Acoustic Identification of Flat Spots On Wheels Using Different Machine Learning Techniques [original]

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Dernbach, Gabriel; Lykartsis, Athanasios; Sievers, Leon; Weinzierl, Stefan (2020): Acoustic Identification
of Flat Spots On Wheels Using Different Machine Learning Techniques. In: Fortschritte der Akustik - DAGA
2020: 46. Deutsche Jahrestagung für Akustik. Berlin: Deutsche Gesellschaft für Akustik e.V. pp. 367–370.
Gabriel Dernbach, Athanasios Lykartsis, Leon Sievers, Stefan Weinzierl
Acoustic Identification of Flat Spots On
Wheels Using Different Machine Learning
Techniques
Published version Conference paper |

Acoustic Iden ti ﬁ cation of Flat Sp ots On Wheels Using
Di ﬀ eren t Mac hine Learning T ec hniques
Gabriel Dern bac h 1 , A thanasio s Lyk artsis 1 , Leon Siev ers 2 , Stefan W einzierl 1
1 TU Berlin, F achgebiet A udiokommunikation, 1058 7 Berlin, Deutschland
2 R ail watch GmbH, 53177 Bonn, Deutschland
stefan.weinzierl@tu-b erlin.de
In tro duction
The con tin uous, non-in v asive monitoring of mac hines and
mac hine-related services can help ensure trouble-fr ee op-
eration and reliev e the provider of unnecessary , manual
routine con trols. Audio recordings in particular require
no further mo di ﬁ cations of the devices or installations
to b e monitored and are thus esp ecially con v enient to
acquire. W e consider the case of the acoustic detection of
ﬂ at sp ots, a sign of wear on the wheels of rail v ehicles.
The task can b e considered as a sp ecial case of acous-
tic scene classi ﬁ cation, assigning one of several acoustic
ev en t categories to a giv en audio recording. Acoustic
scene classi ﬁ cation has traditionally b een addressed b y
extraction of hand-crafted audio features and forwarded
to a general classi ﬁ cation algorithm such as a support
v ector mac hine (SVM) [
1
]. Other classical approac hes are
based on the sligh tly more general mel-frequen cy cepstral
co e ﬃ cients (MF CCs) com bined with a clustering metho d
(e.g. Gaussian mixture mo dels) to facilitate multi class
classi ﬁ cation [
2
]. Recen t progress in the ﬁ eld has b een
stim ulated b y public data sets and op en contests, suc h as
the widely ac knowledged DCASE c hallenge [
3
]. Ov er the
past y ears con volutional neural net w orks in conjunction
with log-mel-sp ectr ograms ha v e prov en to b e promising
building blo cks in addressing acoustic recognition tasks
[4].
W e ha v e adapted these metho ds to the sp eci ﬁ c require-
men ts of the acoustic detection of ﬂ at sp ots. This damage
to the shap e of railroad wheels can b e caused b y slip and
slide conditions that causes wheels to lo c k up while the
train is still mo ving, b y fault y brakes or wheelset b earings.
It is noticeable acoustically through p erio dic kno c king
noises, the frequency of whic h is determined b oth b y the
sp eed of the train and the diameter of the wheels.
W e ha v e compared di ﬀ er en t feature representation suc h
as ra w audio data, MF CCs and log-mel-sp ectrograms,
as w ell as di ﬀ erent classi ﬁ ers, from a SVM classi ﬁ er, a
standard con v olutional net w ork arc hitecture (CNN) to
enco der-deco der segmen tation netw orks (U-Net). W e
ha v e further identi ﬁ ed desirable feature in v ariances and
implemen t the corresp onding acoustic transformati ons.
Our ﬁ ndings suggest that the task is facilitated b y resam-
pling the audio to a virtually constan t ﬂ at sp ot b eating
frequency . F urthermore, conv olutional enco der-deco der
arc hitectures employing sp ectrogram represen tations out-
p erform other metho ds with comparable num b er of pa-
rameters.
Dataset
The data set w as provided b y Railw atc h Gm bH, a com-
pan y resp onsible for monitoring and rep orting fault y train
w agons. The data has b een recorded at three di ﬀ erent
sites in close pro ximity to the rail trac ks and displa ys
minor v ariations in recording dist ance and large v ariations
in am bien t noise. Each recording con tains the sound of
one full train passing b y the recording sp ot. The dura-
tion of a recording v aries from 20 seconds up to sev eral
min utes, dep ending on the train sp eed and the n um b er of
w agons it carries. Each individual sample consists of the
ra w audio ﬁ le, as well as measuremen ts of the train sp eed
and the radii of individual wheels at their corres p onding
timestamps. Estimation of sp eed and wheel radii w ere
based on video recordings and w ere pro vided with the
dataset. The resp ective labels hav e b een annotated b y
exp erts as they listened to the recordings, indicating a
ﬂ at sp ot by marking the correspondin g region of time.
The data set con tains 566 train pass ings, summing to a
join t duration of 7 . 9 hours (see ﬁ gure 1).
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Figure 1:
Statistics of the audio data set, indicating the
duration of the train passings, the duration of the annotated
ﬂ at sp ots, and the sp eed of the passing trains
279 train passings exhibit at least one ﬂ at sp ot annota-
tion. A total of 765 ﬂ at sp ot regions ha ve been marked,
summing to a join t duration of 16
.
2 min utes. W e note the
pronounced im balance of marked to unmark ed regions of
DAGA 2020 Hannover
367

