Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Estimation of Perceptual Qualities of Percussive Sounds
Inspired by Schaefferian Criteria: Attack Profile, Mass,and Harmonic Timbre
Sérgio Freire, José Henrique Padovani and Caio Campos
Federal University of Minas Gerais
sfreire@musica.ufmg.br
jhp@ufmg.br
costacaiocampos@gmail.com

Abstract. Pierre Schaeffer’s typomorphology (1966) proposes seven criteria ofmusical perception for the qualification of sound objects under reduced listening; these criteria form the basis of a theory (solfège) of musical objects fitted
to musical contexts where pitch is not the most relevant feature. We developed
a real-time setup that uses low-level audio descriptors to identify and classify
percussive sounds as bundles of features related to Schaefferian concepts. The
paper describes a segmentation method and the tools and strategies used for addressing three of these criteria: attack profiles (as genres of the criterion dynamic)
and mass (which closely relates to the criterion harmonic timbre). The examples
depict quantitative results and discuss their correlation with perceptual qualities.
Keywords: typomorphology; Pierre Schaeffer; percussion; audio descriptors

1

Introduction

In recent years, many papers have sought to bring the tools and procedures related toaudio descriptors and Music Information Retrieval closer to the theoretical and methodological contributions of Pierre Schaeffer. Such works seek to correlate Schaefferianmorphological descriptions and the quantitative data extracted through various techniques of digital signal processing, aiming to automatically index sounds [1], associate“subjective labels” and acoustic features [2, 3], or conjugate descriptors data and perceptual criteria in analytical contexts by employing statistical methods [4], among otherapproaches [5, 6].
In this paper, we aim to approximate the Schaefferian solfège criteria and lowlevel audio descriptors by implementing real-time audio analysis processes using thecomputer music language/environment Max1 . In this study, we have chosen the universe of percussive sounds since their sonic qualities are represented and qualified in avery rough manner when using the concepts and parameters of traditional music theory(notes and durations). Bringing into account concepts like complex sound, mass profile,grain, among others, we hope to develop a more efficient tool for the qualification ofthese sounds to be used in interactive contexts. In this article, we analyze a limited but
1

https://cycling74.com/products/max

125Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

varied selection of percussive sounds, and the results show the suitability of the chosen
descriptors for the qualification and differentiation tasks.
The text is organized as follows. Firstly we summarize the seven criteria of musicalperception as defined by Schaeffer. After that comes the description of the dataset andprocedures used in audio pre-processing and segmentation. A central section deals withthe chosen audio descriptors and their correlations with Schaefferian concepts. A set of
selected examples comes next. Finally, we present the next steps of the project.

2

Schaeffer’s Criteria of Musical Perception

In the sixth book of his Traité des Objets Musicaux (TOM), Pierre Schaeffer presents
one of the most remarkable contributions of his research: a proposal of a generalized
solfège, dedicated not only to the traditional musical notes, but also to any sound considered “potentially musical”. This solfège entails seven typomorphological criteria ofmusical perception that have the purpose to guide the listening process that consciously
attempts to detach the sonic characteristics from any referential or causal events that
may generate sound objects themselves: a method that Schaeffer, borrowing the Husserlian concept of epoché, named reduced listening. In section 34.3, the TOM includes a
Summary Diagram (tableau général), offering an overview of the whole method, wheretypes, classes, genres, and species of sound objects are described according to seven criteria –– mass, dynamic, harmonic timbre, melodic profile, mass profile, grain, and allure [7, 8]2 . The criteria help to locate the position and thickness (site/calibre) of soundobject attributes in the three-dimensional space of a perceptual field formed by pitches,
durations, and intensities.
In section 88 of the Guide des Objets Sonores [9], Michel Chion outlines the distinctive features of sound objects that each of the morphological criteria proposed by
Schaeffer aims to evaluate.
– The mass details how the sound occupies the pitch perceptive dimension.
– Harmonic timbre describes the “diffuse halos” and “related qualities” that seem tobe related to the mass and allow its qualification.
– Grain, in its turn, is related to the “micro-structure” of sound matter and is associated with rapid variations or reiterations of constituent sounds.
– While grain outlines the link between form and matter as one of the sustainment
criteria, allure expresses the dynamism (mechanical, living, or natural) of what
could be defined as a “generalized type of vibrato”.
– Dynamic expresses the evolution of a sound in the perceptive dimension of intensities.
– Melodic profile describes the general contour of a sound in the perceptive dimension of pitches, a sort of trajectory in the tessitura.
– Mass profile, on the other hand, describes the “internal” variations of a sound in thissame perceptive dimension: these changing shapes are responsible for “sculpting”
the mass, making it to be more or less thick or thin, having thus a more or lesscomplex or tonic quality, for instance [9].
2See, particularly, pp. 584-587 of the original edition; pp. 464-467 of the English translation.

