PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, GermanyTowards Understanding Healthy and Supportive Acoustic
Environments: the case of a nursing homePaul DEVOS1; Pieter THOMAS1; Francesco ALETTA3; Tara VANDER MYNSBRUGGE2; Patricia DE
VRIENDT1-2; Dominique VAN DE VELDE1-2; Dick BOTTELDOOREN1
12Ghent University, Belgium

Artevelde University College, Belgium
3University College London, UKABSTRACT
In elderly care a nursing home is a facility where on a residential basis different caring functions are taking
place in order to satisfy the specific care needs of the residents. As in many outdoor and indoor situations, thesoundscape of such a space, as originated form its acoustic environment, is of crucial importance since it has
the potential to affect the behaviour of the residents. Apart from sounds originating from the care-related
interactions between staff members and residents and other typical indoor sound sources like equipment
noise, a major contribution to the acoustic environment in a nursing home can be the shouting of residents
themselves. It is known that in case of residents with dementia, shouting behaviour can be a prevalentelement of BPSD (behavioural and psychological symptoms of dementia). Considering the supportive
potential of a soundscape, its quality needs to be studied in view of these shouting events. Using signal
processing techniques applied to datasets obtained in different nursing homes in Flanders these shouting
events can be detected and allow to describe the shouting behaviour of a resident, the temporal pattern ofwhich and its relation to the present soundscape is explored in this paper.
Keywords: Soundscape, dementia, BPSD, supportive acoustics1. INTRODUCTION
Ageing is a demographic characteristic of our current society and raises a key challenge inworldwide care. As cognitive impairment is a common problem for older people, it leads to difficulties
in their functioning. In case of dementia, this impairment can lead to problematic behaviour, which
symptoms are known as BPSD (Behavioural and Psychological Symptoms of Dementia). Since
dementia or dementia symptoms can be observed in more than three-quarters of the residents ofnursing homes (1) it gives rise to a higher level of work related stress for nursing staff and a lower
quality of life for residents. Among the different symptoms the agitated and unattended (and oftenrepeated) shouting is a specific type of behaviour disturbing the stay of other residents and making thework of nursing staff unattractive.
With the definition of a soundscape (2) a construct is available to account for the perception of an
acoustical environment as perceived by a person or a group of persons in a specific context. Since anursing home is a specific indoor context, the perceived characteristics can reveal their degree ofpleasantness and eventfulness (3). In this way soundscapes in some living homes of nursing homes
have been shown to have a rather monotonous and uneventful nature (4).
Soundscapes composed of specific acoustic stimuli (e.g. natural sounds) can contribute to a morepositive perception of the environment and can support the feeling and functioning of a person. Such auser was illustrated in (5) where composed continuous daylong soundscapes were delivered toresidents present in the living rooms or the individual rooms of nursing homes. Such a supportive
intervention arose from co-creation sessions with staff members and related stakeholders to obtain a4164

broadly accepted composition.
Considering the acoustic environment, the group of residents showing a disturbing shouting
behaviour needs special attention. Due to the cognitive impairment the shouting of a resident can arise
as a reaction to external auditive of visual signals, which sensing is disturbed, or which interpretation
lacks agreement with the current context (6, 7). As a consequence these shouting events can beconsidered as an indicator of stressful periods or moments of disturbance as experienced by theresident.
For this reason our aim is to study the occurrence of these events. In the present study we restrict
ourselves to the individual room of a resident in order to have a clear indication of the behaviour of theresident and to explore the daily pattern of these shouting events.

2. METHODS
2.1 Dataset
In order to study the acoustic environment a resident is experiencing cost-effective acoustic sensor
nodes were placed in the individual rooms of different residents at places with reasonable distancefrom specific sources which could bias the results (e.g. television, radio). The sensor nodes wereconnected to a central server to guarantee operation and were equipped with a storage device to allow
continuous long-time sound recording (16 bits, 48 kHz uncompressed sampling). These sound datasets
were obtained during the AcustiCare project (8) where the acoustic environment and the soundscapecharacteristics were studied in 5 different nursing homes in Flanders, Belgium (9-12).
2.2 Shouting event detection
In order to study the shouting events the sound recordings were used to obtain a shouting event
detector. In the current stage of our work a standard neural network was constructed for this purpose(13, 14). The general methodology to obtain such a network is depicted in figure 1. In the construction
part of this approach training sounds are transferred into a feature set, and on these same training
sounds manual annotation is performed to obtain the corresponding annotation set. These are used in alearning engine where in case of a standard neural network forward and backward propagation
methods are used to obtain the coefficients of a single hidden layer network. In order to perform thetraining itself the training set is divided in a training and testing set to obtain a performing acousticmodel. This model is the used in the recognition use when input sounds are processed to obtain theoutput shout annotations. In this way a shouting event detector is obtained.

Figure 1 – Framework for construction (left) and recognition use (right) of the neural network
based shouting event detector.

