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DOI: 10.4018/IJSSMET.298667
Volume 13 • Issue 1
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*Corresponding Author
1
This study is a systematic review of literature on the classification of sounds in three domains:
bioacoustics, biomedical acoustics, and ecoacoustics. Specifically, 68 conferences and journal
articles published between 2010 and 2019 were reviewed. The findings indicated that support vector
machines, convolutional neural networks, artificial neural networks, and statistical models were
predominantly used in sound classification across the three domains. Also, the majority of studies
that investigated medical acoustics focused on respiratory sounds analysis. Thus, it is suggested
that studies in biomedical acoustics should pay attention to the classification of other internal body
organs to enhance diagnosis of a variety of medical conditions. With regard to ecoacoustics, studies
on extreme events such as tornadoes and earthquakes for early detection and warning systems were
lacking. The review also revealed that marine and animal sound classification was dominant in
bioacoustics studies.
Acoustic Signals, Artificial Intelligence, Classification, Deep Learning, Environmental Monitoring, Machine
Learning, Medical Diagnosis, Security Surveillance, Sound
Sound or acoustic signals are gradually gaining research popularity as a tool for environmental
monitoring, security surveillance, diagnoses of diseases, critical information infrastructure protection,
and data transmission (Bourouhou et al., 2019; Ibrahim et al., 2018; Loey et al., 2020; Luque et al.,
2018). Sound is considered as the second most important sense after sight that is capable of carrying
information about the environment (Perr, 2005). Although sound varies depending on seasons, time,
geographic location as well as propagation medium, it is considered as one of the most significant
signals used to monitor and detect changes in the environment. Accordingly, the ability to differentiate
(classify) one sound or acoustic signal type from another is pertinent that, if accomplished, would
Akon O. Ekpezu, University of Ghana, Ghana
https://orcid.org/0000-0002-9502-1052
Ferdinand Katsriku, University of Ghana, Ghana
Winfred Yaokumah, University of Ghana, Ghana*
https://orcid.org/0000-0001-7756-1832
Isaac Wiafe, University of Ghana, Ghana
https://orcid.org/0000-0003-1149-3309
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result in significant progress in application areas such as early warning disaster management, medical
diagnosis (Loey, Naman, & Zayed, 2020), and action or event detection. Recent studies have shown
that machine learning (ML) algorithms are efficient in the domains of image and speech recognition,
natural language processing, medical imaging, data extraction (Dwivedi et al., 2019; Malfante et al.,
2018; Tatoian & Hamel, 2018) and text classification (Elfergany & Adl, 2020; Sangwan & Bhatnagar,
2020).
Classification aims at predicting accurately the target object and differentiating one object class
from the other given a set of data. It is predominantly performed using selected features that feed
classifier tools such as machine learning and neural networks (Mitilineos et al., 2018). In particular,
sound classification is aimed at classifying audio segments into specific classes which requires the
understanding of the fundamental structure of frequencies in acoustic signals (Dwivedi et al., 2019).
This is commonly addressed with features used in speech and music processing such as MFCC
(Mel frequency cepstral coefficient), linear prediction coefficients (LPC), linear prediction cepstral
coefficients (LPCC) and fast Fourier transforms (Briggs et al., 2012; Chu et al., 2009; Davis &
Suresh, 2019; Karbasi et al., 2011; Mitilineos et al., 2018; Oletic et al., 2012; Pramono et al., 2017;
Sengupta et al., 2016). A variety of machine learning techniques have also been adopted to obtain
robust sound classification models.
Considering the plethora of acoustic features and machine learning (ML) algorithms coupled
with the nature of sound, it is imperative to offer researchers an indication of the major research
trends and methodologies that can assist in designing and developing automatic sound classification
systems. Accordingly, this study provides summaries of the existing literature on algorithms for the
classification of sound and analyzes the use of ML in the various sound classification tasks. The
specific objective of the review is to identify: (a) publication patterns in acoustic signal classification,
(b) trends in the use of ML in acoustic signal/sound classification, (c) open questions and challenges
in the use of ML algorithms in acoustic signal classification, and (d) research gaps in the subject area.
Previous studies that conducted reviews on sound or acoustic signal classifications employed artificial
intelligence (AI) techniques. These studies were evaluated using Greenhalgh’s (1997) evaluation
criteria. The criterion evaluated systematic reviews by accessing the relevance of the review question,
the search strategy, the methodological quality, and the sensitivity and presentation of results and
findings. Although findings from the evaluation of existing reviews in the subject area indicated
that studies available provided summaries and reproducible review methodologies, they focused
predominantly on the classification of biomedical acoustic signals, particularly, on heart sounds
(Dwivedi et al., 2019), lung sound (Palaniappan et al., 2013), respiratory sound (Pramono et al.,
2017), and speech sound disorder in children (Wren et al., 2018). Thus, systematic review on the
classification of sounds is lacking. Considering the various applications of sound in various activities,
the lack of sufficient summaries justifies the need for a systematic review of sound classification.
Recently, ML algorithms have been used for various classification tasks (Hao, Weiss, & Brown,
2018). However, due to the plethora of ML algorithms (Hlioui, Aloui, & Gargouri, 2020: Salama &
Hassanien, 2014), choosing a suitable algorithm for a specific classification task becomes difficult.