3
.
5%. Most of the mark ed regions are of short duration,
t ypically b et w een 0
.
5 to 2 seconds. W e observ e a wide
v ariability of the speeds p er train passing ( ﬁ gure 1) and
note that the p erceptual quali t y of a ﬂ at sp ot also v aries
substan tially with sp eed. F or lo w sp eeds, the ”b eatin g”
sound is clearly noticeable as separate, coun table hits .
F or very high speeds the b eating resem bles an amplitude
mo dulation of a wide-band tur bulen t noise. In terms
of lab el precision we note that the ﬂ at spot lab els ha ve
considerable v ariations in how m uc h en vironmen tal sound
w as included b efore and after the actual sound of the ﬂ at
sp ot.
Represen tation
W e consider the three most common represen tations in
audio ev en t detec tion analysis comm unity , namely raw
audio, log-mel sp ectr ograms and MF CCs. These show an
increasing degree of compression, and therefore increasing
asso ciated inductive bias. All extraction w as based on the
original audio with a 48000 Hz sampling rate, b eing sub-
sampled to 8192 Hz and cut in to non o verlapping frames
of 2 or 5 seconds. F or the ra w audio data, no further
pro cessing is applied. F or the log mel sp ectrograms, w e
apply a short-time F ourier trans form (STFT) with a win-
do w size of 512 and a hop size of 128 samples, follo w ed b y
a mel ﬁ lterbank with 40 ﬁ lters. Finally eac h element is
b eing log-compressed with a factor of 7. F or the MF CCs
w e extracted the ﬁ rst 13 co e ﬃ cien ts.
During feature extraction, we also include the train sp eed
estimations pro vided. By standardization to a virtual
iden tical sp eed w e aim to diminish the v ariance in tro duced
b y the train sp eed to our input representation, so that the
detection task b ecomes more homogeneous and th us easier
to solv e. Normalization to a common virtual pass-by
sp eed
s c
can b e p erformed b y scaling the audio playbac k
rate, whic h is essentially an audio resampling op eration.
F or the sp eed
s i
of a train
i
, the scaling factor
t i
is then
giv en b y:
t i = s c
s i
(1)
In order to k eep the amoun t of resampling small w e c ho ose
s c
to b e the median ov er all
s i
. T o furthe r re ﬁ ne the
normalization with the information ab out the sp eed of
the train
s i
and the radius of the wheel
r i
, w e can compute
the exp ected freq uency of the b eatin g and standardize the
individual pla yback speeds to a common b eating frequ ency
b c , using a scaling factor b i given b y
b i = b c
2 π r i
s i
(2)
Mo dels
W e considered three mac hine learn ing mo dels: First,
w e applied
supp ort v ector mac hines with Gaussian
k ernel
, a standard metho d well do cumen ted for acoustic
scene classi ﬁ cation and pro viding a basel ine for small to
medium-sized data sets.
Secondly , we used a classical
con v olutional neural net-
w ork
[
5
]. F or the log mel sp ectrogram as an input to the
algorithm, w e take ﬁ v e con v olutional (2D) and t wo fully
connected blo cks applying batc h normalization through-
out. When working with ra w audio w e use eigh t con vo-
lutional (1D) in verted residual blo c ks [
6
] with squeeze
excitation, follow ed b y one fully connected la y er. Eac h
con v olution blo c k ends with a p o oling op eration of k ernel
size three and stride three as presented in SampleCNN [
7
].
The c hoice of the in verted residual blocks w as based on
memory considerations, as w e an ticip ated the use of the
net w ork as the enco der bac k end to the following U-Net
arc hitecture. Finally , we emplo y ed a
U-Net lik e con-
v olutional net w ork
[
8
], i.e., a enco der deco der netw ork
of con v olutional blo c ks with additional skip connect ions
b etw een enco der to deco der la yers of matc hing sizes. F or
the mel-sp ectrogr am represen tation w e to ok the original
2D design and trimmed its n umber of ﬁ lters and depth .
The enco der part of the net work is then iden tical to the
feature extractor of the mel-CNN. The base U-Net out-
puts a 2D segmen tation mask, w e there fore app ended
a con v olutional lay er of 1D o v er frequencies and then
a v erage p o ol. The mo del thus outputs a 1D segmen ta-
tion mask corresp onding to the ﬂ at sp ot regions to b e
predicted. F or the ra w audio representation w e emplo y ed
the SampleCNN as an enco der and built the corresp ond-
ing mirrored deco der simi lar to the mel- ﬁ ltered v ariant.
In literature, the arc hitecture closes t to ours is found in
Stoller et al. [9].
Mo del regularization is achiev ed b y w eigh t decay and
drop out as w ell as data augmen tations. In particular
w e apply mixup augmentation [
10
], whic h is p erformed
b y taking a w eighted sum of t w o randomly selected data
p oints as
˜ x = λ x i +( 1 − λ ) x j
˜ y = λ y i +( 1 − λ ) y j
where
x i
are the features of item
i
,
y i
its resp ective tar-
gets and
λ ∼ Beta
(
α , α
) the random v ariable describ-
ing the distribution o ver w eigh ting factors. W e choose
α ∈
[0
.
1
,
0
.
4], as most augmen tations are then only sligh t
p erturbation s of the original samples and 50
/
50 o v erlaps
are esp ecially rare. W e also consider random shift s in
pitc h [
11
] but this did not lead to considerable p erfor-
mance gains, as was the case with random addition of
lo w v ariance Gaussi an noise. 1 F or the mel sp ectrogram
represen tation we additionally apply random cut outs of
the mel axis (2 %) as w ell as the time axis (10 %) [12].
All mo dels w ere train ed on a cross en trop y loss. A frame
had to rep orted to contain a ﬂ at spot when at least 5 %
of it’s con ten t o verlaps with an annotated ﬂ at sp ot region.
F or tw o second frames this corresp onds to at least 100 ms
of ﬂ at sp ot annotation, whereas for ﬁ v e second frames
at least 250 ms m ust b e annotat ed. 250 ms is also the
1
The signals at hand are rarely of clear pitc hed harmonic conten t
but more of sto chastic and percussive nature. Pitc h shift augmen-
tation, although highly regarded in other domains is of limited use
here, as tested exp erimentally .
DAGA 2020 Hannover
368

