=Paper=
{{Paper
|id=Vol-3011/pattern2
|storemode=property
|title=The Music Note Ontology
|pdfUrl=https://ceur-ws.org/Vol-3011/pattern2.pdf
|volume=Vol-3011
|authors=Andrea Poltronieri,Aldo Gangemi
}}
==The Music Note Ontology==
https://ceur-ws.org/Vol-3011/pattern2.pdf
The Music Note Ontology?,??
Andrea Poltronieri1? ? ?[0000−0003−3848−7574] and Aldo
Gangemi2[0000−0001−5568−2684]
1
LILEC, University of Bologna, Bologna, Italy
2
FICLIT, University of Bologna, Bologna, Italy
Abstract. In this paper we propose the Music Note Ontology, an on-
tology for modelling music notes and their realisation. The ontology
addresses the relation between a note represented in a symbolic rep-
resentation system, and its realisation, i.e. a musical performance. This
work therefore aims to solve the modelling and representation issues
that arise when analysing the relationships between abstract symbolic
features and the corresponding physical features of an audio signal. The
ontology is composed of three different Ontology Design Patterns (ODP),
which model the structure of the score (Score Part Pattern), the note in
the symbolic notation (Music Note Pattern) and its realisation (Musical
Object Pattern).
Keywords: Ontology Design Patterns · Computational Musicology ·
Computer Music · Music Information Retrieval.
1 Introduction
A music note is defined as “the marks or signs by which music is put on paper.
Hence the word is used for the sounds represented by the notes” [10]. However,
musical notation provides some general information on how to play a certain
note. These indications are then enriched in the context of a performance by a
large amount of information, for example, from the musician’s sensitivity or the
conductor’s instructions. Historically, music notation has been a crucial innova-
tion that allowed the study of music and hence the rise of musicology. Music
scores were initially introduced for the primary purpose of recording music by
giving other musicians the possibility of playing the same piece in turn, repro-
ducing it. However, musical notation by definition entails expressive constraints
on the description of musical content.
Representing music involves a number of issues that are closely related to
the complexity of the musical content. This can be found in every system of
?
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
??
This project has received funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement No 101004746.
???
Corresponding author: andrea.poltronieri2@unibo.it
2 A. Poltronieri and A. Gangemi
music representation, from music scores to their various forms of digital encod-
ing, usually referred to as symbolic representation systems. As Cook observed,
music scores symbolise rather than represent music, as people don’t play musi-
cal rhythms as written and often they don’t play the pitches as written: that is
because the notation is only an approximation [7]. To underline this concept, it
is useful to draw a distinction between a score and a musical object [26]. While
the former can be thought of as a set of instructions that the musician or a
computer system uses to realise a piece of music, a musical object consists of the
realisation of this process. In other words, musical notation precedes realisation
or interpretation and defines only partially the musical object [18].
Music notation is not just a mechanical transformation of performance in-
formation. Performance nuances are lost when moving from performance to no-
tation, and symbolic structure is partly lost in the translation from notation
to performance [8]. Moreover, in music notation some features of musical con-
tent are completely overlooked. For example, timbre information is generally
ignored and is at best provided through generic information about the instru-
ment that is supposed to play the part. Many of these problems have not been
overcome by the numerous systems of symbolic representation proposed in recent
decades. Music representation systems (MSR) have been evaluated by Wiggins
et al. [26] using a system based on two orthogonal dimensions. These two di-
mensions have been named as expressive completeness and structural generality.
Expressive completeness is described as the extent to which the original musical
content may be retrieved and/or recreated from its representation [25]. Struc-
tural generality, instead, refers to the range of high-level structures that can
be represented and manipulated [26]. For example, raw audio is very expressive
since it contains a great deal of information about a certain music performance;
but a MIDI representation contains much less information, e.g. timbral features
are not represented in this format. On the contrary, when evaluating structural
generality, raw audio performs poorly, due to the difficulties in the extraction of
structured information (e.g., tempo, chords, notes, etc.) from such a format.
Another problem in representing music is linked to the twofold nature of mu-
sical content, which contains information that can be reduced to mathematical
functions, but also information related to the emotional spectrum and a wide
range of psychoacoustic nuances. In fact, music is distinguished by the presence
of many relationships that can be treated mathematically, including rhythm and
harmony. However, elements such as tension, expectancy, and emotion are less
prone to mathematical treatment [8].