Hence, there is the need to identify open questions, publication trends, and current approaches in
algorithm usage that will assist researchers to position appropriately new research activities in sound
classification and detection. To address this, this review examines two broad issues. The research
questions stated in Table 1 are divided into two categories. The Category A consists of questions that
seek to provide an overview of publication trends whereas the Category B seeks to provide a good
methodological background for a broader work by identifying research gaps and current methodologies
in the domain. Specifically, it is expected that the study will provide pertinent information regarding
patterns in publications since 2010, academic outlets that are dominant and attracting more studies,
and countries that have focused on acoustic signal classification most within the specified period.
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As mentioned earlier, questions in Category B will provide information on techniques and
domains of application. Accordingly, the review questions will seek to summarize information on
application domains that have dominated artificial intelligence (AI) for acoustics signal studies, the
most used datasets, the machine learning techniques adopted, and the various evaluation methods
that are mostly adopted. Table 1 provides a summary of the proposed review questions and the
corresponding rationale for posing them.
A systematic search of the literature was carried out in two databases: Scopus and Acoustical Society
of America (ASA). Scopus was selected because it is arguably the most extensive abstract and citation
database for academic publications, whereas the ASA publications database was selected purposefully
since it is the leading source of theoretical and experimental studies in acoustics-related studies.
Publications were extracted from the selected databases using key search terms and their possible
combination using the logical ‘and’ operator. The key search terms included classification, sound,
acoustic signals, machine learning, deep learning, and artificial intelligence. The combination of
the search terms produced the following search phrases (SP):
SP1 Classification of sound and machine learning
SP2 Classification of sound and deep learning
SP3 Classification of sound and artificial intelligence
SP4 Classification of acoustic signals and machine learning
SP5 Classification of acoustic signals and deep learning
SP6 Classification of acoustic signals and artificial intelligence
Table 1. Research Questions and Objectives
Research Questions Objectives
Category A
• What are the yearly publication trends?
• What journal has the highest number of
publications?
• What is the frequency of authors?
• What is the country’s origin of authors’ affiliated
institutions?
• To identify the frequency of primary studies per year.
• To identify the frequency of publications per journal.
• To identify authors who are consistent in writing on the subject
area.
• To identify countries with the highest number of publications.
Category B
• What kind of sound is classified?
• What is the format of the sound?
• What are the sample rates of the audio
recordings?
• What datasets were used for the classification
and (or) evaluation?
• To identify the dominant and less dominant types of classified
sounds.
• To identify predominantly used audio formats for classification.
• To determine the maximum audio frequency that can be
reproduced.
• To identify datasets that are available for public use.
• What are the various application domains? • To identify domains in which sound classification is
predominantly performed.
• What ML techniques have been used for sound
classification?
• What measures are used to evaluate model
performance?
• To identify predominantly used ML techniques and performance
metrics in sound classification.
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A set of specific eligibility criteria were defined and followed to limit the collection of articles to
only those that fit with the research objectives. A suitability check of returned articles was performed
after examining the title and removing duplicate papers. Only articles in which the aim, classification
techniques, and/or results were explicitly stated in the abstract were considered. The inclusion and
exclusion criteria are as follows:
C1 Include only open-access journal articles and peer-reviewed conference papers written in English
and published between the years 2010 and 2019.
C2 Include articles whose titles contain keywords like classification and acoustic signals or sound
and machine learning or deep learning or whose title suggests sound classification using artificial
intelligence.
C3 Exclude duplicate papers from the search results.
C4 Exclude papers whose abstracts do not explicitly state the classification techniques and/or results
of the evaluation metrics used.
C5 Exclude by document type i.e., exclude secondary studies, books, thesis, reports, and letters.
The six search phrases earlier mentioned were used to search the Scopus and ASA databases. The
protocol for this systematic review has three main steps. In the first step, the retrieved articles were
analyzed with an initial exclusion criterion (C1 to C2). In the second step, eligible articles were then
exported to a spreadsheet (MS Excel) for further exclusion by duplicate, abstract, and type of study
(C3, C4, and C5). ASA database does not have the export feature, hence this phase of exclusion was
done directly from the browser and manually documented. The third step entailed downloading and
reading eligible articles to extract relevant data concerning the review questions. The extracted data
was collated in a spreadsheet for ease of use and analysis. Figure 1 is a flow diagram showing the
results of the screening after each stage of exclusion or inclusion.
Figure 1. Flow diagram of study screening/selection
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As shown in Figure 1, the initial search output contained 1,295 journal and conference articles
published from 2010 to 2019. Out of these, 181 studies were included after an initial screening by title
and keywords and a total of 90 articles were obtained after the removal of duplicates. Furthermore,
22 studies were excluded based on abstract and document type. Finally, 48 journal papers and 20
conference articles were selected and used in the study.
This section presents the findings on the publication frequency, distribution of journals, authors,
and their country of origin. As mentioned earlier, a total of sixty-eight (68) conference proceedings
and journal articles were identified at the end of the selection process. This comprised 20 (29%)
conference publications and 48 (71%) journal articles.
An analysis of the publication frequency (Figure 2) from 2011 to 2016 recorded between 2 and
4 publications per year. However, from 2017 there was a change in trend such that both conference
and journal articles were recorded each year. Also, there was an upsurge in the publications from
2016 with a double leap from 9 publications in 2017 to 18 publications in 2018. Considering the
upsurge in publication, the popularity of artificial intelligence as well as the emergence of sound as
an alternative means of environmental monitoring, it is envisaged that the area of sound classification
will draw more research attention.