shortest duration of ﬂ at sp ot annotat ions and therefore
is the hard limit whic h should not b e exceeded. F or the
segmen tation mo dels, we add the criterion of a per sample
classi ﬁ cation to supp ort the creation of accurate ﬂ at sp ot
lo cation masks.
W e rep ort the F1 score, i.e. , the geometric mean of pre-
cision and recall, for classi ﬁ cation p erformance, since
classi ﬁ cation accuracy is less insigh tful for sett ings of high
class im balance.
Results
The data set w as set up for three fold cross v alidation with
splits retaining grouping of individual train passings. That
is, frames extracted from one passing are only allo w ed to
app ear in one and only one of train, develop or test set.
T raini ng h yp er-param eters suc h as learning rate, weigh t
deca y and drop out w ere determined during a pre run of
randomized searc h. The training data set w as balanced
b y o versampling the minorit y class.

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Figure 2: SVM mo del with MFCCs as input.

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Figure 3: CNN mo del with mel-sp ectrogram as input.
The p erformance of the basel ine SVM can b e seen in
Fig. 2. Its b est score of
F
1 = 0
.
50 w as ac hieved with
a b eat frequency standardization on frames of ﬁ v e sec-
onds. The b est results in total w ere achiev ed with mo dels
based on the log mel sp ectrogram rep orted in Figs. 3

