annotation.
3.1 Datasets
We list the datasets used in the experiment in Table 2. The
datasets cover a variety of music genres, such as pop, clas-

We show beat and downbeat tracking results of different
datasets in Table 3. The results are obtained by the proposed CRNN models with different context ranges, andcompared to previous state-of-the-art methods on several
datasets.
3.2.1 A comparison to state-of-the-art
We compare the proposed models to a previous state-ofthe-art model [1], which applies RNNs for a joint beatand downbeat tracking model. Among the results on thetesting-only datasets (as shown in Table 3a), the proposedmodels achieve best results on the Songle dataset at a context range of 7 frames, with 0.918 and 0.849 for beat anddownbeat F-measures, respectively. This downbeat tracking result is slightly better than that of [1]. All proposedmodels work better than [1] on the SMC dataset. The best
beat F-measure of 0.531 is achieved at a context range of
9 frames, better than the result in [1] by 1.5 percentage
points. The testing-only results of the proposed models arecompetitive to the cross validation result of 0.529 in [10].
From the results obtained with 8-fold cross validation (asshown in Table 3b), we find that the proposed models obtain better results on the Ballroom dataset with all context
ranges, with mean F-measures of 0.949 and 0.875 for beatand downbeat tracking, respectively. Both mean results are
better than the reported results in [1] by above 1 percentage point. On the RWC pop dataset, the proposed models
also outperform [1] on downbeat tracking, with a mean Fmeasure of 0.882, better than the result in [1] by 2.1 percentage points. However, the beat tracking F-measures of

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July 25th-31st, 2021, Santiago, Chile

Songle
Fb
Fd

SMC
Fb

cn9
cn7
cn5
cn3

91.3
91.8
91.4
91.3

83.8
84.9
84.8
84.3

53.1
52.7
52.6
52.5

[1]
[10]

91.8

84.3

51.6
52.9 †(a) Results on testing-only datasets. † denotes results obtainedwith 8-fold cross validation.
Ballroom

GTZAN

Hainsworth

RWCpop

Fb

Fb

Fb

Fb

Fd

Fd

Fd

88 67.6
88.5 69.2
88.3 68.3
88.3 67.3

84.4 61
84.3 61.1
83.9 61.8
83.4 58.4

mean 94.9 87.5

88.3 68.1

84

60.6

93.3 88.2[1]

85.6∗ 64∗

86.7 68.4

94.3 86.1

cn9
cn7
cn5
cn3

95 87.1
94.9 87.4
94.7 88.2
95 87.2

Fd

93.8 86.3

93 88.3
92.9 88.6
93.5 88.8
93.7 87(b) Cross validation results on 4 datasets. ∗ denotes testing-onlyresults.

cn9
cn7
cn5
cn3

Mean len
Fb
Fd

Mean num
Fb
Fd

85.9
85.5
85.4
85.7

89.2
89.3
89.1
89.2

72.4
72.8
72.8
72.1

73.8
74.6
74.5
73.4

(c) Mean cross validation results of 8 datasets. ‘Mean len’ and‘Mean num’ denote weighted average results, weighted by the total length of songs in the dataset and the number of songs in thedataset, respectively.
Table 3: Results (in percentage) on different datasets.

Fband Fd denote the F-measures of beat and downbeat tracking, respectively. cnX represents the CRNN model with thecontext range of X frames.
the proposed models are worse than the F-measure in [1](0.943) by around 1 percentage point. On the Hainsworth
dataset, the F-measures in [1] (0.867 and 0.684) are better
than those of the proposed models, exceeding the mean Fmeasures by 2.7 and 7.8 percentages on beat and downbeat
tracking, respectively. On the GTZAN dataset, the proposed models achieve mean beat and downbeat F-measuresof 0.883 and 0.681, respectively; while the corresponding
F-measures in [1] (testing-only results) are 0.856 and 0.64
respectively.
3.2.2 Effect of the context range
In this subsection, we focus on the change of performancealong with the change of the context range. The results ofdifferent context ranges are very similar to each other when
we look at the mean cross validation results (as shown in
Table 3c), and only the mean downbeat tracking results
weighted by the song number of the dataset shows relatively better results by using context range of 7 and 5