Figure 2. Publications by year
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Further, an examination of the sources of the included studies showed that the 68 studies were
distributed among 35 Scopus indexed conference proceedings and journals, and 69% of the studies
were published in the Journal of America Society of Acoustics (JASA). The journals and their
corresponding number of included studies are JASA (24), Applied Sciences (3), Sensors (3), IEEE
Access (2), APSIPA Transaction on Signal and Information Processing (1), Biomedical Journal,
ELSEVIER - Computers & Electronics in Agriculture, Electronics (1), Elektronika ir Elektrotechnika
(1), Eurasip Journal on Image & Video processing (1), Expert Systems with Applications (1), Frontiers
in Neuroscience (1), IEEE Signal Processing Letters (1), IEICE Transactions on Information anhd
Systems (1), International Journal of Fuzzy logic & Intelligent Systems (1), International Journal
of online & biomedical engineering (1), International Journal of online engineering (1), Noise
mapping (1), PeerJ (1), PLoS ONE (1). While the conference proceedings include ACM International
Conference Proceeding Series (2), ICASSP, IEEE International Conference on Acoustics, Speech
and Signal Processing (2), Computing in Cardiology (2), Lecture Notes in Computer Science
(including subseries Lecture Notes in Artificial Intelligence and Bioinformatics) (2), Proceedings of
the Annual Conference of the International Speech Communication Association, INTERSPEECH
(2), 8th International Conference on Health Informatics (1), International Conference on Machine
Learning and Applications, ICMLA 2012 (1), International Conference on Pattern Recognition (1),
Proceedings of the International Conference on Neural Networks (1), MATEC Web of Conferences
(1), IEEE International Workshop on ML for signal processing, MLSP (1), Procedia Computer Science
(1), 2019 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT
(1), Journal of Physics: Conference Series (1), 2010 Annual International Conference of the IEEE
Engineering in Medicine and Biology Society EMBC (1).
Authors and country origin. An analysis of the authors and their country origin (the country in
which their affiliated institution is located) was performed to identify the author or group of authors
who are consistent in writing on the subject area, as well as countries with the leading number of
publications. With the number of authors per article ranging from 2 to 9, a headcount of the various
authors showed that 229 authors wrote the 68 selected papers. Furthermore, 6 groups of leading
authors in the subject area (i.e., authors with more than one publication) were identified (see Table
2). And it was observed that out of the 6 groups, 4 groups of authors were all interested in classifying
sounds from animals (bioacoustics) and their publications were all journal articles. The 5th group
was interested in classifying environmental sound and they had both conference and journal articles,
while the 6th group focused on heart sounds with conference articles only.
Furthermore, the authors’ country of origin (i.e., address of the authors) and the frequency of
publications per year were identified. According to Figure 3, it is observed that the authors were from
31 different countries with the UK and USA leading the trend by 16% and 12% respectively. China,
France, India, and Korea made up 8%,7%, and 6% respectively. Portugal and Spain 5%, Germany 4%,
while the other 22 countries make up 37% of the publication trend. Further, the highest number of
publications in 2019 was from India (5), the highest in 2018 was from China (4) and Korea (4), and
the highest in 2017 was from UK (5). Again, China (2) and the USA (3) had the highest publications
in 2016 and 2012 respectively. Other years had one study per country. The countries with only one
study include Ireland, Pakistan, Saudi Arabia, Morocco, Italy, Hong Kong, Jordan, Taiwan, Brazil,
Estonia, Switzerland, Singapore, Sweden, and Austria. It is worth noting that publications from the
UK are most consistent since 2012, each year there is at least one publication coming from the country.
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The discussions in this section are results obtained in line with Category B of the research questions.
For ease of reference, the selected articles have been numbered in the order in which they were
selected - A1 to A68 and will be used accordingly in further ana lysis (see Appendix for a list of
included studies).
Table 2. Leading authors
Groups Paper Title Year Journal Study
1 An approach for automatic classification of
grouper vocalizations with passive acoustic
monitoring.
2018 The Journal of the
Acoustical Society of
America
A9
Classification of red hind grouper call
types using a random ensemble of stacked
autoencoders.
2019 The Journal of the
Acoustical Society of
America
A12
2 Dynamic time warping and sparse
representation classification for birdsong
phrase classification using limited training
data.
2015 The Journal of the
Acoustical Society of
America
A18
A robust automatic birdsong phrase
classification: A template-based approach.
2016 The Journal of the
Acoustical Society of
America
A14
3 Domestic cat sound classification using
learned features from deep neural nets.
2018 Applied Sciences A30
Domestic cat sound classification using
deep learning.
2018 International Journal of
Fuzzy Logic and Intelligent
Systems
A44
4 Non-sequential automatic classification of
anuran sounds for the estimation of climate
change indicators.