The limits of symbolic representations are also accentuated when the per-
formance is considered in relation to the concept of interpretation. For each
composition, several performances of the same one correspond to as many in-
terpretations of the musical piece. These interpretations can also vary greatly
from one another from agogic (note accentuation, [3]), timbral and dynamic
viewpoints.
Therefore, this work aims to represent musical notes both symbolically and as
a realisation of the note itself. The alignment between these two representation
The Music Note Ontology, 3
systems can be relevant on several grounds. For example, it can allow musical
analysis using a structured representation (i.e. a symbolic representation) while
simultaneously taking into account all the information that is only contained in
the signal (e.g. timbre information). This would allow a different level of analysis,
taking into account information that is usually overlooked in music scores and
symbolic representations. On the other side, this type of representation would
allow structured analysis of the music signal by having the signal information
encoded in strict relation with the music score and the multiple hierarchies that
the music score entails.
Furthermore, this type of representation allows the analysis of different reali-
sations of the same note, also providing information on how a score is performed
differently in different performances.
2 Physical Features and Perceptual Features
The features that need to be taken into account when representing music can be
reduced to four main categories: tempo, pitch, timbre and dynamics. However,
all these concepts refer to music perception. Symbolic representations, however,
only represent abstractions of these features. In contrast, musical performance, as
analysed in recorded form (i.e. signal representation), contains information that
refers to physical characteristics. Through the analysis of these characteristics,
it is in turn possible to abstract the perceptual characteristics aroused by a
particular sound. However, this work aims not to analyse the perceptual aspects
of music, but rather to formalise a representation that aims to express a musical
note from the points of view of both scores and musical objects.
As far as time is concerned, the main distinction that arises is that between
the quantised time of a symbolic representation, and real time, which is expressed
in seconds. A symbolic representation describes temporal information using pre-
determined durations for each note (e.g., quarter notes, eighth notes, sixteenth
notes, etc.). These durations are executed in relation to a time signature, in-
dicated by a tempo marking (e.g., slow, moderate, allegro, vivace, etc.), or by
a metronome mark, which indicates the tempo measured in beats per minute
(bpm). Generally, symbolic notations (e.g. MusicXML) use the same expedi-
ents as classical musical notation. However, some representation systems rely on
real-time, such as the MIDI standard [2].
When considering frequency, the representation issue is more challenging. A
sound, e.g. a note in the form of a musical note, is usually associated with a
pitch. However, although pitch is associated with frequency [16], it is a psycho-
logical percept that does not map straightforwardly onto physical properties of
sound [15]. The American National Standards Institute (ANSI) defines pitch as
the “auditory attribute of sound according to which sounds can be ordered on
a scale from low to high” [11]. In fact, in many languages, the pitch is described
by using terms having a spatial connotation such as “high” and “low” [21].
However, the spatial arrangement of sounds has been shown to be influenced
by the listener’s musical training [23] and their ethnic group [1]. The relation
4 A. Poltronieri and A. Gangemi
between pitch and frequency is evident in the case of pure tones [17]. For ex-
ample, a sinusoid having a frequency of 440 Hz corresponds to the pitch A4.
However, real sounds are not composed of a simple pure note with a unique
and well-defined frequency. Playing a single note on an instrument may result
in a complex sound that contains a mixture of different frequencies changing
over time. Intuitively, such a musical tone can be described as a superposition
of pure tones or sinusoids, each with its frequency of vibration, amplitude, and
phase. A partial is any of the sinusoids, by which a musical tone is described
[17]. The frequency of the lowest partial is defined as the fundamental frequency.
Most instruments produce harmonic sounds, i.e. composed of frequencies close
to the partial harmonics. However, some instruments such as xylophones, bells,
and gongs produce inharmonic sounds, i.e. composed of frequencies that are not
multiples of the fundamental. As a result, the pitch perception of these sounds
is distorted for a listener who is unfamiliar with these instruments [15]. Several
approaches have been proposed to determine the perceived pitch of these sounds,
based both on the periodicity of the frequencies that compose the sound [22] and
on spectral clues [5]. However, recent research suggests that pitch perception in-
volves learning to recognise a sound timbre over a range of frequencies, and then
associating changes in frequency with visuospatial and kinesthetic dimensions of
an instrument [15].
On the other hand, timbre is a sound property even more difficult to grasp.
