A Neural Network Approach for Automatic Detection of Acoustic Alarms

Alex Peir

o Lilja, Ganna Raboshchuk and Climent Nadeu

TALP Research Center, Department of Signal Theory and Communications, Universitat Polit

ecnica de Catalunya,

C. Jordi Girona, 1-3, 08034, Barcelona, Spain

Keywords:

Acoustic Event Detection, Alarm Detection, Neural Network, Neonatal Intensive Care Unit.

Abstract:

Acoustic alarms generated by biomedical equipment are relevant sounds in the noisy Neonatal Intensive Care

Unit (NICU) environment both because of their high frequency of occurrence and their possible negative

effects on the neurodevelopment of preterm newborns. This work addresses the detection of speciﬁc alarms

in that difﬁcult environment by using neural network structures. Speciﬁcally, both generic and class-speciﬁc

input models are proposed. The ﬁrst one does not take advantage of any speciﬁc knowledge about alarm

classes, while the second one exploits the information about the alarm-speciﬁc frequency sub-bands. Two

types of partially connected layers were designed to deal with the input information in frequency and in

time and reduce the network complexity. The time context was also considered by performing experiments

with long short-term memory networks. The database used in this work was acquired in a real-world NICU

environment. The reported results show an improvement of more than 9% in absolute value for the generic

input model and more than 12% for the class-speciﬁc input model, when both consider time information using

the proposed partially connected layer.

1 INTRODUCTION

The acoustic environment of a Neonatal Intensive

Care Unit (NICU) is very noisy and contains a high

diversity of sounds that happen spontaneously and of-

ten overlap in time. The possibility of noxious ef-

fects of the noisy NICU environment on preterm new-

borns has been well documented and is of great con-

cern in the medical literature (Wachman and Lahav,

2010). Acoustic alarms, which are frequently gen-

erated by various biomedical equipment, are among

the most relevant types of sounds in the NICU envi-

ronment and may negatively affect the neurodevelop-

ment of the preterm infants. Besides, the excess of

alarms that do not have clinical relevance can cause

an alarm fatigue to the medical staff and affect the

quality of healthcare provided by them (Freudenthal

et al., 2013). Therefore, automatic detection of acous-

tic alarms in the NICU can be useful to: 1) study

relations between the detected alarms and the infant

health; and 2) assist the medical staff in their work.

So far, the problem of general acoustic alarm de-

tection has not been much explored. In general, it was

investigated for hearing impaired assistance or hear-

ing support in noisy conditions (Beritelli et al., 2006;

Carbonneau et al., 2013). To the best of our knowl-

edge, the ﬁrst work on automatic alarm sounds detec-

tion is reported in (Ellis, 2001), where an approach

borrowed from speech recognition and an approach

based on sinusoid modelling and separation were

compared. Later works on acoustic alarm detection

usually make use of particular properties of alarms

as: detection of amplitude periodicity in a speciﬁed

frequency bandwidth (Carbonneau et al., 2013); pitch

detection in a predeﬁned frequency range (Meucci

et al., 2008); spectral- and time-domain properties

extracted with morphological features (Xiao et al.,

2009); explotation of the long-term periodicity with

the autocorrelation function (Lutﬁ and Heo, 2012).

A system for acoustic alarm detection in a NICU

environment was ﬁrst presented in (Raboshchuk et al.,

2014), for the task of binary alarm detection; and also

an acoustic description of that environment was pro-

vided. The proposed system included a denoising

pre-processinng step, employed generic features that

cover the whole frequency bandwidth and pre-trained

neural networks. In (Raboshchuk et al., 2015), the

problem of detecting speciﬁc NICU acoustic alarm

classes was addressed. The system consisted of a

set of binary Gaussian mixture modelling based de-

tectors (one per each alarm class). The knowledge

about the spectro-temporal alarm characteristics was

exploited. First, by using sinusoid detection around

alarm-speciﬁc frequencies for feature extraction; and

PeirÃ¸s Lilja A., Raboshchuk G. and Nadeu C.

A Neural Network Approach for Automatic Detection of Acoustic Alarms.

DOI: 10.5220/0006167200840091

In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017), pages 84-91

ISBN: 978-989-758-212-7

second, by including a post-processing step that takes

into account the temporal structure of alarms.