2018 Expert systems with
Applications
A31
Temporally aware algorithms for the
classification of anuran sounds
2018 PeerJ A33
5 Unsupervised Feature Learning for Urban
Sound Classification
2015 ICASSP, IEEE International
Conference on Acoustics,
Speech and Signal
Processing
A55
Deep Convolutional Neural Networks and
Data Augmentation for Environmental
Sound
2017 IEEE Signal Processing
Letter
A46
6 Heart murmur classification with feature
selection
2010 2010 Annual International
Conference of the IEEE
Engineering in Medicine
and Biology Society
A67
Heart murmur classification using
complexity signatures
2010 Proceedings - International
Conference on Pattern
Recognition
A58
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Classification of sound and data sources. Sound produced by plants, animals, and humans are
numerous and it varies on land, air, and water depending on the medium of propagation, seasons,
activities, and geographic location. There are three main sources of sound - Anthrophony (sounds made
or caused by humans) such as shipping and drilling noise; Geophony (sound from the environment)
such as sea surface noise like the breaking of waves, icebreaking, raindrops; and Biophony (sounds
from animals) such as vocalizations of mammals, anurans, groupers. This section highlights the
different kinds of sounds that were classified, data sources, sample rates, and availability of datasets
as found in the selected articles. As shown in Table 3, 31 studies focused on classifying sounds
caused by animals (Biophony), 19 classified sounds caused/made by human beings (Anthrophony),
and the other 18 classified sounds from a combination of the three sounds categories (anthrophony,
geophony, and Biophony).
In the biophony category, researchers were predominantly interested in classifying sounds from
different species of Odontocetes and Mysticetes (marine mammals). While some of the researchers
were interested in automatically detecting, classifying, and localizing call types from different species
(Guilment et al., 2018; Halkias et al., 2013; Roch et al., 2011; Shamir et al., 2014), others were only
interested in classifying vocalizations of humpback whales, whistles & pulse of dolphins, song cycles
of whales and echolocation clicks of beaked whales (Allen et al., 2017; LeBien & Ioup, 2018; Ou et
al., 2013; Parada & Cardenal-Lopez, 2014).
Classified sounds caused by humans (anthrophony) included respiratory sounds, human voice
disorder, blast sound, snore sound, and baby cry. The baby cry was classified to identify the health
state of a baby (i.e., need, pain, discomfort, or a medical condition) (Aucouturier et al., 2011) while
snoring as a medical condition was classified as a means to automatically differentiate types of snore
sounds (Amiriparian et al., 2017). Similarly, to automatically detect medical conditions such as
cardiovascular diseases and respiratory tract diseases, EEG signals and heart sounds were classified
to identify wheezes, crackles, murmur, extra-systole, normal and abnormal heartbeats. It would
appear that apart from organs involved in respiratory and cardiovascular activity no other sound
from internal organs of the human body was of interest. It might be instructive therefore to consider
extending work to cover sounds from other internal human organs such as for example the intestines.
Such work might be useful in understanding ailments that affect the digestive system. Also, Blast
Figure 3. Distribution of publications by authors country of origin
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Table 3. Summary of classified sounds and datasets
Sound source/ type Link to dataset/name of the dataset Article
code
Biophony (sounds from animals)
1Marine mammals –
Whales and Dolphins
DEFLOHYDRO, OHAS-ISBIO, DCLDE 2015, Auau Channel 2002,
French Frigate Shoals (FFS), CEMMA datasets (http://www.cemma.org),
https://data.gulfresearchinitiative.org
A1, A4,
A13,
A15,
A16,
A17,
A19,
A20,
A22
2Birds http://www.animalsoundarchive.org/Refsys/Statistics.Php, Birdcalls71,
Flight calls, Anuran, CAVI, and CUB-20002011 standard dataset
A2, A5,
A14,
A18,
A21,
A41,
A7
3Fish and Groupers http://www.fishbase.org/and http://www.dosits.org/, SEACOUSTIC2014 A3, A9,
A12,
A37
4Primates- Marmosets and
Monkeys
http://home.ustc.edu.cn/~zyj008/background_noise.wav., http://
marmosetbehavior.mit.edu
A8,
A11,
A35
5Amphibians – Frogs and
Anuran
Recordings from commercial compact discs (CD), recordings from
natural habitat, http://www.fonozoo.com/
A24,
A31,
A33,
A52
6Domestic/farm animals
– dog, cat, sheep, cattle,
Maremma sheepdogs
Online video sources including YouTube, Kaggle challenge database and
Flicker, and https://github.com/kyb2629/pdse.
A25,
A32,
A30,
A44
Anthrophony (sounds made/caused by humans)
7Military blast sound LRPE, East South Central, APG, SERDP-PITT, MCBC-PITT, New York
(Fort Drum)
A6
8Baby cry, human voice
disorders
N/M A24,
A48
sound was classified to differentiate between blast noise and non-blast noise (Cvengros et al.,
2017). Sounds from the environment were predominantly classified to differentiate indoor, outdoor,
natural, vocal, and non-vocal human sounds.
Table 3 continued on next page
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Table 3 continued
Sound source/ type Link to dataset/name of the dataset Article
code
9Respiratory/heart/
lung sound, EEG
(electroencephalogram)
signals
https://github.com/yaseen21khan/Classification-of-heart-sound-
signal-using-multiple-features-/blob/master/README.md, https://
physionet.org/challenge/2016/. https://www.cs.colostate.edu/eeg, (the
Physionet database), Int. Conf. on Biomedical Health Informatics
(ICBHI) scientific challenge database, Dataset B- PASCAL classifying
heart sounds challenge, live recordings from patients using Bluetooth
stethoscope
A27,
A28,
A29,
A34,
A38,
A45,
A47,
A51,
A53,
A54,
A56,
A58,
A61,
A66,
10 Snore sound Munich-Passau snore sound corpus A63
Geophony (sound from the environment) and combination of various sound sources
11 Cinematic sound 44-film dataset A57
12 Oil, water, and gas Life recordings A68
13 Environmental sound Real-world computing partnership (RWCP) sound scene dataset,
DCASE challenge dataset, FindSounds database, Urban-sound 8k
dataset, TIDIGITS dataset, ESC-10, ESC-50 dataset, freeseound.org,
TUT database for acoustic scene classification & sound event detection,
YouTube videos
A26,
A35,
A36,
A39,
A40,
A42,
A43,
A46,
A49,
A50,
A55,
A59,
A60,
A62,
A64,
A65,
Environmental sounds classified are both indoor and outdoor sounds including air-conditioner, car horns, children playing, dog bark, drilling, engine
idling, gunshot, jackhammers, siren, street music, running water, applause, footsteps, crowd, musical instruments, thunder, sea waves, etc.