ANSI defines the timbre as the “attribute of auditory sensation, in terms of
which a subject can judge that two sounds similarly presented and having the
same loudness and pitch are dissimilar” [11]. However, even today, the definition
of timbre is rather debated. For example, it has been defined as a “misleadingly
simple and vague word encompassing a very complex set of auditory attributes,
as well as a plethora of psychological and musical issues” [14]. The main prob-
lem in defining timbre is that there is no precise correspondence with one or
more physical aspects. Timbre is also challenging to abstract, as it is usually
described using adjectives such as light, dark, bright, rough, violin-like, etc. Nu-
merous studies have shown that timbre depends on a large number of factors
and physical characteristics. Some research has tried to represent the perception
of timbre using multidimensional scaling (e.g. [9]). Other studies in this area
focus on the grouping of timbre into a “Timbre Space” [14]. While those studies
represented timbre in terms of perceptual dimensions, others have represented
timbre in terms of physical dimensions, such as the set of parameters needed for
a particular synthesis algorithm [8]. These physical characteristics concern both
the time axis, such as attack time and vibrato variation and amplitude, spectral
features such as spectral centroid, and harmonic features such as odd-to-even
ratio and inharmonicity. An approximation of these features is provided by a
model called ADSR, which consists of the analysis of the envelope of the sound
wave and the measurement of attack (A), decay (D), sustain (S) and release (R).
The envelope can be defined as a smooth curve outlining the extremes in the
amplitude of a waveform. The ADSR model, however, is a strong simplification
The Music Note Ontology, 5
and only yields a meaningful approximation for amplitude envelopes of tones
that are generated by specific instruments [17].
The same distinction drawn between frequency and pitch can be made for
loudness and sound intensity. Loudness is a perceptual property whereby sounds
are ordered on a scale from quiet to loud. Sound intensity is generally measured
in dB, and expresses the sound power (expressed in Watts) in relation to the
physical space in which the sound propagates (expressed in square metres) [4].
As a perceptual property, it is by its nature subjective and is closely related to
loudness. However, loudness also depends on other sound characteristics such as
duration or frequency [17]. Also, the sound duration influences perception since
the human auditory system averages the effect of sound intensity over an interval
up to a second.
Formalizing the aforementioned perceptual concepts is beyond the scope of
this paper. Instead, the purpose of this contribution is to define (some of) the
attributes that can provide helpful information to describe sound material. The
importance of this analysis is to highlight the inner complexity of the sound
material, underlying the connections that can be drawn between physical aspects
and perceptual components, as well as the relationships among these aspects.
3 Related Work
Several Semantic Web ontologies have been proposed for modelling musical data.
The Music Theory Ontology (MTO) [19] aims at modelling music theoretical
concepts. This ontology focuses on the rudiments that are necessary to under-
stand and analyse music. However, this ontology is limited to the description of
note attributes (dynamics, duration, articulation, etc.) at the level of detail of a
note set. This peculiarity considerably reduces the expressiveness of the model,
especially in relation to polyphonic and multi-voice music.
The Music Score Ontology (Music OWL) [12] aims to represent similar con-
cepts described in the Music Theory Ontology. However, it focuses on music
notation, and the classes it is composed of are all aimed at representing music
in a notated form, hence related to music sheets.
The Music Notation Ontology [6] is an ontology of music notation content,
focused on the core “semantic” information present in a score and subject to
the analytic process. Moreover, it models some high-level analytic concepts (e.g.
dissonances), and associates them with fragments of the score.
However, all these ontologies only consider the characteristics of the musical
material from the point of view either symbolic representation or music score.
The aim of this paper is instead to propose a model that can be as general as
possible and that can therefore represent musical notation expressed in different
formats and considering at the same time scores and symbolic notations. This
would allow interoperability between different notation formats and the stan-
dardisation of musical notation in a format that aims to be as expressive as
possible.
6 A. Poltronieri and A. Gangemi
Moreover, to the best of our knowledge there is no works that relates the
musical note (understood as part of the symbolic notation) to the corresponding
musical object (understood as the realisation of a musical note). In addition,
none of the proposed ontologies seem to model the physical information related
to the audio signal.
Therefore, this paper proposes an ontology that can represent a note both
from a symbolic and a performance point of view. To do this, it is necessary
to take into account both the symbolic characteristics (the signs and attributes
typical of musical notation), and the physical characteristics of the note as re-
produced by a musical instrument. However, this work is not intended to go into
the perceptual characteristics of music.