An approach based on Neural Networks (NN) is

proposed in this paper to detect alarm sounds in a

real-world NICU hospital environment. The NNs de-

scribed in (Ellis, 2001; Beritelli et al., 2006) have a

conventional topology and use extracted features at

the input (perceptual linear predictive cepstral coefﬁ-

cients in (Ellis, 2001) and mel-scale cepstral coefﬁ-

cients in (Beritelli et al., 2006)). Conversely, in this

work, a simple magnitude spectral representation is

used at the input, but a more elaborated NN struc-

ture is explored, though, due to the small size of the

dataset, deep topologies could not be considered. Two

types of partially connected hidden layers with lim-

ited weight sharing are implemented to reduce the

number of network parameters to train. Those layers

are employed in the networks to extract local infor-

mation in either time or frequency, so we call them,

respectively, Frequency Weighting (FW) and Tempo-

ral Weighting (TW). Two different kind of models are

proposed: a Generic Input Model (GIM) and a Class-

speciﬁc Input Model (CIM). GIM does not employ

any knowledge about the particular alarm class, while

CIM takes advantage of the knowledge about alarm-

speciﬁc frequency components. Both models were

studied and compared through the presented results.

The NN-based techniques which have been devel-

oped and compared in this work operate at the frame

level. In order to evaluate the systems also in terms of

event detection, the sequence of frame-level classiﬁ-

cation decisions is processed with a simple smoothing

technique.

The rest of the paper is organized as follows. Sec-

tion 2 contains the description of the database and

of the acoustic alarms. In Section 3, the system de-

velopment is presented, including the description of

input representation, pooling techniques and partially

connected hidden layers, and the description of the

two input models proposed. Finally, in Section 4 the

evaluation setup and the experimental results are dis-

cussed.

2 DATA DESCRIPTION

The database used in this work contains audio ac-

quired during ten recording sessions in the NICU of

Hospital Sant Joan de D

eu Barcelona (108.7 minutes

in total). Two electret unidirectional microphones

connected to a linear PCM Recorder were used to

make recordings. One microphone was placed inside

the incubator, close to the infant’s ear, and the other

one outside the incubator, usually pointing to the cen-

Figure 1: Typical structure of an alarm sound.

ter of the room. But only the recordings obtained

from the microphone placed outside are used in this

work. The manual annotations cover 54.3 minutes of

this data, where 19.28% of time is labelled as alarms.

Note that each alarm signal was annotated separately

(see Figure 1).

A large diversity of sounds can be found in a typi-

cal NICU environment. In the acquired database 16

different alarm classes were observed, which were

generated by various types of biomedical equipment,

such as cardiorespiratory, monitors, ventilators, infu-

sion pumps, incubators, etc. Only 7 most populated

alarm classes, which were also relevant from the med-

ical point of view, are selected in this work.

Alarm sounds are periodic in time, and each pe-

riod is deﬁned by a signal and silence intervals of

speciﬁc durations (see Figure 1). The signal inter-

val contains one or several consecutive tones, which

are stationary. Each tone is deﬁned by one or several

simultaneous frequency components harmonically re-

lated or not. Several alarm classes show some varia-

tions in the frequency and duration values, and such

cases are referred to as different versions of the alarm

class. The speciﬁc spectro-temporal properties of the

selected alarm classes are presented in Table 1.

Most of the time, alarm sounds occur together

with other types of sounds. However, the temporal

overlaps between alarms are not rare either, and in the

annotated data two or more alarms sound simultane-

ously almost 8% of time.

3 SYSTEM DEVELOPMENT

3.1 Input Representation

The recorded audio was downsampled from 44.1 to

24 kHz, thus, the observed frequency range is up to

12 kHz. For processing, the frame length was set to

2048 points with 50% of overlap. Then, each spec-

tral frame length is 1024 points, which corresponds

to a resolution of 11.78 Hz per spectral point. In this

work, a spectrum representation rather than an ex-

tracted set of features is used directly as the input data

to let the network learn the feature representation by

A Neural Network Approach for Automatic Detection of Acoustic Alarms

Table 1: Description of the chosen alarm classes. After “alarm-classes”: comma-separated frequencies are simultaneous in

time; information separated with brackets corresponds to a different version of the alarm; frequencies separated with slash

correspond to consecutive tones.