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3. RESULTS
3.1 Shouting characteristics
Unattended shouting is a common sound of specific residents in a nursing home. In general thesesounds (‘shouts’ or ‘screams’, here referred to as shouts) originate from the human vocal tract and theirresulting spectrogram structure ranges between a harmonic structure (resulting from almost periodic
vocal-fold vibrations) to a noise like structure (resulting from completely atonal turbulent air flow)(15). In order to illustrate such a sound a spectrogram of such a recording is shown in figure 2. In theshouting events the harmonic structure is visible, and in general they show a broad frequency
bandwidth, extending up to frequencies in the range of 6000-8000 Hz.

Figure 2 – Example spectrogram of a shouting recording, where time is indicated in seconds.
Illustrated is a 60 s recording with different shouting events (around 20 – 26 s and around 36-37 s).
(Other sound elements in this spectrogram arise from a background radio and an alarm clock giving a
tap every second.)

3.2 Acoustic shouting detection model
In order to build the acoustic model features need to be calculated from the time sliced audio
recordings. Non overlapping time slices of 170.67 ms were processed with this purpose to obtain afeature vector containing the following different features subsets: general statistical features (crestfactor, standard deviation, variance, mean frequency, median frequency and root mean square value),
MFCC coefficients (as is common in audio processing) and the logarithm of filter bank energies (FBE)(16-18). As a preprocessing step a high pass Butterworth (order 8) filter with a cutoff frequency of 500
Hz was used. The 12 MFCC and the 12 FBE coefficients were calculated for frequencies up to 4000 Hz.
The chosen frame length was motivated by the observed duration of the shouting events, which showa rather slow temporal sequencing. In addition it has the function to smear out the underlying effect ofelementary sounds (as the tapping of an alarm clock) on the feature vectors.
Starting from a manual labelled set of shouting and non-shouting events (with a size of morethan 1000 frames) a single hidden layer neural network with 15 hidden nodes was constructed using a50% split of training and testing sounds. The accuracy obtained for this binary classification achieved
a value of 0.98 for the weighted averages f1-score. The model was the used to obtain the timing of theshouting events during a complete day. The resulting shouting index (count of shouting frames during
the reference period) of 24 hours of recording in the individual room of a resident is shown in figure 3.

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Runs on the datasets of other days showed similar results.

Figure 3 – Shouting index during a complete day (24 hours) as obtained from the acoustic shouting
detector from the individual room of a resident in a nursing home.

4. DISCUSSION
4.1 Detector performance
While the current shouting detector was targeted to make a distinction between shouting andnon-shouting events (sounds), a degree in shouting exists among these detected sound elements. Suchsounds, variants of human vocalizations, can range from breathing sounds, sounds of speaking,chattering to screaming, shrieking and other types of related vocalizations.
Where the feature vector consisted of different subsets of features, it can be assumed that only alimited number of features (e.g. the FBE coefficients) can result in acceptable classification between
shouting and non-shouting events. This would result in an improved processing speed.
4.2 Daily temporal pattern
As can be seen from the typical result as presented in figure 3, the shouting index shows clear peaks
at distinct moments during the day. A strong peak is present around 8 hr and around 12 hr. These peaks
correspond with the time care interventions are taking place. In the morning, the washing of theresident and the breakfast are moments of strong interaction between the resident and the staff,
typically giving rise to a high level of shouting. The same occurs at noon when lunch is distributed andthe eating is supported by the nursing staff. In the afternoon a broader peak is present. In generaldinner takes place around 17 hr and residents are prepared to sleep later in the afternoon. These arealso moments with staff intervention and are reflected in the typical resulting graph. The broad peak in
the afternoon can arise from afternoon activities taking place which are not understood by the resident.
As can be seen shouting is absent during night, but can start already early in the morning, probably
reflecting wakening of the resident.
It is clear that the soundscape, reflecting the typical characteristics of the context (in our case thecontext of a nursing home), is the result of the contextual details of the present situation. The socialinteractions, in our case the professional care interactions, are an important component to describe thesoundscape. Since these interactions are following a specific time schedule, they result in timing
indications for delivery of restorative soundscape elements.

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5. CONCLUSIONSIn order to study the shouting behaviour of residents of a nursing home showing BPSD standard
signal processing methodologies can be used. Long time recordings in individual rooms of such
residents were analyzed with this purpose. Based on a feature representation of sound time slices aneural network based acoustic shouting detector was constructed. The degree of shouting over time asreferenced by a shouting index (number of shouting frames in a reference period) can be determined inthis way, and leads to the temporal pattern of the shouting. As such, this in an indication of the stressor unsafety level the resident is experiencing during a day. During care interactions with care staff
takes place, which can involve manipulations disturbing a relaxed feeling of a resident. These havebeen indicated to give rise to a high shouting level. The temporal pattern reveals important additional
information for composing a soundscape, as it can be used as a time directive for the delivery ofspecific soundscape elements with an overall restorative purpose.

ACKNOWLEDGEMENTS
The dataset used in this study was obtained during the AcustiCare project, which was supported by
the Flemish Agency for Innovation and Entrepreneurship (VLAIO) under the TETRA program forapplied research (grant no. HBC.2016.0089).

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