The problem of modelling information with respect to its realisation has al-
ready been analysed in the past. For example, the Ontology Design Pattern
(ODP) Information Realization3 –extracted from DOLCE+DnS Ultralite On-
tology4 [13]– proposes to help ontology designers to model this type of scenario.
This pattern allows designers to model information objects and their realiza-
tions. This allows to reason about physical objects and the information they
realize, by keeping them distinguished.
The same kind of relationship can be found in the FRBR5 [24] conceptual
entity-relationship model, which groups entities into four different levels (work,
expression, manifestation and item). The relationship between expression, de-
fined as the “specific intellectual or artistic form that a work takes each time it
is realized” and manifestation, defined as “the physical embodiment of an ex-
pression of a work”, can be traced back to the modelling problem discussed in
this paper.
However, these examples do not solve the modelling problems that have been
advanced in the previous sections. In particular, the relationship between the
approximate features represented by the symbolic notation and their realisation
remains a challenge in the field of ontology design. To achieve this, this research
proposes a new ontology to represent symbolic musical notation and its realisa-
tion, considering the relationships between the abstraction of musical features
expressed in the symbolic notation and the physical characteristics of the audio
signal. Furthermore, this paper proposes a pattern-based approach, by proposing
an ontology composed of modular and reusable elements for the representation
of both audio and symbolic musical content.
4 The Music Note Ontology
The Music Note Ontology6 (see Figure 1) models a musical note both as a
constituent element of the score, and as a musical object, i.e. as a realisation of
3
http://www.ontologydesignpatterns.org/cp/owl/informationrealization.owl
4
http://ontologydesignpatterns.org/wiki/Ontology:DOLCE+DnS_Ultralite
5
https://www.ifla.org/publications/functional-requirements-for-bibliographic-records
6
The OWL file of the ontology can be found at: https://purl.org/
andreapoltronieri/notepattern
The Music Note Ontology, 7
the event represented by a score or symbolic notation. To do this, the proposed
ontology represents both the elements and the typical hierarchies of a score,
describing all its attributes. In addition, the ontology models the realisation of
a note, describing its physical characteristics in a given performance.
The proposed ontology is composed of three distinct Ontology Design Pat-
terns, which are presented in this paper and have been submitted to the ODP
portal at the following links::
– The Score Part Pattern: http://ontologydesignpatterns.org/wiki/Submissions:
Scorepart
– The Musical Object Pattern: http://ontologydesignpatterns.org/wiki/
Submissions:Musicalobject
– The Music Note Pattern: http://ontologydesignpatterns.org/wiki/Submissions:
Notepattern.
Table 1. List of competency questions for the Music Note Ontology.
ID Competency Question
CQ1 what is the name of a note?
CQ2 what part of the score does a note belong to?
CQ3 what are the dynamic indications referring to a note in the score?
CQ4 what is the fundamental frequency of a note?
CQ5 what are the different frequencies that make up the spectrum of a note?
CQ6 what is the duration in seconds of a note, in a given performance?
CQ7 how is the envelope of a note shaped?
The note as represented in the score is described by the Music Note Pattern
(in green in the figure), which in turn imports the Score Part pattern (in orange
in the figure) and the Musical Object pattern (in yellow in the figure). The former
import describes the relationships between the different components of a score,
while the latter models the musical object from the point of view of its physical
features.
A selection of competency questions that the ontology must be able to an-
swer are listed in Table 1. However, the complete list of compency questions in
available on the repository of the project7 .
Some of the classes and properties of the ontology have also been aligned with
currently available ontologies and the alignments are available in a separate file8 .
Alignments with other ontologies were mainly made on the Score Part Pattern
and the Music Note Pattern. Although some of the available ontologies (see
Section 3) define some of the classes and properties present in these patterns, for
7
The project repository is available at: https://github.com/andreamust/music_
note_pattern/
8
Alignments to the Music Note Ontology are available at the URI: purl.org/
andreapoltronieri/notepattern-aligns
8 A. Poltronieri and A. Gangemi
this we needed to re-model the score and symbolic elements. In fact, the objective
of this work is, among others, to abstract from a specific representation system,
combining the attributes of the musical score with other elements of the MIDI
representation (as the most widely used symbolic representation) and, to the beat
of our knowledge, there are no ontologies available with this characteristics.
