PROCEEDINGS of the
23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Environmental noise event classification based on self-organizing
map using psychoacoustic features and spatial filtering
Jure MUROVEC1; Luka ČUROVIĆ1; Tadej NOVAKOVIĆ1, Jurij PREZELJ1
1

Faculty of Mechanical Engineering, University of Ljubljana, Slovenia

ABSTRACTOne of the main challenges of environmental noise monitoring is the classification of noise events. This is
critical for the assessment of individual noise source contribution to the overall noise level. Numerous
attempts have been made to provide algorithms for such classification, however with low certainty. The main
reason for poor results is the lack of proper noise features. For this purpose, a new feature "noise source
direction" has been applied, based on a small horizontal microphone array. It uses a never seen before
algorithm which is computationally inexpensive and provides exceptional beam-pattern directivity. The arrayis implemented as a part of an autonomous environmental noise source classification system. Some of thefeatures are provided directly from the array itself and are combined with psychoacoustic metrics. Artificial
Neural Network (Self Organizing Map) performs the classification based on extracted spatial and acousticalfeatures of environmental noise sources.
Keywords: Environmental noise, Classification, Self-Organizing maps, Psychoacoustics, Localization

1. INTRODUCTION
Environmental noise is defined as unwanted outdoor sound, created by human, animal o renvironmental activities and it has become a growing world problem. Obtaining satisfactorymeasurements both in the time domain and spatially is one of the main challenges in environmental
noise monitoring. The noise level at the point of measurement dep ends on a combined contribution
from all surrounding noise sources. Also, Determining the contribution of a specific noise source is afrequent task acoustic laboratories have to perform. A typical example is depicted in Figure 1.

Figure 1 – Environmental noise source measurement point where multiple noise sources are present

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jure.murovec@fs.uni-lj.si

The problem arises when residual noise levels are comparable or even higher than the observed
noise source. The contribution of main noise sources to the total noise level can be determined byextensive measurements under different operating conditions of individual sources. Different noise
sources can operate simultaneously or at different time intervals while the duration of their operation
is rarely deterministic. Therefore, the presence of personnel is always required to sample and record
the operating conditions and the associated sound pressure levels (SPL). Another argument against
extended periods of monitoring time are the changes in weather conditions which have a significant
effect on monitored noise levels, (1-3). Continuous noise measurements are a credible source of
information and a prerequisite for extrapolation when modeling, for example, traffic noise, (3,4).
Expensive equipment, long measurement time and the need for educated staff are what makes suchmeasurements expensive. Those around shipyards, ship ports, airports, factories, mines, and otherlarge industrial areas, as well as in the city centers, usually require permanent installation of soundlevel measuring equipment. In such cases, the measurement equipment is left unattended and th erecording of the environmental noise levels, including residual noise, is performed automatically. If
operators are present during the noise measurements, residual noise events can be excluded from themeasurements and noise properties, including the classification of noise sources and their directions,can be recorded in situ. However, it is impractical and costly to assure the presence of certified
personnel at the noise monitoring location over long periods of time.
In order to control and reduce environmental noise, we must find new approaches of how toeffectively measure it. People can very skillfully perceive and judge the general characteristics of thesurrounding sound field. The computer does not have enough capacity to match this, so developing
computational methods to automatically extract this information also has great potential in a variety
of applications. Designing an environmental noise classification system that mimics the operation of
educated personnel as much as possible should be the next logical step. A considerable amount ofmanual work can be saved by automatically determining the contribution of each individual noise
source to the total noise level.
As almost all environmental noise sources are placed far from the measurement loca tion, we canconsider that the incoming sound rays propagate in a two-dimensional plane parallel to the ground.
This leads to the assumption that one-dimensional microphone array can be applied for the purposeof spatial filtering and classification of environmental noise sources.

2. AUTOMATION OF ENVIRONMENTAL NOISE MONITORING
In the last decades, automatic noise identification and classification has become a very active
subject of research. It can be directly or indirectly implemented into a very wide area o f topics,
including speech recognition, pattern identification, and context-aware applications. In order toachieve automation of environmental noise monitoring, the equipment should mimic the operator's
activities during measurements. Personnel classifies noise events depending on the noise level
threshold, which can be similar to the background noise level, the direction of the noise source andthe subjective noise recognition capabilities. The classification is therefore based on the experienceof the operator.
A pattern classification algorithm typically consists of a feature extractor and a classifier as seenin Figure 2. Regarding classifiers, different systems and algorithms for automatic classification ofenvironmental noise sources were already proposed including Hidden Markov Model, Neural
Networks, k-NN, Support Vector Machine, and Fuzzy logic, dynamic time warping, and Gaussian
Mixture Models (GMM) (5, 6, 7).