Class Period duration (s) Frequency components (kHz) Samples Frames

2.050

[2.246]

0.495, 1.465, 2.435

[0.515, 2.455, 3.445, 4.415]

238 3935

a3 15.300

0.665, 1.330, 1.990, 2.660 / 0.540, 1.60, 3.150

[0.520 / 0.420]

130 1928

a6 0.447 2.410 203 1785

a7 1.015

0.980, 2.935

[2.880]

114 2239

a8 2.245 0.490, 1.480, 2.460, 3.440, 4.420 452 2956

a10 1.000

1.140, 2.280, 3.425

[0.880]

75 1186

a16 2.053 0.495 135 969

itself. This was inspired by recent works reported in

(Sainath et al., 2013; Abdel-Hamid et al., 2013).

3.2 Pooling Techniques for Input

Pre-processing

Several pooling techniques are implemented to reduce

the input size while preserving the relevant informa-

tion. Average Pooling (AP) technique averages the

small portion of frequency sub-band values. Then two

non-linear pooling techniques based on maximum are

proposed, where the goal is to preserve high spectral

peaks with their real values: Standard Max Pooling

(SMP) and Mel-Scale Max Pooling (MSMP). SMP

technique has the same structure as AP, but it uses

maximum rather than averaging. MSMP, on the other

hand, looks for the maximum value following the

mel-scale ﬁlter bank distribution and provides a more

natural representation according to the human ear per-

ception.

3.3 Model Structures

3.3.1 Generic Input Model

The aim of using this kind of model is to have a net-

work structure and a set of features that do not de-

pend on the alarm properties, so they have not to be

changed when the model for a new alarm has to be

trained. Figure 2(a) shows the general scheme of this

model. The input-vector is the whole spectral frame

representation (magnitude DFT of the signal frame).

First, the spectral values are pre-processed to reduce

the size of the representation of the spectrum, while

keeping the relevant information. Then, logarithm

and Mean-Variance Normalization (MVN) are used

to improve the data distribution before it is fed to the

classiﬁer, which is either a feedforward NN or a long

short-term memory NN.

3.3.2 Class-speciﬁc Input Model

For this model the particular spectral and temporal

properties of alarms are assumed known. In fact, only

the spectral information is used, since the inclusion

of the temporal information (i.e. signal and silence

interval duration) requires that many more network

weights are trained, what is not feasible. The spectral

information is exploited at the input of the network

(see Figure 2(b)), where the input features are only the

spectral values at the alarm-speciﬁc frequency bins

( f

, f

) and the bins around them: Left Neigh-

bors (LN) and Right Neighbors (RN). Logarithm and

MVN are further applied to the input data. Obviously,

neither pooling strategies nor partially connected lay-

ers for frequency weighting are used by this model.

3.4 Partially Connected Layers

3.4.1 Frequency Weighting

For model size reduction we ﬁrst propose to use par-

tial connections as a weighted average of a portion of

spectral bins, as shown in Figure 3(a), where F is the

number of input spectral bins of frame S

. As shown

in the ﬁgure, the upper layer has a size that is the quo-

cient between the input size and the pooling size.

3.4.2 Temporal Weighting

This layer exploits the frame temporal context as

shown in Figure 3(b). The central frame S

of the

context window is the one to be classiﬁed by the NN,

but the input of NN is a concatenation of all frames

BIOSIGNALS 2017 - 10th International Conference on Bio-inspired Systems and Signal Processing

(a) Generic Input Model (GIM) (b) Class-speciﬁc Input Model (CIM)

Figure 2: Proposed input models.

inside the context window. Therefore, the input vec-

tor contains information from past and future frames.

The upper layer performs a time weighted average for

each frequency bin, and so it has the same size F than

a single input frame.

4 EXPERIMENTAL

EVALUATIONS

4.1 Experimental Setup

Since the dataset is relatively small, a 10-fold cross-

validation scheme was implemented to obtain more

statistically relevant results. As the dataset consists

in 10 recording sessions, on each fold 9 sessions

were used for training and the remaining one for test-

ing. Then, the overall metric results were obtained.