Fig. 1. The Music Note Ontology in Graffoo notation. Colours define the different
ODPs that constitute it.
4.1 The Score Part Pattern
The hierarchy (i.e. the mereological structure) of a music score is described
by the Score Part Pattern9 . The Part class describes the instrumental part of
a score, which refers to a specific staff of the musical notation that is associ-
ated with an instrument. The instrument playing the part is modelled by the
class Instrument, which is associated by means of a datatype property with
the MIDI program assigned to that instrument (hasMidiProgram). Each part of
the score is also divided into sections (class Section), similar music fragments
(class HomogeneousFragment) and voices (class Voice). A voice is defined as
9
Score Part Pattern URI: https://purl.org/andreapoltronieri/scorepart
The Music Note Ontology, 9
one of several parts that can be performed within the same staff (e.g. alto and
soprano). A section is instead a part of the music score defined by the repetition
signs. A fragment, on the other hand, is a grouping of notes that share the same
metric, tempo and clef, described by the object properties hasMetric, hasTempo
and hasClef.
4.2 The Musical Object Pattern
The Musical Object Pattern10 models the physical features of a musical note
object, i.e. the execution of a note. Specifically, this pattern models the physi-
cal characteristics that can be extracted from the sound wave produced by an
instrument playing a musical note. The MusicalObject class is connected to
four classes that describe these physical characteristics, namely duration, sound
intensity, frequency and envelope. The MusicObjectDuration class expresses
the duration in seconds of the musical object, by means of the object prop-
erty hasDurationInSeconds. In the same way, the musical intensity is modelled
via the SoundIntensity class. Frequency is modelled by means of the class
Frequency and its sub-classes FundamentalFrequency and PartialFrequency.
For each expressed frequency, the magnitude of the frequency is also indicated us-
ing the hasFrequencyMagnitude datatype property. Finally, the Envelope class
is connected to four datatype properties that describe the envelope of the wave-
form according to the ADSR model, namely hasAttack, hasSustain, hasDecay
and hasRelease.
4.3 The Music Note Pattern
The Music Note Pattern11 models the symbolic note (as represented in a score
or in most of symbolic representation systems) and its attributes. Moreover, this
ODP aims to abstract over different music representation systems. This means
that it is possible to represent with this pattern music that was originally encoded
in other formats. To do so, the pattern proposed describes each symbolic notes
both by means of the standard music score notation and by using the MIDI
reference for each of the notes’s attributes. This would allow the conversion
between
SymbolicNote is the central class representing the note as represented in the
score. This class has seven subclasses, one for each of the seven notes of the tem-
pered system (primitive notes), plus 35 subclasses which are defined as the com-
bination between primitives and accidentals. The class Accidental (which has
in turn five subclasses: Flat, Sharp, Natural, DoubleFlat, and DoubleSharp)
allows the representation of altered notes. This allows to represent every note
of the Western notation system, taking into account the function with respect
to the tonality of the piece (e.g. differentiating between the notes A# and Bb).
10
Musical Object Pattern URI: https://purl.org/andreapoltronieri/
musicalobject
11
Music Note Pattern URI: https://purl.org/andreapoltronieri/notepattern
10 A. Poltronieri and A. Gangemi
Notice that such differentiation has an important cognitive function for the mu-
sic reader (distinguishing enharmonics according to the tonality), function that
is mostly irrelevant for a computer reading a midi, but very relevant for an AI
learning to generate music from notation). Furthermore, the SymbolicNote class
is linked to several classes and data properties, describing the note’s attributes.
The class Position defines the position of the note both in relation to the score
(datatype property hasToMeasure) and within the measure (datatype property
hasPositionInMeasure). The class NoteDuration describes the symbolic du-
ration of the note, as expressed in the music score. The NoteDynamic class,
instead, describes the dynamics of the note with reference to both musical no-
tation (datatype property hasLiteralDynamic, e.g crescendo, vibrato, etc.) and
the MIDI standard (data property hasMidiVelocity). Similarly, the NotePitch
class defines both the octave in which the note is located (datatype property
isDefinedByOctave) and the MIDI pitch (datatype property hasMidiPitch).
In addition, the SymbolicNote class is linked to the other two patterns that
make up the Music Note Ontology. The SymbolicNote belongs in fact to a Sec-
tion rather than to a Voice, while it has realisation in a MusicalObject. Finally,
the relationships between the attributes of the SymbolicNote and those of the
MusicalObject are expressed.