Figure 2 – Block diagram of a typical noise classification system4423

Deep convolutional neural networks (CNN) have the ability to learn discriminative spectro temporal patterns and that makes them well suited to environmental sound classification. First, they
are capable of capturing energy modulation patterns across time and frequency when applied tospectrogram-like inputs, which has been shown to be an important trait for distinguishing between
different, often noise-like, sounds such as engines and jackhammers (7). Neural networks tend to be
extremely precise and more often than not, outperform other classification algorithms, (8-11). The
problem with CNN is the vast amount of training data and time that is needed to fine -tune the classifier.
Additionally, the algorithms are designed or learned for a specific kind of noise source to acqui re thebest possible performance, (12). Generally, artificial neural networks are divided into two maincategories: supervised and unsupervised. Self-Organizing Maps (SOMs) are competitive andunsupervised training networks of feed-forward type. There is no training data and no expected output
for the learning process. SOMs are able to discover statistical similarities in the input space which
they exploit and automatically cluster the data into different input classes. A SOM is built from nodes
or neurons and can be visualized as a grid-like neural network array. The radius of surroundings is
given by the number of neurons in the surroundings of the best matching unit (BMU) /winning neuron.
Weight vectors of the same dimension as the input and at a particular position in the map space areassociated with each node. Kohonen self-organizing neural networks have two layers, an input layer
and an output layer, known as the competitive layer. Each input neuron is connected to every neuron
on the output layer, the latter being organized as a two-dimensional grid, (13).
While one part of the research deals with the classification itself, the other one concentrates onfeature extraction. The commonly used features including the well -known mel-frequency cepstralcoefficients or more specialized features such as histograms of sound events, or histogram of gradientslearned from time-frequency representations. Although there has been some use of psychoacoustic
metrics outside of their initial purpose, they have not yet been implemented in any kind of system forclassification of environmental noise sources.
Psychoacoustics is the science of the human perception of sound. It studies the relationshipbetween sensory perception (psychology) and physical variables (physics) . Equations were developed
to calculate a set of metrics to objectively describe the complex human perception of sound quality,(14). Loudness N (DIN 45631/A1), tonality T (DIN 45681) and sharpness S (DIN 45692) have alreadybeen standardized. Psychoacoustics is already widely accepted as an important approach in the design
and manufacturing of products to attract and retain customers. This is especially true in the automotive
industry, as car manufacturers have realized that the produced sound could also s erve as a desired
acoustic signal, giving the driver feedback about the functioning of the car. Gradually, thepsychoacoustic technique has been spreading to other industrial areas, from the car door closing soundquality, design of centrifugal fans for vacuum cleaners, end-of-line inspection and gear fault diagnosis
to a concept for the general detection of machine faults. If we want to mimic the operator's activities
during measurements, the use of features that describe the human perception of sound is q uite obvious.
Practically all classification algorithms are implemented in combination with many different typesof signal attributes. Earlier attempts were focused on the development of a universal procedure toclassify all possible noise events. However, this proved to be impossible. More recent algorithms are
specialized for a specific type of environmental noise. Studies have shown that the use of ANN for
the purpose of environmental noise monitoring is the most promising technique going forward, (9 -11).
They do have their shortcomings that need to be addressed and worked on. As said before, it is
impossible to create a universal database for recognition of all noise sources/events. Therefore, thebest performing ANN are those who are trained and designed for a specific type of environmental
noise. Also, the training requires a vast amount of data and a human expert until the classification is
fully autonomous. This is time-consuming and is in contradiction with lowering the costs and making
the environmental noise monitoring cheaper and easier to use and implement.
Motivated by the observations that humans are highly effective in distinguishing different noise
sources, we must pay close attention to how environmental noise measurements are done. Conscio usly
or not, we spatially filter our acoustic space in which we are present. All algorithms used in systems
for automatic classification of environmental noise are based on a single microphone channel soundpressure signal. Therefore, a microphone array in combination with beamforming techniques shouldbe implemented for the purpose of automatic noise source classification. This opens up the possibilityof filtering noise events and sources before the extraction of features for the purpose of classification .
Additionally, the synthesized signal provides an improved Signal-to-noise (SNR) ratio which naturally
improves the recognition of noise sources.

