INTERNATIONAL AUDIO LABORATORIES ERLANGEN
A joint institution of Fraunhofer IIS and Universität Erlangen-Nürnberg
Tempo Distributions
tures, and then compare our results with those from other
methods. Finally, in Section 5, we present our conclusions.
2. TEMPO ESTIMATION
To lay the groundwork for our error correction method, we
first describe a simple tempo estimation algorithm, then in-
troduce several test datasets and discuss common pitfalls.
In Section 2.5, we introduce performance metrics and de-
scribe observed errors.
2.1 Algorithm
To estimate the dominant pulse we follow the approach
taken in [24], which is similar to [23, 28]: We first con-
vert the signal to mono and downsample to 11025 Hz.
Then we compute the power spectrum Y of 93 ms win-
dows with half overlap, by applying a Hamming win-
dow and performing an STFT. The power for each bin
k 2 [0 : K] := {0, 1, 2, . . . , K} at time m 2 [0 : M] :=
{0, 1, 2, . . . , M} is given by Y (m, k), its positive logarith-
mic power Yln
(m, k) := ln (1000 · Y (m, k) + 1), and its
frequency by F(k) given in Hz. We define the onset signal
strength OSS(m) as the sum of the bandwise differences
between the logarithmic powers Yln
(m, k) and Yln
(m
1, k) for those k where the frequency F(k) 2 [30, 720]
0
10
20
% of tr
0
10
20
30
% of tracks
ISMIR2004 Songs
µ = 89.80, = 27.83
N = 464
0
10
20
30
% of tracks
GTZAN
µ = 94.55, = 24.39
N = 999
0
10
20
30
% of tracks
ACM MIRUM
µ = 102.72, = 32.58
N = 1410
0
10
20
30
% of tracks
Hainsworth
µ = 113.30, = 28.78
N = 222
20
30
acks
Ballroom
µ = 129.77, = 39.61
mic power Yln
(m, k) := ln (1000 · Y (m, k) + 1), and its
frequency by F(k) given in Hz. We define the onset signal
strength OSS(m) as the sum of the bandwise differences
between the logarithmic powers Yln
(m, k) and Yln
(m
1, k) for those k where the frequency F(k) 2 [30, 720]
and Y (m, k) is greater than ↵Y (m 1, k) (see [16]):
I(m, k) =
8
<
:
1 if Y (m, k) > ↵Y (m 1, k)
and F(k) 2 [30, 720],
0 otherwise
(1)
OSS(m) =
X
k
(Yln
(m, k) Yln
(m 1, k)) · I(m, k)
Both the factor ↵ = 1.76 and the frequency range were
found experimentally [24].
The OSS(m) is transformed using a DFT with length
8192. At the given sample rate, this ensures a resolution
of 0.156 BPM. The peaks of the resulting beat spectrum
B represent the strength of BPM values in the signal [7],
but do not take harmonics into account [10, 21]. There-
fore we derive an enhanced beat spectrum BE
that boosts
frequencies supported by harmonics:
BE
(k) =
2
X
|B(bk/2i + 0.5c)| (2)
0
10
20
% of tr
N = 222
0
10
20
30
% of tracks
Ballroom
µ = 129.77, = 39.61
N = 698
20 – 30
30 – 40
40 – 50
50 – 60
60 – 70
70 – 80
80 – 90
90 – 100
100 – 110
110 – 120
120 – 130
130 – 140
140 – 150
150 – 160
160 – 170
170 – 180
180 – 190
190 – 200
200 – 210
210 – 220
220 – 230
230 – 240
240 – 250
250 – 260
0
10
20
30
Tempo intervals in BPM
% of tracks
GiantSteps µ = 136.66, = 28.33
N = 664
Figure 1. Tempo distributions for the test datasets.
highest value of BE
, divide its frequency by 4 to find the
first harmonic, and finally convert its associated frequency
to BPM:
60
40 – 50
50 – 60
60 – 70
70 – 80
80 – 90
90 – 100
100 – 110
110 – 120
120 – 130
130 – 140
140 – 150
150 – 160
160 – 170
170 – 180
180 – 190
190 – 200
200 – 210
210 – 220
0
10
20
30
Tempo intervals in BPM
% of tracks
µ = 121.32, = 30.52
N = 8, 596
Figure 1: Tempo distribution for the Train dataset con-
sisting of LMD Tempo, MTG Tempo, and EBall.
sisting of multiple components (“layers”) that has evolved
naturally. But to the best of our knowledge, nobody has
replaced the traditional multi-component architecture with
a single deep neural network (DNN) yet. In this paper we
describe a CNN-based approach that estimates the local
end, we estimated the tempo of the matched audio pre-
views using the algorithm from [31]. Then the associated
MIDI files were parsed for tempo change messages. If the
value of more than half the tempo messages for a given
preview were within 2% of the estimated tempo, we as-
sumed the estimated tempo of the audio excerpts to be cor-
rect and added it to LMD Tempo. This resulted in 3,611
audio tracks. We were able to match more than 76% of the
tracks to the Million Song Dataset (MSD) genre annota-
tions from [29]. Of the matched tracks 29% were labeled
rock, 27% pop, 5% r&b, 5% dance, 5% country,
4% latin, and 3% electronic. Less than 2% of the
tracks were labeled jazz, soundtrack, world and
others. Thus it is fair to characterize LMD Tempo as a
good cross-section of popular music.
2.2 MTG Tempo
The MTG Key dataset was created by Faraldo [8] as a
Proceedings of the 19th ISMIR Conference, Paris, France, September 23-27, 2018 99
Diverse
μ=121.3 σ=30.5 μ=94.6 σ=24.4
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