The NotePitch of a symbolic note is represented as the abstraction of the
musical object’s frequency (expressed by the object property
hasFrequencyAbstraction), while NoteDynamic and NoteDuration are described
as the abstraction of SoundIntensity and MusicObjectDuration, respectively (ob-
ject properties hasDynamicAbstraction and hasDurationAbstraction).
In order to test the ontology, a Knowledge Base (KB)12 containing a single
note and a single musical object was created. SPARQL queries were also created
for each of the competency questions defined in Table 1. The KB can be tested
against the SPARQL queries, in order to verify the expressiveness of the ontology
with respect to the competency questions. The complete list of SPARQL queries
is available on the repository of the project.
The ontology can also be used to enrich the description of music content that
is already annotated using other ontological models. For example, the classes
and properties describing the structure of musical notation can be aligned to the
widely used Music Ontology [20]. Similarly, the same classes can be aligned with
the aforementioned Music OWL and Music Score Ontology.
5 Conclusions and Future Perspectives
In this paper we have proposed the Music Note Ontology, an ontology for mod-
elling musical content both from a symbolic point of view and in terms of its
realisation. To do this, three different Ontology Design Patterns have been de-
signed to model the internal relations of the score, the note from the symbolic
point of view, and the note as musical object. The relationships between the
12
https://purl.org/andreapoltronieri/notepatterndata
The Music Note Ontology, 11
different musical notations have also been considered, in particular between the
symbolic abstraction and the features that can be extracted from the audio signal
of a performance.
In our future work we aim at modelling the perception of the different compo-
nents of the audio signal, describing the different perceptual levels at which it is
possible to experience music. We also aim to formalise the musical perception of
different features from a subjective point of view, e.g. in individuals with different
musical skills or from different musical cultures. Furthermore, since the model
proposed at the moment can only express notes related to the traditional nota-
tion system (and thus related to the temperate system of the Western musical
tradition) we plan to expand the ontology with notes from other temperaments
and musical traditions, as well as unpitched notes and microtonal variations.
References
1. Antovic, M.: Musical metaphors in serbian and romani children: An
empirical study. Metaphor and Symbol 24(3), 184–202 (Jul 2009).
https://doi.org/10.1080/10926480903028136, https://doi.org/10.1080/
10926480903028136
2. Association, M.M.: The complete midi 1.0 detailed specification: Incorporat-
ing all recommended practices (1996), https://books.google.it/books?id=
cPpcPQAACAAJ
3. Brown, C.: Historical performance, metronome marks and tempo in
Beethoven’s symphonies. Early Music XIX(2), 247–260 (05 1991).
https://doi.org/10.1093/earlyj/XIX.2.247, https://doi.org/10.1093/earlyj/
XIX.2.247
4. Bruneau, M., Scelo, T., d’Acoustique, S.: Fundamentals of Acoustics. ISTE, Wiley
(2013), https://books.google.it/books?id=1\_y6gKuGSb0C
5. Carlyon, R.P., Shackleton, T.M.: Comparing the fundamental frequencies of
resolved and unresolved harmonics: Evidence for two pitch mechanisms?
The Journal of the Acoustical Society of America 95(6), 3541–3554 (1994).
https://doi.org/10.1121/1.409971, https://doi.org/10.1121/1.409971
6. Cherfi, S.S.s., Guillotel, C., Hamdi, F., Rigaux, P., Travers, N.: Ontology-
based annotation of music scores. In: Proceedings of the Knowledge Capture
Conference. K-CAP 2017, Association for Computing Machinery, New York,
NY, USA (2017). https://doi.org/10.1145/3148011.3148038, https://doi.org/
10.1145/3148011.3148038
7. Cook, N.: Towards the complete musicologist. In: Proceedings of the 6th Interna-
tional Conference on Music Information Retrieval (2006)
8. Dannenberg, R.B.: Music representation issues, techniques, and systems. Computer
Music Journal 17(3), 20–30 (1993), http://www.jstor.org/stable/3680940
9. Grey, J.M.: Multidimensional perceptual scaling of musical timbres. The
Journal of the Acoustical Society of America 61(5), 1270–1277 (1977).