For training the model for an alarm class, from each

recording session, all the alarm class frames were ex-

tracted plus the same amount of non-alarm frames to

create a balanced training set. In order to maximize

variability, those non-alarm frames were chosen ran-

domly.

The fully connected hidden layer employed in the

neural networks of the presented experiments had 8

hidden units. The activation function of the hidden

units was the sigmoid, and that of the output units

was softmax. Note that there was no pre-training.

Binary cross-entropy was used as objective function

for networks training, which seems to perform better

than mean-squared error function neither pre-training

nor a good initialization are used (Golik et al., 2013).

Stochastic gradient descend was used for the network

optimization. The learning rate and momentum pa-

rameters of the network were set to 0.01 and 0.9, re-

spectively. The number of epochs was 70 and the

mini-batch size was 10.

With both types of models the mean and the vari-

ance required for applying MVN to both training and

testing samples were obtained from the training data.

According to (Lecun et al., 1998), convergence is usu-

ally faster if the average of each input variable over

the training set is close to zero.

4.2 Evaluation Metrics

In this work, frame-level and event-level metrics are

used to evaluate the detection performance. The

Missing Rate (MR) and the False Alarm Rate (FAR)

metrics are used for the frame-level evaluations. And

these are deﬁned as

MR =

, FAR =

, (1)

where N

and N

is the number of misclassiﬁed

frames for alarm and non-alarm class, respectively,

and N

is the total number of alarm and non-

alarm frames, respectively.

The Equal Error Rate (EER) was chosen as the

decision criterion at the frame level, therefore the

reported MR and FAR metric scores have the same

value.

All the alarms are periodic sounds. The period of

the alarm was chosen for the event-level evaluation,

A Neural Network Approach for Automatic Detection of Acoustic Alarms

(a) Partially connected layer for frequency weighting (b) Partially connected layer for temporal weighting

Figure 3: Partially connected layers implemented.

since it is a natural alarm-speciﬁc unit. The Period-

Based Error Rate (PB-ERR), is deﬁned as

PB − ERR = 1 −

2 · N

+ N

, (2)

where N

is the number of correctly detected refer-

ence alarm periods, N

and N

is the number of

missed and falsely inserted periods. For calculating

this metric, a smoothing post-processing, which is

based on majority voting, is applied to the frame-level

output labels. The length of the smoothing window

was set to be the minimum of the signal and silence

interval length in the alarm period. A detailed expla-

nation of this event-level metric is presented in (Ra-

boshchuk, 2016).

4.3 Results and Discussion

The experiments were ﬁrst carried out with the most

populated alarm class from the recorded database (see

Table 1), which is class ’a8’. Then, the best network

structures for each model were replicated for the rest

of the alarm classes.

Table 2 shows the baseline results when either an

amplitude (A) or log-amplitude (LogA) spectral input

is used. The latter was introduced taking into account

that the non-linear logarithm transformation reduces

the value range, which could beneﬁt the network’s

learning. Both experiments were performed using the

whole spectral frame length (thus, 1024 input units)

fully connected with a hidden layer of 8 units.

Table 2: Baseline results.

Input

Evaluation metrics (%) Network

weightsMR=FAR PB-ERR

A 26.42 56.17 8218

LogA 23.44 57.53 8218

It can be seen that using the log-amplitude spec-

trum (LogA) the EER was reduced by almost 3% in

absolute value, although PB-ERR slightly increased.

Since in this work the focus is on the frame-level clas-

siﬁcation, the rest of the experiments were performed

using the logarithmic representation of the spectrum.

The results in Table 3 show the performance of the

system using the proposed pooling techniques for the

Generic Input Model (GIM): Average Pooling (AP),

Standard Max Pooling (SMP) and Mel-Scale Max

Pooling (MSMP). AP and SMP had a pooling size

of 4 bins so the resultant input had 256 units. This

size was chosen to avoid merging of spectral bins

corresponding to class-speciﬁc frequencies of differ-

ent alarm classes inside pooling groups, which may

harm the system performance. In order to keep a

minimum resolution while preserving the maximum

alarm-speciﬁc spectral information, a bank of 60 ﬁl-

ters was used in MSMP, although typically 40 ﬁl-

ters are used in speech and acoustic recognition (e.g.