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Sample rate, audio format, and signal representation. The sample rate which is the number of
samples of audio carried per second ranged from 0.1kHz to 192kHz. The dominantly used sample rates
lied between 22 and 44.1kHz. Out of the 13 classified sounds sound categories identified from the
included primary studies, the dominant audio format used was the .wav format. Others included mp3
(Parada & Cardenal-Lopez, 2014; Shamir et al., 2014), ARFF (Zhang et al., 2016), and HDF5 format
(Bold et al., 2019). Furthermore, signals and audio files were predominantly visually represented as
spectrograms. Spectrograms are graphical or visual representations of sound with frequency on the
vertical axis, time on the horizontal axis, and a dimension of color that represents the intensity of the
sound at each time-frequency location. According to (Amiriparian et al., 2017; Halkias et al., 2013;
Malfante et al., 2018; Oikarinen et al., 2019; Ou et al., 2013), the classification of spectrograms as
natural images allows it to be processed with available image processing tools. Additionally, it helps
in removing the effect of background disturbances on the classification process (Thakur et al., 2019).
Features extracted from spectrograms usually outperform hand-crafted features since spectrograms
do not discriminate phrase classes with similar dominant frequency trajectories (Tan et al., 2015).
However, disparate images in which the axes carry the same meaning irrespective of their location (i.e.,
the axes are shared weights across the vertical and horizontal dimensions), the axes of a spectrogram
do not carry the same meaning (it has time and frequency as the vertical and horizontal dimensions).
Sources of data. To identify publicly available datasets, the datasets used in the reviewed articles
were divided into two categories: pre-existing sound datasets and live recordings.
i. Pre-existing sound datasets: This was made up of sound collected from past experiments, past
projects, or existing sound databases, 28 datasets were identified from this category. Out of the
28, only 18 were stated to be publicly available, while the availability of others was either not
mentioned or stated as not available due to licensing or privacy issues.
ii. Life recordings: This category of datasets was generated by the researchers specifically for their
research. It is made up of recordings of the subject of interest either in their natural habitat (Allen
et al., 2017; Briggs et al., 2012; Ibrahim et al., 2019; LeBien & Ioup, 2018; Roch et al., 2011;
Shamir et al., 2014), or in a controlled environment such as recording rooms and laboratories (Giret
et al., 2011; Oikarinen et al., 2019; Zhang et al., 2018). In some cases, a recording device was
attached to the animals while for humans a Bluetooth stethoscope was used to obtain recordings
of heart sounds. Other life recordings were collected with any of the following recording units,
hydrophones, passive acoustic monitoring (PAM) systems, short-gun microphones, etc. attached
to divers, seafloor moving boats, or sinks. In all, 24 datasets were privately generated and only
5 are available to the public.
An important consideration in research into sound classification is the availability of datasets.
Easy access to high quality dataset is critical to research success in the field. With a total of 52
mentioned data sources from both categories, only 24 are reported to be publicly available, this is
a confirmation of the challenges of limited datasets stated by researchers in sound classification.
Whilst researchers may be able to readily generate or record some forms of sounds that can be used
in research including outdoor sounds like barking of dogs, some other forms of sound may not be
so easily generated or recorded, for example volcanic activity or sound from an impending tsunami.
Distribution of classified sounds according to the application domain. Considering the
different types of classified sounds, the specific sound environment, and the researcher’s objective
for classifying the chosen sound, the classified sounds were categorized into three broad domains.
They include Bioacoustics, Biomedical acoustics, and Ecoacoustics (see Figure 4). The application
domain of bioacoustics was the most explored making up 50% of the study population. This domain
consists of studies that classified sounds made from animals and human beings with the predominant
aim of differentiating sounds and call types between and within animal species. Variations in animal
sounds were also classified based on geographical locations.
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On the other hand, the biomedical domain made up 24% of the study population and consists of
studies that classified snore, heart, and lungs related diseases using sound. The goal of this domain
was to provide an automated and efficient sound/acoustic signal classification system that will assist
medical practitioners in smart diagnosis. Studies in this category also sought to eliminate the invasive
traditional vision methodologies such as the use of medical imaging (Chen et al., 2019; Oweis et
al., 2015; Vrbancic & Podgorelec, 2018). Equally, 26% of the studies explored sounds from the
environment (ecoacoustics) to automatically recognize environmental acoustics scenes as well as
to precisely classify the detected sound. This classification will enable the identification of sound
events, environmental monitoring, and surveillance. The ecoacoustics domain consisted of sounds
from sub-domains such as human activities, urban environment, surveillance, machinery, weather,
and musical instruments.