https://doi.org/10.1121/1.381428, https://doi.org/10.1121/1.381428
10. Grove, G., Fuller-Maitland, J.: Grove’s Dictionary of Music and Musicians. No. v. 4
in Grove’s Dictionary of Music and Musicians, Macmillan (1908), https://books.
google.it/books?id=FBsPAAAAYAAJ
12 A. Poltronieri and A. Gangemi
11. Institute, A.N.S., Sonn, M., of America, A.S.: American National Standard Psy-
choacoustical Terminology. N.: ANSI, American National Standards Institute
(1973), https://books.google.it/books?id=5Fo0GQAACAAJ
12. Jones, J., de Siqueira Braga, D., Tertuliano, K., Kauppinen, T.: Musicowl: The
music score ontology. In: Proceedings of the International Conference on Web
Intelligence. p. 1222–1229. WI ’17, Association for Computing Machinery, New
York, NY, USA (2017). https://doi.org/10.1145/3106426.3110325, https://doi.
org/10.1145/3106426.3110325
13. Mascardi, V., Cordı̀, V., Rosso, P.: A comparison of upper ontologies. In: WOA.
pp. 55–64 (01 2007)
14. McAdams, S., Giordano, B.L.: The perception of musical timbre., pp. 113–124.
Oxford library of psychology., Oxford University Press, New York, NY, US (2016)
15. McLachlan, N.M.: Timbre, Pitch, and Music. Oxford University Press (Jun
2016). https://doi.org/10.1093/oxfordhb/9780199935345.013.44, https://doi.
org/10.1093/oxfordhb/9780199935345.013.44
16. Moore, B.C.J.: An introduction to the psychology of hearing, 5th ed. An introduc-
tion to the psychology of hearing, 5th ed., Academic Press, San Diego, CA, US
(2003)
17. Müller, M.: Fundamentals of Music Processing: Audio, Analysis, Algorithms, Ap-
plications. Springer Publishing Company, Incorporated, 1st edn. (2015)
18. Nattiez, J.J., Dunsby, J.M.: Fondements d’une semiologie de la
musique. Perspectives of New Music 15(2), 226–233 (2021/05/14/ 1977).
https://doi.org/10.2307/832821, http://www.jstor.org/stable/832821, full
publication date: Spring - Summer, 1977
19. Rashid, S.M., De Roure, D., McGuinness, D.L.: A music theory ontology. In:
Proceedings of the 1st International Workshop on Semantic Applications for
Audio and Music. p. 6–14. SAAM ’18, Association for Computing Machinery,
New York, NY, USA (2018). https://doi.org/10.1145/3243907.3243913, https:
//doi.org/10.1145/3243907.3243913
20. Raymond, Y., Abdallah, S., Sandler, M., Giasson, F.: The music ontology. In:
Proceedings of the 8th International Conference on Music Information Retrieval
(ISMIR 2007). pp. 417–422. Vienna, Austria (Sep 2007)
21. Rusconi, E., Kwan, B., Giorndano, B., Umilta, C., Butterworth, B.: Spatial
representation of pitch height: the SMARC effect. Cognition 99(2), 113–129
(Mar 2006). https://doi.org/10.1016/j.cognition.2005.01.004, https://doi.org/
10.1016/j.cognition.2005.01.004
22. Schouten, J.: The Perception of Subjective Tones. Separaat / Laboratoria N. V.
Philips Gloeilampenfabrieken, Verlag nicht ermittelbar (1938), https://books.
google.it/books?id=d9vjzQEACAAJ
23. Stewart, L., Walsh, V., Frith, U.: Reading music modifies spatial map-
ping in pianists. Perception & Psychophysics 66(2), 183–195 (Feb 2004).
https://doi.org/10.3758/bf03194871, https://doi.org/10.3758/bf03194871
24. Weiss, P.J., Shadle, S.: Frbr in the real world. The Serials Librarian 52(1-2), 93–
104 (2007). https://doi.org/10.1300/J123v52n01 09, https://doi.org/10.1300/
J123v52n01_09
25. Wiggins, G.: Computer-representation of music in the research environment. Mod-
ern Methods for Musicology: Prospects, Proposals, and Realities pp. 7–22 (2009),
https://doi.org/10.4324/9781315595894
26. Wiggins, G., Miranda, E., Smaill, A., Harris, M.: A framework for the evaluation
of music representation systems. Computer Music Journal 17(3), 31–42 (1993),
http://www.jstor.org/stable/3680941