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As a general method, reduced listening involves a conscious attitude that refrains
the habitual curiosity towards the sound sources and their meanings in favor of addressing intrinsic features of the sonic phenomena. While authors like Di Scipio [10] remarkthat the concept of reduced listening is ideologically and technologically circumscribed,
ignoring the very audible traces of electroacoustic tools that enable us to focus on the‘sound itself’, its relevance, since the Schaefferian seminal contributions, lies in thefact that the project of a “generalized solfège” has been successful in providing a richtheoretical framework that makes possible to describe different features, behaviors, andqualities of sound objects according to morphological criteria and perceptual dimensions.
Considering Pierre Schaeffer’s well-founded warnings regarding the differences between the study of sound objects using perceptual-sensory criteria, on the one hand, andphysical-acoustic analysis of audio signals, on the other, it is relevant to underline theexperimental nature of the present work. Thus, despite the differences between perceptual and signal-based evaluation, description, and categorization of sounds, our work
is motivated by a common trait of low-level audio descriptors and the Schaefferiansolfège: both focus on intrinsic qualities of sound phenomena, seeking to discriminate
particular characteristics based on certain criteria, dimensions, or parameters. Indeed,
Schaeffer himself, even warning to the differences between perceptual processes andwhat signals can represent, also recognized the usefulness of real-time visualization of
signals using bathygraphs and sonagraphs. [7, 8] 3 .

3

Selection of Sounds, Pre-processing and Segmentation

In our program, the expected inputs are audio streams delivered by microphones, pickups, or mixers, featuring different background noise levels and dynamic ranges. In thecurrent phase, we have chosen to use a set of pre-recorded sounds. This procedure offers
not only variety but also repeatability, two relevant factors for building and improving
tools. The sound selection, depicted in Table 1, was based on Schaefferian types. These
recorded sounds function as live inputs to the setup4 , which runs with a sampling frequency of 48 kHz.
The sounds are segmented between onset and offset points. In some situations, a
new segmentation clue may occur before the offset; in these cases, this clue determines
the offset of the previous event and the beginning of the current one, characterized as“slurred”. The detection of onsets and offsets occurs by comparing an RMS envelope(expressed in dBFS) with two thresholds, 6 dB and 3 dB, respectively, above the background noise level. This envelope uses a very short window for its estimation — 256
3

In the pp. 556-557 of the English translation of the TOM [8]: “It is perhaps disconcerting to
see us, after so many warnings, recommending the use of the bathygraph and the Sonagraphto describe a piece of music.(...) On the physical level the bathygraph and the Sonagraph give
two graphs of the signal in real time: its projection on the dynamic and the harmonic plane. Of
course, these lines are not very intelligible because perceptions of sound differ so much (byanamorphosis) from the signal on the printout.”
4
The soundfiles used in this study are available in the following repository:
https://github.com/lapis-ufmg/2021_CMMR_arquivos

127Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Table 1. Selected sounds.
soundtabla.gliss
tomtomwhip

description
single tabla strokewith glissando
single tomtom stroke
single whip attack

soundrattledescription
single directional rattle shaketamb.tremolo
berimb.jete

tambourine tremolo
berimabau jete, multiple strokes
single berimbau stroke,tamb.slap
single tambourine hand slap berimb.vib
with vibrato
sdrum.dragsnare drum drag, with snare pand.rim.frict pandeiro tremolo-like rim friction
single snare drum stroke,sdrum.nosnare
sleighbells
multiple sleighbells shakes
without snare
bassdrum
single bassdrum stroke
thunder.shake multiple thunder sheet shakes
cymbalsingle cymbal strokerainstickrainstick tip
gong.tuned
single tuned gong stroke timp.roll
timpani rollgong.untuned single untuned gong stroke whistle
single whistle blow
guiro
single directional guiro rub pand.skin.frict single pandeiro skin friction
ratchetsingle ratchet swing
vibes.bow
single vibraphone key bow
cymbal.bow
single cymbal bow