An automatic classifier does not only identify or differentiate one sound from another, but it also
reduces false detection of sounds (Binder & Paul, 2019). Thus, this section will provide a summary
of the distribution of ML techniques and performance metrics used in the included studies for sound
classification over the study years (i.e., between 2010 and 2019). Several ML techniques were identified
from the studies and categorized as follows:
i. Support Vector Machine (SVM) – SVM, Linear SVM, Radial Basis Function (RBF) SVM,
MIML (multi-instance multi-label) SVM
ii. Convolutional Neural Network (CNN) - CNN, Feedforward deep convolutional neural network,
two-stream CNN (TSCNN-DS), CaffeNet pre-trained CNN, LeNet based CNN, SoundNet,
EnvNet, multi-scale CNN (WaveMsNet), AlexNet, GoogleNet, and VGG16
iii. Artificial Neural Network (ANN) - Deep Neural Network (DNN), Multilayer perceptron (MLP),
Self-organizing map, Deep residual networks (ResNets), Convolutional deep belief network
(CDBN), Sparse Auto-Encoder (SAE), Self-organizing map-Spike Neural Network (SOM-SNN)
iv. Long Short-Term Memory - Recurrent Neural Network (RNN), LSTM-RNN, and Long short-
term memory-fully convolutional network (LSTM-FCN)
v. Random forest (RF)
Figure 4. The distribution of application domains
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vi. K-Nearest neighbor (kNN)
vii. Logistic Regression (LR)
viii. Decision Tree (DT)
ix. K-Means
x. Ensemble Learners (EL)
xi. Others - Sparse Representation-based Classifiers (SRC), Dynamic Time warping (DTW), Hidden
Markov Model (HMM), Gaussian Mixture Models (GMM), aural classifiers, Non-Temporally
Aware (NTA), Kernel-based extreme machine (KELM), Multi-view simple disagreement
sampling (MV-SDS)
Overall, SVM, CNN, and ANN were the three predominantly used ML techniques in sound
classification. These 3 techniques put together were adopted by 62% of the included studies (see
Figure 5). Figure 5 presents a summary of the amount of research interest that each ML technique
has received during the past decade. Further, it highlights the distribution of research interest in ML
techniques in each publication year. It is important to note that more than one ML technique was used
in some studies. Compared to other identified ML techniques, SVM, CNN, and ANN have received
dominant research interest over the years with at least one of these techniques used between 2010
and 2019, except in 2014 where Gaussian mixture models (GMM) was used.
Support vector machine (SVM) has been identified as a robust technique in both classification and
regression tasks. It is a supervised machine learning algorithm and it seeks to find the hyperplane which
optimally separates the labeled data into their various classes (Bourouhou et al., 2019; Cvengros et al.,
2012; Noda et al., 2016; Qian et al., 2017; Yaseen et al., 2018). Most of the articles that used SVM
were focused on improving the classification performance either by modifying existing approaches
of SVM-based classification or by adding new features to it. Modifications to existing approaches
included Recursive feature elimination (SVM-RFE) and linear SVM (Cvengros et al., 2012), and
SVM with linear kernels (Han et al., 2016), while added features included cost parameter CSVM
(Malfante et al., 2018). Generally, SVMs have been reported to be cumbersome for multi-class tasks
but robust for binary sound classification.
Figure 5. Distribution of ML techniques over publication year
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Neural networks are algorithms that imitate the operations of a human brain to identify patterns
and trends in data. Although its effectiveness is limited by the unavailability of labeled data, it is
argued that they have self-organizing and adaptive learning properties with an outstanding ability to
detect trends based on the sample data (Dwivedi et al., 2019). Accordingly, different types of Neural
Networks in deep learning including CNN, ANN, LSTM were adopted by researchers in the included
studies, and they made up 44% of the identified ML techniques.
Further, the identified classification techniques and their distribution of use were categorized as
follows: supervised ML technique (76%), unsupervised ML technique (5%), semi-supervised (1%),
ensemble learning (3%), and the others (sequential classifiers and statistical modeling techniques)
made up 15%. Furthermore, advanced learning techniques such as transfer learning and ensemble
learners were adopted by some researchers to obtain a more robust sound classification model as
well as overcome the challenges of limited data, overfitting, and lack of labeled data. CNN pre-
trained models such as VGG16, VGG19 LeNet based CNN, SoundNet, EnvNet, multi-scale CNN
(WaveMsNet), AlexNet, GoogleNet and CaffeNet were adopted for Transfer learning (Amiriparian
et al., 2017; Boddapati et al., 2017; Bold et al., 2019; Pandeya & Lee, 2018; Zhao et al., 2018; Zhu
et al., 2018). While an ensemble of stacked autoencoders (Ibrahim et al., 2019), and an ensemble
of supervised, unsupervised, and semi-supervised learning techniques such as random forest, kNN,
Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), and SVM-RBF
(Humayun et al., 2018; Pandeya & Lee, 2018) using majority voting and unweighted average were
adopted for ensemble learning. Additionally, a semi-supervised learning technique called active
learning was used to minimize the demand for human descriptions on sound classification training
models (Han et al., 2016).
Figure 6. Distribution of classification techniques over application domain
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Furthermore, the ML techniques were analyzed with respect to the three application domains
identified in this review. More specifically, the distribution of ML techniques as earlier categorized,
was mapped to the domains of bioacoustics, biomedical acoustics, and ecoacoustics (see Figure 6). As
shown in Figure 6, SVM and CNN were mostly used to classify medical and environmental sounds
respectively. While ANN and other sequential classifiers and statistical models were mostly used
to classify sounds from animals (bioacoustics). Generally, SVM, CNN, ANN, and other statistical
models were predominantly used in the three domains. Figure 6 also shows that while all the identified
classification techniques were used in bioacoustics, certain ML techniques were not used in the domains
of biomedical acoustics and ecoacoustics. Specifically, random forest, K-means, and decision trees
were not used in classifying medical sounds. Similarly, K-means and decision trees were also not
used in the classification of sounds in the environment.