(Sainath et al., 2013)).

Table 3: Pooling techniques results.

Technique

Evaluation metrics (%) Network

weightsMR=FAR PB-ERR

AP 30.24 64.15 2074

SMP 23.55 55.20 2074

MSMP 25.00 57.47 506

Non-linear pooling techniques clearly provide

better results than average pooling, both in terms

of frame-level and period-level metric scores. This

may be explained by the fact that both max-pooling

schemes preserve high spectral peaks corresponding

to alarms. Notice that a strong reduction of network

weights in MSMP with respect to SMP is obtained at

the expenses of only a small increase in EER (6.1%

relative). Similarly, at the frame-level compared to

the best baseline (LogA), much smaller number of

training weights is used, what is an advantage in terms

of computation time and overﬁtting. Next experi-

ments aimed to further decrease both the error rate

and the number of training weights.

Experiments with GIM were performed includ-

ing partially connected hidden layers for weighting in

BIOSIGNALS 2017 - 10th International Conference on Bio-inspired Systems and Signal Processing

frequency (FW) and in time (TW), the results from

which are shown in Table 4. Spectral representations

after SMP and MSMP were used with a FW layer of

pooling size 4 and 5, respectively, creating a hidden

layer of size 64 and 12 units. Further, a TW layer

with a context window of 5 frames was implemented

also on both representations. The pooling sizes were

experimentally optimized.

Table 4: Generic input model results using partially con-

nected structures.

Setup

Evaluation metrics (%) Network

weightsMR=FAR PB-ERR

SMP+FW(4) 18.27 50.14 450

SMP+TW(5) 16.67 46.03 2050

MSMP+FW(5) 19.05 45.32 98

MSMP+TW(5) 13.87 41.65 482

In terms of the frame-level evaluation, the use of

FW layer with the SMP representation gives slightly

better results than using it with MSMP, but the oppo-

site is true in terms of the event-level evaluation. Us-

ing TW layer rather than FW layer with both SMP

and MSMP better results were achieved, although

the number of training weights was around 5 times

higher. Also the size of the input representation it-

self affects: it seems that wider frequency representa-

tions (using SMP) work better when the input is only

one frame, but shorter representations (using MSMP)

work better when temporal context is included.

Recurrent neural networks were also explored

for GIM, speciﬁcally, the Long-Short Term Memory

(LSTM) networks. The objective was to let the net-

work learn the temporal recurrence inherent to the se-

quence of alarm periods. Table 5 shows the results

for different LSTM setups. Starting from a baseline

setup, experiments were performed changing only

one parameter value at each time. The ﬁxed setup was

the MSMP spectral input, a single hidden layer, and a

hard sigmoid inner activation function. The baseline

setup had 4 cells (C) and 5 timesteps (TS), i.e. the

number of spectral frames to look backward. And in

the rest of experiments the number of timesteps and

the number of cells were modiﬁed. Also, averaging

the MSMP input frames in a context window (CW) of

size 5 was tried, similarly to TW layer. Note that more

experiments were carried out with different parameter

values, but only the best results are presented.

The various LSTM results do not differ much from

their baseline performance and do not achieve those

from the combination of MSMP input frames with

TW layer that actually shows a smaller number of net-

work weights.

In Table 6 the experimental results for the Class-

Table 5: Generic input model results employing LSTM.

Setup

Evaluation metrics (%) Network

weightsMR=FAR PB-ERR

Baseline 14.84 48.03 >1000

TS(2) 14.56 49.45 >1000

CW(5) 14.30 47.41 >1000

C(6) 14.84 47.62 >1500

C(6)-CW(5) 14.09 45.66 >1500

Speciﬁc Input Model (CIM) are shown. In the ﬁrst

two rows, the network consists of a single fully con-

nected (FC) layer of 8 hidden units. The network in-

put consisted of concatenating ± 1 or 2 bins around

the bins corresponding to alarm-speciﬁc frequency

components (SF). TW layer was also used in CIM

with the same size of the context window as in the

previous experiments (i.e. 5 frames). As the num-

ber of training weights was small enough, the experi-

ment with an extra fully connected (FC) hidden layer

of 8 hidden units on the top of TW layer was also per-

formed.