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Figure 4: U-Net mo del with mel-sp ectrogram as input.
tp fp fn tn f1 cv split ﬁ lters
90 23 37 616 0.75 0 [16, 32, 64]
91 23 30 662 0.77 1 [16, 32, 64]
116 36 31 603 0.78 2 [16, 32, 64]
T able 1:
P erformance metrics of mel sp ectrogram unet
trained with b eat frequency norma lization on ﬁ v e seconds
frames. The mo del size amounts to only 72k parameters
and 4. F or the mo dels learne d on ra w audio w e had to
increase the capacit y signi ﬁ cantly to obtain comparable
results. The mo dels were scaled up to 23 M parameters,
but still remained sligh tly b ehind their mel-sp ectrogram
coun terparts of 76 K parameters, probabl y also b ecause
of missing corresp ondin g augmen tation and regulariza-
tion tec hniques, since there is no direct corresp ondence of
sp ectral cut out augmentations for ra w audio data. The
SampleCNN ac hieved its best scores of median 0.63 F1,
with a maxim um of 0.68 F1. The Sample-UNet achiev ed
it’s b est scores of median 0.73, with a maximum of 0.75
F1. While the mel-U-Net can b e easily trained on a com-
mo dity CPU found in most laptops, the sample-U-Net is
only feasible to train on mo dern GPUs.
There is a strong tendency in 5 s frames pro viding b etter
results than the 2 s frames, which migh t b e due to the
increased con text necessary for the solution of the task.
Ho w ev er, one should b e a w are of the p ossibilit y of broader
windo ws subsuming several ﬂ at spots. A case hard to
detect could then hide in a frame with a more prev alent
one that triggers classi ﬁ cation and w ould then stop to
con tribute to the error. A quick inspection of the detailed
segmen tation masks provided b y the melUnet, ho w ev er,
did not indicate suc h cases. The detailed segmen tation
mask of the melUnet is a k ey adv antage for the usage of
this arc hitecture in practice, since predicting the precise
lo cation of a ﬂ at sp ot is more v aluable than just detecting
it’s mere presence.
The normalization to a virtually constant beating fre-
quency turned out useful. With eac h com binat ion of
represen tation and netw ork it constan tly increased p er-
formance signi ﬁ can tly .
DAGA 2020 Hannover
369

W e notice a tendency for the false p ositiv es and false
negativ es to b e balanced (T ab. 1). In many applica-
tions, ho w ever, the economic cost of false p ositiv es and
false negativ es may not be symmetric. This im balance
is b est taken in to consideration b y w eigh ting the resp ec-
tiv e loss of the classes, but sta ying with the balanced
sampling strategy . This allows un tangling mo delling of
costs (re-w eigh ted loss) and pro viding prop er gradien ts
for optimization (training with balanced sampli ng).
Small c hanges in the amoun t of frequency cut o ﬀ and
log compression sho w ed no consid erable v ariations in F1
Score. Learning rate, drop out and w eigh t decay can v ary
o v er wide range for the melccn without degrading p er-
formance. The unet arc hitectures are more sensitiv e to
optimal training parameterization. Replacing the trans-
p osed conv olution in the upsampling paths of the melUnet
with a simple linear upsampling did not degrad e p erfor-
mance although b eing muc h c heap er to compute. Mo dest
increases in the width and depth of the melUnet did not
impro v e p erformance. Alread y small mo dels can ﬁ t the
training data set v ery w ell and thus are in need of strong
regularization. One migh t in terp ret this as suggesting a
necessary expansion of the training data set. Ho w ev er,
when training the mo dels on subsets of v arying size we
notice a saturation in p erform ance form usage of 70%
of the data set on w ards. The combination of su ﬃ cien t
mo del complexity to o v er ﬁ t and the lac k of impro vemen t
b y taking more data in to consid eration suggests to test for
lab el noise. There is indeed some arbitrary v ariati on to
the exact starting and stop times of the mark ed ﬂ at sp ot
regions as w ell as the di ﬃ cult p er case decision of whether
the prev alence of a wheel b eating su ﬃ ces to rep ort a ﬂ at
sp ot.
Conclusion
In this study , w e presented a system for the iden ti ﬁ cation
of wheel ﬂ at sp ots of passi ng trains based on audio record-
ings. Di ﬀ eren t prepro cessing tec hniques and machine
learning mo dels hav e b een ev aluated. W e could sho w
that con v olutional segmentation arc hitectures (U-Net)
emplo ying mel sp ectrogram represen tations outp erform
other metho ds with comparab le n um b er of parameters.
F urther our ﬁ ndings suggest that the task is facilitated
b y resampling the recording to a standardized exp ected
ﬂ at sp ot b eating frequency .
Impro v ed p erformance could b e ac hiev ed by further in-
v estmen ts in lab elling. That is, some ﬂ at sp ots are more
or less pronounced suggesting the use of soft lab els. T o
sp eed up lab elling and ﬁ nding relev ant unlab eled sections
quic kly w e suggest using hard negative mining. Also,
more elab orate data augmentation could be applied, suc h
as including and o verla ying samples with c haracteristic
en vironmen tal noises.
F or future work, w e are planning the direct prediction of
the fault y w agon axles by aligning the audio and other
metadata. A data set that con tains annotations of which
axle w as in fact defectiv e w ould side step lossy intermedi-
ate represen tations as well as the label noise of manual
annotation and could b e train ed end to end.
Gabriel Dern bach ac kno wledges partly supp ort by the
German Ministry of Education and Researc h (BMBF ) in
the pro ject ALICE I I I (01IS18049B).
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