frames.
For a more detailed analysis, we show cross validation
results on individual datasets in Figure 3. We find that thecontext range has bigger influence on downbeat tracking(Figure 3b) than beat tracking (Figure 3a) except for the
RWC classic dataset. The performance variance of eachdataset is related to the number of songs in the dataset.
For example, the RWC royalty (15 songs), RWC classic(50 songs) and RWC jazz (50 songs) datasets have larger
performance variance, while the Ballrrom (694 songs) and
GTZAN (1000 songs) datasets have much smaller performance variance. This is because the overall result of adataset is more likely to be influenced by results of individual songs when the song number is small.
Despite the influence of the song numbers of the datasets,
we still can find that for more difficult datasets (theHainsworth, RWC classic and SMC datasets), it tends tohave better beat tracking results with larger context ranges.
With regarding to downbeat tracking results in Figure 3b
and Table 3a, the Ballroom, Hainsworth, RWC genre,
RWC pop datasets have the best results with a context
range of 5 frames. The GTZAN, RWC royalty and Songle
datasets have the best results with a context range of 7
frames. The RWC classic dataset achieves the best result at a context range of 9 frames, while the RWC jazz
dataset achieves the best result at a context range of 3frames. Despite the best downbeat performance achievedat the context range of 5 frames, we find on the RWC pop
and Hainsworth datasets, the downbeat tracking results issignificant improved with larger context ranges (cn5, cn7
and cn9) in comparison to the results with cn3.
From above results, we find that increasing the context
range (increasing the parameter number) of the CRNN
model doesn’t guarantee to increase the beat and downbeat
tracking performance. We believe this is because when thedata already show clear beat clues, such as the RWC pop
music shown in Figure 1a, a small context range is enoughto process the information, and a large context range (moreparameters) can bring a risk of overfitting. But for moredifficult datasets (the RWC classic and SMC datasets), theresults indicate increasing the context range can help detectbeats and downbeats.
We take an example from the RWC classic dataset to illustrate the effect brought by increasing the context range.
In Figure 4, we show the outputs of separated layers of
two CRNN models with context ranges of 3 frames (on theleft) and 9 frames (on the right) of a classic music clip.
For the top sub-figures, we can find that the timing of theonsets or other spectral events is more recognisable on theright side (with a large context range) than on the left side(with a small context range). With a clearer CNN output
for the right model, the 2nd RNN layer has already started
to recognise the tactus. Then, the 3rd and 4th RNN layers work on detecting the beats and downbeats. In contrast, for the left model, the tactus only starts to become
clear in the 4th RNN layer. Hence, based on the output of
the left model, double-tempo beats are detected, while in
the right model correct beats and better downbeats are detected. The example indicates that using a larger context
range in the CNN filters helps to detect the blurring onsets
in classic music (as shown Figure 1b), which improves beatand downbeat tracking eventually.

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Proceedings of the ICMC

July 25th-31st, 2021, Santiago, Chile(a) Beat tracking F-measures.

(b) Downbeat tracking F-measures.
Figure 3: Cross validation results (in percentage) on individual datasets. cnX represents the CRNN model with the context

range of X frames. Note that the y-axis ranges of sub-figures are different in order to provide a clear illustration.
4. CONCLUSION(a) Output of the reshape layer.

(b) Ouputs of 4 RNN layers.

(c) Output of the dense layer.
Figure 4: Outputs of separated layers of two CRNN models

on a music clip from the RWC classic dataset. Left andright denote the outputs of the models with context range of3 and 9 frames, respectively. In the bottom sub-figures, •and N denote beat and downbeat annotations, respectively.
Blue (up) and orange (down) lines denote estimated beatand downbeat activations, respectively.