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An autonomous environmental noise source classification system is presented in this paper. It
consists of a small microphone array for the purpose of spatial and noise filtering. To mimic theoperations carried by personnel, psychoacoustic features are extracted and then fed into an artificial
neural network. SOM performs the classification and thus significantl y reduces the time required forpost-processing identification of each noise source. The system acts as a human expert, using soundsource localization and psychoacoustic metrics in its decision making. Reducing the need for humaninteraction lowers the cost and complexity of long term environmental noise monitoring.

3. PROPOSED SYSTEM
3.1 System's characteristics
The proposed system consists of 4 channel horizontal microphone array, A/D converter and acomputer to process the information. All the main parameters are presented in Table 1. The four
microphones measure the sound field simultaneously and their signals are processed for the purposeof noise source localization in a two-dimensional plane. The use of horizontal microphone array isjustified by the fact that almost all environmental noise sources are placed far from the measurement
location. The incoming sound rays, therefore, propagate in a two-dimensional plane parallel to theground.
Table 1 – Main parameters of the proposed system
Parameter

Value

Sample rate

192 kHz per channel

Bits per sample

24

Feature extraction time constant

125 msArray dimension

fi 7 cm x 30 cm

Spatial resolution

6°

Number of features

8

Number of possible noise source classes

Infinite

The direction of the most dominant noise source at a particular time is calculated with a highly
advanced, never seen before algorithm with exceptional beam-pattern directivity. The four acquired
signals are then delayed and added up to provide a synthesized signal with a higher SN R for the
purpose of feature extraction as all microphone array based systems do. What makes this system
remarkable is the additional use of sound source localization directly for pre -classification and as astandalone feature as seen in Figure 3. This brings us one step closer to designing a human-likeenvironmental noise measurement system that has great potential in a variety of applications .

Figure 3 - Block diagram of our environmental noise measurement system. Spatial pre-classification is
performed prior to feature extraction
3.2 Noise source localization
Array processing relies on multiple microphones arranged in a certain geometry. Among all theestablished methods in signal processing, those based on beamforming are probably the most common.
They are simple and have a low computational cost that makes them a priori well suited for long4425

measurement times. However, their performances strongly depend on the array geometry and numberof microphones, (6). The localization of conventional beam-formers is limited to low frequencies, andthus alternative developing a new algorithm was necessary. To keep the system compact andinexpensive, the array needed to be as small as possible with the number of microphones as little aspossible. The frequency range of environmental noise sources goes as low as 20 Hz which translates
to a wavelength of 17 m. Known beamforming techniques would have to be performed on huge arrays,
making them permanently stationary.
Due to the innovative nature of the algorithm, the details of mathematical operations are not present
in this paper. However, the simulation of the array's frequency response is presented in Figure 4 andcompared to the delay-and-sum beamforming. The algorithm is able to localize the noise source in thelowest of frequencies while retaining small distance between the microphones.

Figure 4 – Simulation of array's frequency response for: a) Classical delay-and-sum beamforming, b) Our
algorithm
The detected direction of the most dominant noise source at a given time, as seen in Figure 5,
determines which parts of the whole measurement will be put into the classification algorithm. It is
pointless to extract features when the detected direction is not stationary as this means that there isno clear-cut dominant noise source. This way we avoid large quantities of unnecessary data, reducing
the computational time for classification with SOM.
3.3 Feature extraction
Seven features are extracted from the synthesized signal. Joining psychoacoustic metrics ofloudness, sharpness, tonality and roughness are crest factor, zero crossings and one third octave
spectrum. The calculation is performed every 125 ms or 24000 samples. Another additional featureprovided by the beam pattern is immission directivity, (6), which indicates how dominant the detected
noise source is. It can be used for either spatial classification (by filtering out only the most dominantsources) or as one of the inputs for the final classification. Extracted feature vector serves as an input
for SOM.
3.4 SOM Classification
Input for the SOM are all the feature vectors when the direction is stationary and/or the immission
directivity is high enough. The neuron lattice is a two-dimensional rectangular grid and its size ischosen beforehand. Usually, the number of neurons should be slightly larger than the expected numberof noise sources. The size of the lattice can be fine-tuned with the help of different characteristics ofthe generated output SOM. We can observe the number of times each neuron won while the SOM wascompetitively learning or the statistical properties of neuron and input vector distances such as meanvalue and standard deviation. SOM was chosen because of its unsupervised nature which isn't time consuming nor does it need any training database. Classification based only on the direction of noise
sources and immission directivity would be difficult for moving targets, such as trains, automobiles,4426