Performance Metrics. An examination of the performance measures adopted by researchers
to validate the reliability of their proposed ML techniques for sound classification is presented in
this section. This includes evaluation measures such as cross-validation methods and classification
metrics. Seven cross-validation methods were identified in the included studies for the primary
purpose of evaluating model performance and computing classification accuracies. They include
10-fold cross-validation (Aucouturier et al., 2011; Han et al., 2016; Lebien & Ioup, 2018; Medhat et
al., 2020; Pandeya et al., 2018; Salamon & Bello, 2015, 2017; Su et al., 2019), Leave-One-Out Cross-
Validation (LOOCV) (Bourouhou et al., 2019; Colonna et al., 2016; Oweis et al., 2015; Parada &
Cardenal-Lopez, 2014; Vahabi & Selviah, 2019), 2-fold cross-validation (Ibrahim et al., 2018; Noda
et al., 2016), 1 for 4-fold (Zhang et al., 2019), 5-fold cross-validation (Boddapati et al., 2017; Briggs
et al., 2012; Fang et al., 2019; Mun et al., 2017; Tschannen et al., 2016; Yaseen et al., 2018), 20-fold
cross-validation (Kumar et al., 2010), and 10-fold stratified cross-validation (Gingras & Fitch, 2013;
Nogueira et al., 2019). Other specific reasons for the adoption of the cross-validation techniques were
to determine validation error rate and estimates of algorithm performance (Han et al., 2016; Lebien
& Ioup, 2018; Vahabi & Selviah, 2019).
Furthermore, classification metrics used to compare and evaluate the performance of the various
ML and statistical techniques were identified. It is important to note that, more than one metric was
used to evaluate the performance of a classification technique in most of the studies. Figure 7 provides
an overview of the number and the proportion of reviewed studies using each performance metric.
As shown in Figure 7, it is observed that accuracy is the predominantly used performance metric and
it was adopted by 36% of the included studies. This is followed by Confusion Matrix (16%), Recall/
sensitivity (14%), and Specificity (10%). Precision, F1-score, AUC score, ROC curve, and UAR are
other adopted metrics in the included studies with an equal distribution of 4%. True Positive Rate
(TPR), False Positive Rate (FPR), G-mean, and mean error rate are the least used. Generally, it was
observed that the classification techniques used in the included studies predominantly had good
classification accuracies.
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The primary objective of the systematic review was to identify publication trends, methodological
approaches, and current algorithms used in the automatic classification of sounds using ML techniques.
This review was restricted to open-access conferences and journal articles published between 2010
and 2019. Based on a set of inclusion and exclusion criteria, the included 68 studies were selected
from Scopus and ASA databases with conference and journal articles making up 29% and 71% of
the study population respectively.
This systematic review was guided by two categories of review questions which were answered
accordingly. In the first category of this review, the publication trends between the years 2010 and
2019 were highlighted. It was observed that 60% of leading authors (that is authors with more than
one publication) in sound classification predominantly focused on classifying sounds from animals,
while the other 40% had an equal interest in classifying sounds in the environment and the biomedical
domain. In addition, most of the studies originated from European and Asian countries (including
the UK, USA, China, France, India, Korea, Portugal, Spain, and Germany) with a minimum of 3
publications and a maximum of 14 publications within the selected study years.
In the second category of review results, 13 groups of classified sounds that cut across the three
major sound sources (anthrophony, biophony, and geophony) were identified. Further, these sound
groups were divided into three application domains namely Bioacoustics, Biomedical acoustics,
and Ecoacoustics. It was observed that the bioacoustics domain attracted more research interest and
researchers were mostly interested in classifying sounds from marine mammals. Yet, little attention
was given to classifying sounds from the underwater environment even in studies that classified
environmental sound. This is a research gap considering that 70% of the earth is covered with water
and the temperature of the ocean determines climate and wind patterns which in turn affects life on land
and the ecosystem (Domingo, 2012). On the other hand, studies in the biomedical domain primarily
focused on diagnosing respiratory diseases using sound. Although the classified sounds cut across
three major application domains, the list of unclassified sounds is inexhaustive. For instance, studies
in the biomedical domain should be extended to classify sounds from other internal body organs
(as an alternative to radiography) to diagnose a variety of medical conditions. Studies should also
investigate the classification of extreme events such as tornadoes, hurricanes, drought, earthquakes
using sound. This will enable early detection and warning systems for natural disasters.
Figure 7. Distribution of the studies over performance metrics
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Review results also showed that a major research challenge reported by researchers was the
unavailability of standardized labeled public datasets. This was particularly challenging for the
biomedical domain, thus, researchers collected data (life recordings) from patients using Bluetooth
stethoscope. Yet, the problem abounds because the collected data cannot be publicly available for
future research. Perhaps, this could be a delimiting factor that dissuades researchers from delving
into certain areas of sound classification.
In the identification of feature extraction techniques, it was observed that, although a variety
of feature extraction techniques were used, specific patterns in the use of these techniques to a
particular application domain could not be established. However, it was observed that MFCCs were
predominantly used in feature extraction due to their ability to imitate the hearing properties of the
human ear.