points and hop size of 64 —, which will be referred to as rms256:4. In order to obtain
more efficiency and precision, we implemented this curve with the Max [gen~] object, which uses native audio signal processing routines. Its output is smoothed with alow-pass filter (a single one pole filter, with a –6 dB per octave attenuation), and different cutoff frequencies are employed, depending on the purposes of its use. We use acutoff frequency of 4 Hz in the estimation of onsets, offsets, and attacks. In the latter
case, the audio stream may pass through a filter before the calculations. The estimationof attack profiles and iterative grains uses this same signal with a cutoff frequency of 30
Hz. A control-rate version of this envelope builds the attack profile. Onsets and offsets
also function as gates for other processing tools in time and frequency domains. These
processes explore the data delivered by a rms2048:4 curve and by spectral peaks values
estimated by the [sigmund~] object [11], using the same window and hop size.

4

Perceptual Attributes and Algorithms

Due to text size restrictions, we will concentrate on a subset of Schaefferian perceptual
criteria (or sub-criteria) and their correlated audio tools, namely the attack profile, mass,and harmonic timbre.
4.1

Attack Profile

The importance of the attack portion of sounds has been stressed clearly in the work of
Pierre Schaeffer, deserving special attention in the TOM and Solfège. At that time, hecomplained that physical measurements were far from representing an accurate pictureof the perceived sonic dimensions. About the initial transient portion of sounds (ca. 50ms), he observed that: “A more spectacular experiment involved asking a very good128

Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

trumpet player to play a staccato with an accuracy appreciable to the ear: none of thissound’s eight impulses gave an oscillogram similar to the others (fig. 5)” [8], (p. 164).
It is not our purpose to argue about how different oscillograms associate with similar perceptions of the beginning of sounds. On the other hand, we intend to approach
the initial portion of sounds (ca. 400 ms) with a descriptor that allows for an association
between audio features and perceived qualities, using a tool similar to the “bathygraphic
traces” depicted by Schaeffer [7, 8]5 . This approach is similar but not equal to the estimation of log-attack-times and attack slopes [12]. We prefer to analyze the entire
profile, which may surpass 300 ms, instead of stopping at the point usually named theend of attack. For the same reason, this point will be called the attack first plateau. The
difference between the levels of the first plateau and the onset is named attack size, andthe time interval between them attack duration. The slope of the first plateau (FPSlope)is the ratio between these two values.
The first plateau is estimated as the instant when the derivative of the low-pass filtered audio-rate rms256:4 curve from the (possibly filtered) input audio stream crosses(or comes near to) zero, just after having surpassed a predetermined positive threshold(a sharpness parameter). A value of 200 ms is set as the default reattack threshold since
we prefer to consider multiple fast strokes (such as flans, drags, ricochets) as belonging
to the same profile. Depending on the settings (filtering and thresholds), this estimation
may not produce results for soft attacks6 . We also prefer to consider some iterative sustainments as a single object displaying allures, even when the distance between peaks
exceeds the reattack threshold. The onset of a slurred event also marks the offset ofthe previous one and is defined as the instant when the already mentioned derivative
surpasses the given threshold.
The attack profiles depend on their context of production, mainly on the dynamic
level and duration (which is also related to excitation and sustainment types). Schaeffer
defines seven genres: abrupt, steep, soft, flat, gentle, sforzando, and nil. The first threegenres relate to different attack-resonance types; a sudden burst of energy characterizes
the flat profile; the gentle genre has no apparent attack; sforzando generally associateswith short sounds with a characteristic crescendo; nil points to the very progressive
emergency of a profile. For the sake of comparison, we have kept all the parameters,
except the background noise threshold, fixed for all sounds selected for the present
study. The first 300 points after the onset, corresponding to 400 ms, are plotted ona user screen, and stored in a buffer, for further analysis. Figure 1 depicts the attack
profiles of nine sounds.
4.2

Mass / Harmonic Timbre

Schaeffer defines seven classes of mass: pure sound, tonic, tonic group, channeled,nodal group, node, white noise. Pure and tonic sounds present a clear pitch, while tonic
5
6

See p. 533 of the original edition; p. 425 of the English translation.
These parameters (reattack time and sharpness) help to redefine the fluids limits between thecontext (“whether the criteria are artificially put into a structure...”) and the contexture (“...ornaturally form a structure”) of percussive sounds in contexts with different segmentation clues.
The quotations are from p. 402 of the English translation [8].

129Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

whip

0

20

40

40

40

60

60
0.1

0

0.2

tomtom

0.3

0.4

80
0.0

60
0.1

0

0.2

sdrum.drag

0.3

0.4

80
0.0

20

20

40

40

40

60

60
0.1

0

0.2

gong.tuned

0.3

0.4

80
0.0
0

0.2

guiro

0.3

0.4

80
0.0
20

40

40

40

60

60
0.2
seconds

0.3

0.4

80
0.0

0.1

0

20

0.1

0.2

0.3

0.4

0.2

0.3

0.4

0.2

0.3

0.4

rattle

60
0.1

20

80
0.0

0.1

0

20

80
0.0

berimb.jete

0

20

80
0.0

dB

tamb.slap

0

20

thunder.shake

60
0.1

0.2

0.3

0.4

80
0.0

0.1

Fig. 1. Attack profiles and first plateau (red mark) of nine percussive sounds. The gray curve is arms256:4 low-pass filtered (4 Hz) envelope of the low-pass filtered input signal, and is used forthe estimation of the plateau. The blue curve is a rms256:4 low-pass filtered (30 Hz) envelopeof the non-filtered input signal, and is used for the qualification of the attack profile. Filters andthreshold parameters remained unchanged for all sounds.

groups are chord-like sounds. Node is a filtered noise occupying a definite spectral region, and nodal group a combination of nodes. Channeled sounds are an ambiguousclass between the pitched and unpitched sounds. Within percussion instruments, themost common classes are tonic, channeled, node, and nodal group. However, the latteroccurs more like a combination of single nodes than as a single sound object. Harmonic
timbre is a complementary criterion to mass, being almost inseparable in some situations. In Schaeffer’s words: “Consequently we intend to use the two criteria of massand harmonic timbre in conjunction with each other, considering them rather as connecting vessels, with the exception of certain specific examples(...)” [8]7 . These specificexamples are the pure and tonic sounds. Therefore, we have opted to use the same audio
descriptors for both criteria. Typical harmonic timbre attributes are expressed by terms
like full/hollow/narrow, rich/poor, and bright/matt. The last attribute pair may overlap
with the judgment of the spectral region occupied by the mass of a given sound.
Our strategy to deal with the main classes of mass, and the associated harmonic timbres in the percussive realm, relies on the analysis of spectral peaks and (monophonic)
pitches estimated by the [sigmund~] object. As we are presently not interested in isolating individual notes inside chords, these two outputs are sufficient for our purposes.
Since our context does not presuppose the existence of a harmonic series of spectral
peaks, we can not use traditional descriptors as noisiness, inharmonicity, odd-to-evenharmonic ratio, tristimulus, among others.
The following descriptors use the spectral peaks (up to 20) estimated for each analysis frame and the energy equivalence given by Parseval’s theorem. Their output is a
7

Quoted from p. 412.

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

curve with a refresh rate of 10.67 ms. Percentile 50 (pct50) and percentile 80 (pct80)
are the number of spectral peaks needed to obtain 50% (-3dB) and 80% (-1dB) of thetotal energy present in the signal. For sounds with a broad spectral distribution, 20 peaks
may not reach the chosen percentiles; in these cases, the output will be 20 peaks, andfurther information is to be delivered by the next descriptor. The percentage of soundenergy represented by up to 20 peaks (20P/total) is calculated for each frame and expressed in values between 0 and 1. The frequency of the most prominent peak (MPP)in each frame is expressed in Midicents8 . The object [sigmund~] outputs a valuein Midicents for the pitched frames, and the value -1500 for unpitched frames. Our
descriptor outputs a scalar (percentage of unpitched to total frames: unpitched/total)
and a curve with all values. In this curve, the unpitched values are represented by the
number 1. The estimation of the intrinsic dissonance uses the algorithm developed by
Sethares [13].
The spectral centroid (SC), or the center of gravity of a spectrum, is estimated witha [gen~] routine delivered with the Max program since its version 6. Instead of using
a nominal value in Hz, we use values in Midicents, which define a scale ranging from15.5 to 155 in the audible range. The difference between the lowest and highest peakfrequencies (∆ peaks) is also expressed in Midicents. The spectral region is estimatedfrom the contribution of each peak to different spectral ranges. The first three octaves(20–160 Hz) define the low range, the four intermediate octaves (160–2,560 Hz) the
medium range, and the last three octaves (2,560–20,000 Hz) the high range. If noneof these ranges carry 40% or more of the total energy, the sound frame is classified as
wideband, labeled as (7). Otherwise, any range with more than 40% of the total energy
contributes to qualify one the six spectral combinations: (1) Low, (2) Low/Medium, (3)Medium, (4) Low/High, (5) Medium/High, (6) High.
4.3