etc. SOM also helps to recognize similar or even the exact same noise sources regardless if theirlocation varies with time.
The output of the system is the direction plot, immission directivity plot and noise s ource classplot as seen in Figure 5. Combining the output information with a simultaneous sound level meter(SLM) measurements gives us a calibrated SPL and the contribution of each individual noise source
to the total noise level can be determined.

Figure 5 - Example of the output of the proposed system: Noise is classified only when the direction isstable and/or immission directivity is high enough. Three different noise sources are recognized based on
extracted features

4. EXPERIMENTAL RESULTS
To test the prototype system, we performed a typical measurement of environmental noise near thePort of Koper, Slovenia. Multiple noise sources at the measurement point (MP) were expected as seenin Figure 6. The system was left unattended for approx 5 hours.

Figure 6 – Measurement point near the Port of Koper, (15).
Classification with SOM was performed once the recording has been finished. We chose a 3 by 2
neuron lattice as we expected at least three dominant sources around the MP (Ship MSC Katrina, ship
NEPTUNE and the local traffic of a nearby road). The classification of the 5-hour data was carried

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out instantly because of the spatial filtering. Only data points where the noise source direction is stable
and the immission directivity is high enough are run through the SOM. The performance of thelocalization algorithm was excellent. Frequencies observed in the measurements had far larger
wavelengths than the distance between four microphones. The detected direction of the soundemission proved to be stable and accurate, which is clearly seen in Figure 8.
The results showed three main noise sources as predicted: the two ships and the nearby road. The
classification can be seen in Figure 7. Classes 2, 4 and 6 suggested residual noise sources with random
direction and immission directivity. We can see that immission directivity varies from source to source
as it is directly linked to the noise source's frequency range. In Figure 8a we can observe the detectionof a vehicle as the direction goes from 100° down to 0° and then to around 300°. The vehicle , in thiscase, passed the MP from right to left according to Figure 7. In cases of ships MSC Katrina and
Neptune (Figure 8b and 8c), the direction was stable as one would expect. Even though they wereanchored almost one behind the other in respect to the MP, the system was able to differentiate between
them. The features used for classification performed well and expand the use of spatial filtering: eventhough the dominant noise sources may share the same direction, they can be detected individually.
Figure 8d depicts no particular dominant noise source as the immission directivity is low and the
direction is not stationary. These data points were not included in the SOM classification, thus noclass is assigned.

Figure 8 – Automatic classification od different noise sources using SOM: a) Traffic on a nearby road, b)
MSC Katrina, c) Neptune and d) residual noise
The results clearly show the advantages of the proposed system. It can classify periods of time
where there is a dominant noise source. These ques can be paired with simultaneous SLM
measurements, therefore the contribution of each individual noise source to the total noise level canbe calculated. The use of SOM proved that it can classify different noise sources quicker as there isno need for any kind of training process. Only a couple of minutes of was needed to tune theparameters and perform the classification, instead of hours of manual work by either pressing thetrigger on SLM or post-processing with "pause" and "back erase".

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5. CONCLUSION
The proposed system proved to be effective for determen ing the contributions of individual noise
sources to the overall noise level, especially for long term measurements. A typical environmental
noise measurement was performed and three noise sources were detected and classified. Further
development of the presented system should be carried on. Selection of classes sould be simpler,integration of more indicators for determination of how dominant the noise source is at a give timeshould be implemented and immision directivity still needs to be improved. Upgrading the direction
algorithm for the purpose of detection of multiple noise should be done in the next iteration of thepresented system. Full integration with the SLM is also one of the priorities. Further testing of thesystem is necessary, which will give us a better understanding of its shortcomings and suitability for
integration to other applications, such as production line quality control, cavitation detection, acoustic
imaging, etc.

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