In this paper we apply CRNN models for joint beat anddownbeat tracking and compare four models with different
convolution filter sizes (covering context ranges of 3, 5, 7
and 9 frames, respectively). We analyse the results of the
proposed CRNNs models on ten datasets. In the testingonly datasets, the proposed models outperform a previous
state-of-the-art method [1] with a context range of 7 frameson the in-house Songle dataset, and with all context ranges
on the SMC dataset. In general, applying different context ranges has a bigger influence on downbeat tracking
results than on beat tracking results (in all datasets except
for the RWC classic dataset). We observe clear improvements in downbeat tracking performance on the RWC pop
and Hainsworth datasets by using larger context ranges (of5, 7 and 9 frames) instead of a context range of 3 frames.
A large context range can improve beat and downbeat performance for difficult datasets (the RWC classic and SMC
datasets), but is not optimal for pop and dance music. We
could set the default context range to 7 frames for future
unseen data for a balance between music in various styles.
We find that in many cases the results obtained with different context ranges are similar to each other. In our analysis, we try to draw conclusion only based on the results
where performance differences are clear (larger than 1 percentage) to distinguish from each other, and avoid the influence of the song numbers of the datasets. In order toreflect the context ranges more clearly, we plan to design
and test other types of network architectures in the future.
Acknowledgments
This work was supported in part by JST ACCEL Grant
Number JPMJAC1602, Japan.
5. REFERENCES[1] S. Böck, F. Krebs, and G. Widmer, “Joint Beat andDownbeat Tracking with Recurrent Neural Networks,”
in Proc. ISMIR, 2016.
[2] M. Goto and Y. Muraoka, “Real-Time Beat Tracking

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Proceedings of the ICMC
for Drumless Audio Signals: Chord Change Detection for Musical Decisions,” Speech Communication,
vol. 27, pp. 311–335, 1999.
[3] M. Müller, Fundamentals of Music Processing Audio,
Analysis, Algorithms, Applications. Springer, 2015,ch. 6 Tempo and Beat Tracking.
[4] E. Nakamura, E. Benetos, K. Yoshii, and S. Dixon,“Towards Complete Polyphonic Music Transcription:
Integrating Multi-Pitch Detection and Rhythm Quantization,” in Proc. IEEE ICASSP, 2018, pp. 101–105.
[5] M. Mauch and S. Dixon, “Simultaneous Estimationof Chords and Musical Context From Audio,” IEEE
Transactions on Audio, Speech and Language Processing, vol. 18, no. 6, pp. 1280–1289, 2010.
[6] M. McVicar, R. Santos-Rodriguez, Y. Ni, and T. D.
Bie, “Automatic Chord Estimation from Audio: A Review of the State of the Art,” IEEE/ACM Transactions
on Audio, Speech and Language Processing, vol. 22,
no. 2, pp. 556–575, 2014.
[7] M. Levy and M. B. Sandler, “Structural Segmentationof Musical Audio by Constrained Clustering,” IEEE
Transactions on Audio, Speech and Language Processing, vol. 16, no. 2, pp. 318–326, 2008.
[8] J. Paulus, M. Müller, and A. Klapuri, “State of the
Art Report: Audio-Based Music Structure Analysis,”in Proc. ISMIR, 2010.
[9] S. Böck and M. Schedl, “Enhanced Beat Tracking
with Context-Aware Neural Networks,” in Proc. DAFx,
2011.
[10] S. Böck, F. Krebs, and G. Widmer, “A Multi-Model
Approach to Beat Tracking Considering Heterogeneous Music Styles,” in Proc. ISMIR, 2014.
[11] ——, “Accurate Tempo Estimation Based on Recurrent Neural Networks and Resonating Comb Filters,”
in Proc. ISMIR, 2015, pp. 625–631.
[12] F. Krebs, S. Böck, and G. Widmer, “Downbeat Tracking Using Beat-Synchronous Features and Recurrent
Networks,” in Proc. ISMIR, 2016.
[13] S. Durand, J. P. Bello, B. David, and G. Richard, “Robust Downbeat Tracking Using an Ensemble of Convolutional Networks,” IEEE/ACM Transactions on Audio
Speech and Language Processing, vol. 25, no. 1, pp.
72–85, 2017.
[14] M. Fuentes, B. Mcfee, H. C. Crayencour, S. Essid, andJ. P. Bello, “Analysis of Common Design Choices in
Deep Learning Systems for Downbeat Tracking,” inProc. ISMIR, 2018.
[15] R. Vogl, M. Dorfer, G. Widmer, and P. Knees, “DrumTranscription via Joint Beat and Drum Modeling Using
Convolutional Recurrent Neural Networks,” in Proc.
ISMIR, 2017.
[16] M. Cartwright and J. P. Bello, “Increasing Drum Transcription Vocabulary Using Data Synthesis,” in Proc.
DAFx, 2018.