Time Series Statistics

Most of the descriptors detailed above are represented by time series, which are subjected to simple statistical analysis just after the offset. Our implementation adapts thealgorithms given in [12], and we have chosen the following scalar descriptors: meanvalue and standard deviation; temporal centroid and spread (normalized by the total
duration); skewness; kurtosis; crest; flatness. These values will support the correlations
with the Schaefferian perceptual attributes.

5

ExamplesFor each live input sound, our program generates real-time curves (or markers) for all
descriptors and calculates the scalar values described in section 4.3. The results of theanalysis of the attack profiles shown in Figure 1 appear in Table 2. We will focus firstly
on the sounds produced by one single stroke (percussion-resonance type). Perceptually,
the whip sound presents an abrupt profile. This attribute correlates with its short duration, low values for temporal centroid and spread, a positive skewness, and a high crest.
8

Midicent is the unit of a logarithmic scale for frequencies, in which the value 69 represents
440 Hz (note A4), and each integer step is an equal-tempered semitone.

131Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

The tomtom stroke presents a steep profile. Although it has a temporal centroid similar
to the whip, its spread is much larger, and the skewness and crest are less pronounced.
The profile of the tambourine slap lies in between these two sounds. The tuned gong
stroke presents a soft profile; in this case, we have a “reinforcement of the resonator”. A
longer duration, a slightly positive skewness, and a high value for flatness correspond to
this attribute. The rattle profile, produced by a shake, is perceived as gentle since an initial shock is absent. The sound production mixes iterative and continuous aspects. The
negative skewness, a small crest, and medium flatness point to this attribute. There arethree sounds with a clear iterative or granular profile: the guiro rub, the berimbau jeté,
and the drag on a snare drum. The parameters of the two latter are halfway between thesteep and the soft profiles; in them, a new stroke prolongs the short resonance. On theother hand, the guiro has a sforzando profile: a high temporal centroid, a considerable
negative skewness, a high crest. We believe that the iterative character could integrate
the basic profiles as a second-order qualifier. Finally, we have the long thunder shake,
whose profile approaches the genre nil: its medium temporal centroid, a low value forcrest, and a high value for flatness corroborate this qualification. In this case, the dynamic level (the average value of the entire object) indicates that a marked crescendo
will happen during its course. In general, long sounds will rely less on their attack portion for their characterization. It seems to us that the use of the slope of the first plateau(which may vary significantly according to specific parameter settings) and kurtosis
could be more significant with more homogenous sounds. In the present selection, they
are not as meaningful as the other parameters.
Table 2. Attack parameters for nine selected percussive sounds (the same from Figure 1), plus
total duration and dynamic level.
FPSlope temp. temp.
dur DLskewness kurtosis crest flatness(dB/ms) centroid spread(ms) (dB)
whip
0.42
0.24
0.26
1.25
2.87 6.08 0.30
396 −46
tamb.slap
0.85
0.22
0.74
0.31
0.25 4.07 0.42
461 −33.5
tomtom
1.03
0.24
1.18
0.17
0.09 3.60 0.43
487 −28.5
sdrum.drag
0.33
0.35
1.06
0.08
0.10 4.28 0.61
616 −31.8
guiro
0.63
0.72
0.39
-0.66
1.36 11.09 0.67
631 −42rattle
0.13
0.67
0.67
-0.21
0.28 2.18 0.67 1151 −42
berimb.jete
0.81
0.35
0.53
0.33
0.66 4.77 0.60 2019 −55.6
gong.tuned
0.24
0.42
1.13
0.08
0.11 1.96 0.92 9219 −42.6
thunder.shake 0.18
0.53
0.20
-0.19
3.82 2.80 0.86 15541 −33
sound