Furthermore, reported approaches for sound classification involved the use of both machine
learning and non-machine learning techniques. Amongst the various identified classification
techniques, support vector machines (SVM), convolutional neural networks (CNN), artificial neural
networks (ANN), and other probabilistic statistical models were predominantly used in the domains
of bioacoustics, biomedical acoustics, and ecoacoustics. The findings on the prevalence of ANN,
CNN, and SVM in the classification of medical acoustics is similar to findings from the systematic
review on ML in lung sound classification (Dwivedi et al., 2019; Palaniappan et al., 2013). Indeed,
the predominant use of CNN for sound classification is no surprise considering that, most of the
studies adopted an image-based approach for sound classification using spectrograms. Mitilineos et
al., (2018) posit that neural network are adopted for sound classification due to their ability to identify
specific patterns exhibited by sound sources using the distribution of energy over frequency and time.
Also, machine learning techniques are outstandingly able to differentiate target acoustic signals/sound
from an acoustic background (Shamir et al., 2014). Although neural networks reportedly require high
computational power and large datasets, no study reported this as a limitation or a challenge. Overall,
satisfactory results were reported for the various classification techniques as observed in the results
of the performance metrics. Performance metrics such as cross-validation, classification accuracy,
confusion matrix, recall, and precision were used to evaluate the performance of the classifiers. In
cases of an unbalanced distribution of datasets, other performance metrics such as UAR, AUC curve,
ROC curve were adopted.
Finally, two types of acoustic signal classification schemes were identified, they included
detection-and-classification otherwise known as acoustic event detection (AED), and detection-by-
classification otherwise known as acoustic event classification (AEC). While the former involves
detection of the sound and then its classification, the latter involves sound detection by classifying
the audio segments. In detection-and-classification, no classification decision is made, rather
segmentation is done when a segment boundary is detected based on a chosen threshold (Temko &
Nadeu, 2009). Conversely, in detection-by-classification, the task of detection automatically translates
to classification as its strategy is based on using classifiers (such as HMM, logistic regression) with
inbuilt segmentation algorithms (Temko & Nadeu, 2009). As shown in Figure 8, 71% of the studies
focused on AEC, while 29% adopted the AED approach. Also, detection and classification were
performed in the domains of bioacoustics and environment only, while detection-by-classification
cut across the three identified domains with bioacoustics as the most explored.
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This paper presented the findings of a systematic review of primary studies in the area of sound
classification between the years 2010 and 2019. A major strength of this systematic review is that
it was not specific to a particular sound, but it considered every kind of sound that cut across the
domains of bioacoustics, ecoacoustics, and biomedical acoustics. It also identified two broad categories
of sound classification schemes: acoustic event detection (AED) and acoustic event classification
(AEC). Findings from the review indicated that automatic detection and classification systems were
useful tools that could differentiate one acoustic event from the other, especially when deep learning
techniques were used for the task.
Although the reviews provided methodologies and algorithms used in various domains of sound
classification, findings indicated that the methodologies and domains (in terms of scope) were not
exhaustive. For instance, there was no study on the acoustic classification or detection of extreme
events such as seismic and volcanic activities or the classification of medical conditions other than
respiratory tract-related diseases. Also, the unavailability of publicly benchmarked datasets for sound
classification in certain domains posed a challenge to the reproducibility of research approaches.
Another hindrance to reproducibility is that model architectures and methods used for training datasets
were not disclosed, especially in conference articles. Considering the relevance of reproducibility
in scientific research, this research gap should be addressed in future studies. Generally, future
studies should seek to address research challenges such as limited bandwidth, threshold problems,
lack of general applicability of classifiers and publicly available datasets. Furthermore, this study
acknowledged that the search strategy is not exhaustive: limiting the search to only open-accessed
Scopus and ASA creates the possibility of omitting other relevant related studies.
The publisher has waived the Open Access Processing fee for this article.
Figure 8. Distribution of classification categories per application domain by year
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Akon O. Ekpezu is a Lecturer in the Department of Computer Science, Cross River University of Technology
(CRUTECH), Nigeria. She holds; a Bachelor of Science (B.Sc) in Mathematics and Statistics from the University
of Calabar, Nigeria, a Post Graduate Diploma (PGD) in Computer Science from the same university, a Master of
Science (M.Sc.) in Information Technology from the National Open University of Nigeria (NOUN) and a Master of
Philosophy (MPhil) in Computer Science from the University of Ghana. She is currently pursuing a PhD in Information
Processing Science, University of Oulu, Finland. She is interested in the following areas of research; Persuasive
Systems, Behavior Change Support Systems, Machine Learning and Information Security.
Winfred Yaokumah is a researcher, cyber security expert and senior faculty at the Department of Computer Science
of the University of Ghana. His work appears in several reputable journals including Information and Computer
Security, International Journal of Distributed Artificial Intelligence, Journal of Information Technology Research,
Information Resources Management Journal, IEEE Xplore, International Journal of e-Business Research, and
International Journal of Enterprise Information Systems. He is an editor of the Modern Theories and Practices for
Cyber Ethics and Security Compliance. His research interest includes Cyber Security, Machine Learning, Network
Security, and Information Systems Security. He also serves on an International Review Board for the International
Journal of Technology Diffusion.
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All content in this area was uploaded by Akon Ekpezu on Nov 30, 2022
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