Table 6: Class-speciﬁc input model results using fully con-

nected and partially connected hidden layers.

Setup

Evaluation metrics (%) Network

weightsMR=FAR PB-ERR

SF(±1)+FC 16.44 44.09 146

SF(±2)+FC 16.37 41.45 226

SF(±2)+TW 13.23 41.02 202

SF(±2)+TW+FC 10.69 38.36 376

A wider margin around speciﬁc frequency compo-

nents slightly improved the evaluation results at both

frame and alarm period levels. As in GIM, using a

TW layer instead of fully-connected layer improved

the results, reducing the frame-level metric score by

almost 3% in absolute terms and also reducing the

number of network parameters. Then, even better re-

sults were obtained when a second fully connected

hidden layer of 8 units was added. The number of

network weights still was low and further reduction of

both metric scores was around 3% in absolute value.

Finally, the best structures obtained with ’a8’

alarm class for the baseline, GIM and CIM, which are

highlighted in bold in Tables 2, 4 and 6, respectively,

were replicated to the rest of alarm classes:

1. Baseline: logarithmic representation of the mag-

nitude spectrum and a fully connected layer of 8

hidden units (LogA+FC, see Table 2).

2. GIM: TW layer over 5 concatenated MSMP input

frames (MSMP+TW(5), see Table 4).

3. CIM: concatenation of ±2 spectral bins around

A Neural Network Approach for Automatic Detection of Acoustic Alarms

the alarm-speciﬁc bins at the input, TW layer

over 5 concatenated MSMP input frames and a

second fully connected hidden layer of 8 units

(SF(±2)+TW+FC, see Table 6)

The results shown in Table 7 are the metric scores

averaged over the 7 considered alarm classes. The

baseline model and GIM have the same number of

training weights for all the classes as they have the

same input. But the CIM structure has a different

number of weights depending on the number of class-

speciﬁc frequency components. Namely, for each

alarm class, all its frequency components (shown in

Table 1) were included at the input; thus, each alarm-

class has a different number of network parameters.

Note that the number of network weights provided in

Table 7 for CIM is an average value over all alarm-

speciﬁc structures.

Table 7: Best model structure results over all alarm classes.

System

Evaluation metrics (%) Network

weightsMR=FAR PB-ERR

Baseline 23.42 68.68 8218

GIM 17.76 54.80 482

CIM 11.13 53.75 330

Both kinds of input models improve signiﬁcantly

the baseline results, and the proposed CIM struc-

ture clearly outperforms GIM. It can be seen that the

event-based evaluation was considerably improved by

both input model structures with respect to the base-

line, but it still has much room for improvement.

5 CONCLUSIONS

In this work several neural network based structures

were presented to detect alarm sounds in a NICU en-

vironment. Two kind of models based on different in-

put formats, that either make use or not of the knowl-

edge about the alarm class properties, were proposed

and tested: generic and class-speciﬁc. Due to the

scarcity of available annotated data, the number of

layers and nodes was kept small. Both linear and non-

linear pooling techniques were considered in order to

reduce the size of the input. Also, in order to exploit

frequency and temporal information while reducing

that network complexity, two types of partially con-

nected hidden layers were implemented.

As expected, it was observed that the class-

speciﬁc input model, which takes advantage of the

knowledge about the alarm frequency components,

yielded better results than the generic input model;

however, the structure of the latter model has does

not need to be adapted to each new alarm class. Also,

both types of partial connections, which make a sig-

niﬁcant reduction of training weights, improved the

error rate, especially the time-based one.

The detection rate is still high, but we believe

that, when a much bigger dataset will be available

and used, the differences in performance among the

various tested neural net structures that have been ob-

served in this work will be substantially kept.

ACKNOWLEDGEMENTS

This work has been supported by the Spanish

government (contracts TEC2012-38939-C03-02 and

TEC2015-69266-P) as well as by the European Re-

gional Development Fund (ERDF/ FEDER). The

authors are grateful to Ana Riverola de Veciana

and Blanca Muoz Mahamud for their work on the

database collection and on the medical aspects of this

study, and to Vanessa Sancho Torrents and Francisco

Alarc

on Sanz for their work on the database annota-

tion.

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