July 25th-31st, 2021, Santiago, Chile[17] F. Gouyon, A. Klapuri, S. Dixon, M. Alonso, G. Tzanetakis, C. Uhle, and P. Cano, “An Experimental Comparison of Audio Tempo Induction Algorithms,” IEEE
Transactions on Audio, Speech and Language Processing, vol. 14, no. 5, pp. 1832–1844, 2006.
[18] F. Krebs, S. Böck, and G. Widmer, “Rhythmic Pattern
Modeling for Beat and Downbeat Tracking in MusicalAudio,” in Proc. ISMIR, 2013, pp. 227–232.
[19] M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka,“RWC Music Database: Popular, Classical, and Jazz
Music Databases,” in Proc. ISMIR, 2002, pp. 287–288.
[20] A. Holzapfel, M. E. P. Davies, J. R. Zapata, J. a. L.
Oliveira, and F. Gouyon, “Selective Sampling for Beat
Tracking Evaluation,” IEEE Transactions on Audio,
Speech and Language Processing, vol. 20, no. 9, pp.
2539–2548, 2012.
[21] M. Goto, K. Yoshii, H. Fujihara, M. Mauch, andT. Nakano, “Songle: A Web Service for Active Music
Listening Improved by User Contributions,” in Proc.
ISMIR, 2011, pp. 311–316.
[22] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen,
C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devinet al., “TensorFlow: Large-Scale Machine Learning on
Heterogeneous Systems,” 2015. [Online]. Available:
http://tensorflow.org/[23] F. Chollet et al., “Keras,” https://keras.io, 2015.
[24] T. Cheng, S. Fukayama, and M. Goto, “Convolving
Gaussian Kernels for RNN-based Beat Tracking,” inProc. EUSIPCO, 2018, pp. 1919–1923.
[25] T. Tieleman and G. Hinton, “Lecture 6.5-RMSprop:
Divide the Gradient by a Running Average of its Recent Magnitude,” pp. 26–31, 2012.
[26] D. P. Kingma and J. Ba, “Adam: A Method
for Stochastic Optimization,” arXiv preprint
arXiv:1412.6980, 2014.
[27] F. Krebs, S. Böck, and G. Widmer, “An Efficient StateSpace Model for Joint Tempo and Meter Tracking,” inProc. ISMIR, 2015, pp. 72–78.
[28] C. Raffel, B. McFee, E. J. Humphrey, J. Salamon,
O. Nieto, D. Liang, and D. P. W. Ellis, “mir eval: A
Transparent Implementation of Common MIR Metrics,” in Proc. ISMIR, 2014.
[29] G. Tzanetakis and P. Cook, “Musical Genre Classification of Audio Signals,” IEEE Transactions on Speech
and Audio Processing, vol. 10, no. 5, 2002.
[30] U. Marchand and G. Peeters, “Swing Ratio Estimation,” in Proc. DAFx, 2015.
[31] S. W. Hainsworth and M. D. Macleod, “Particle Filtering Applied to Musical Tempo Tracking,” EURASIP
Journal on Applied Signal Processing, vol. 15, 2004.
[32] M. Goto, H. Hashiguchi, T. Nishimura, and R. Oka,“RWC Music Database: Music Genre Database andMusical Instrument Sound Database,” in Proc. ISMIR,
2003, pp. 229–230.

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