The discussion about mass and harmonic timbre relies on data displayed in Tables
3 and 4. A significant presence of pitched frames points to a tonic sound; the opposite
indicates a node or a channeled sound. In addition, the concentration of energy in a few
spectral components helps differentiating between tonic and channeled sounds on one
side and nodal sounds on the other. The combination of these two descriptors can discriminate between tonic (bass drum, whistle, friction of a tambourine skin, and tabla),channeled (tomtom, tuned gong, and snare-drum without snares), and nodal (cymbal,

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guiro, ratched, and rattle, among other) sounds. The stability of the most prominent peak
may also favor tonic and channeled sounds, but one must be careful with the presenceof melodic profiles, as in the case of the tabla sample. Although intrinsic dissonancevalues may also point to tonic sounds, this is not a univocal association since spectral
peaks not presenting a simple harmonic ratio only increment this value if they are closeenough in frequency (see details in [13]). The interpretation of spectral centroid (andregion) values is straightforward; however, a high value for the standard deviation indicates the presence of some profile (glissando, undulation, filtering, etc.). We can analyze
the shape of these profiles with tools similar to those used for the attack profiles.
In addition to the symbiotic relation between mass and timbre, Schaeffer states that“there is no classified index for perceptions of harmonic timbre“ (TOM, p. 420). In thepresent case, we try to approximate the perceptive attributes full/hollow/narrow with thedescriptors ∆ peaks, pct50, pct80, and region (helped by the intrinsic dissonance), andthe opposition rich/poor with the values estimated for pct80 and 20P/total. For instance,
observing different sounds classified in the medium region, it is possible to split thembetween hollow (bass drum, tomtom) and narrow (whistle, rattle) according to theseparameters.
Table 3. Mass and harmonic timbre parameters (1) for 10 selected percussive sounds.
20P/total unpitched/total(ratio)(ratio)tabla.gliss
227 1.3 ± 0.5 6.6 ± 8.5 0.85 ± 0.12
0.24
tomtom487 1 ± 0.2 1.96 ± 3.1 0.96 ± 0.1
0.38
sdrum.nosnare 560 1.1 ± 0.4 2.4 ± 3.7 0.96 ± 0.1
0.32
ratchet753 19.8 ± 0.6 20 ± 00.42 ± 0.1
0.86
rattle1103 8.4 ± 2.2 19.7 ± 1.3. 0.71 ± 0.1
1.0
pand.skin.frict 1915 1 ± 0.2 1.5 ± 2.1 0.98 ± 0
0.07
tamb.tremolo 1365 12.5 ± 4.2 20 ± 00.62 ± 0.1
0.77
whistle1463 1.4 ± 0.6 5.2 ± 6 0.89 ± 0.1
0.13
bassdrum
3671 1.1 ± 1 1.2 ± 1.5 0.98 ± 0.1
0.04
gong.tuned
9219 1.3 ± 0.8 2.2 ± 1.6 0.98 ± 0.05
0.42
sound

6

dur

pct50

pct80

Final remarks

The results obtained so far have demonstrated that our setup can qualify and differentiate diverse types of percussive sounds with a good approximation to the Schaefferiancriteria. We believe that we have shown the importance of attack profiles for percussive
sounds and the pertinence of the implemented tools for the qualification of mass andharmonic timbre. The next planned steps are the work with performers in real-time interactive contexts (when the pandemic allows), the choice of the most efficient descriptors for each intended perceptive feature, the training of a machine learning algorithm,
and the development of interactive musical works.

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Proc. of the 15th International Symposium on CMMR, Online, Nov. 15-19, 2021

Table 4. Mass and harmonic timbre parameters (2) for 10 selected percussive sounds.
MPP∆ peaksSCregion
(mc)
(mc)
(mc)
tabla.gliss
37 ± 22.5 46.2 ± 10.4 69.9 ± 19.3 54 ± 7.6 1.8 ± 1
tomtom46.2 ± 17.5 55.8 ± 0.2 64.9 ± 15.3 60.8 ± 10.2
3
sdrum.nosnare 45.3 ± 21.9 61.2 ± 7.7 59.4 ± 15.6 65.5 ± 13.3
3
ratchet122.5 ± 30.9 97.6 ± 9.5 30.7 ± 4.5 110.2 ± 3.6 6.5 ± 1.3
rattle138.8 ± 42.6 95.5 ± 1.9 17.5 ± 9.1 101.2 ± 2.7
3pand.skin.frict 43.2 ± 30.5
48 ± 1.6
70.2 ± 6.7 51.7 ± 6.7
1
tamb.tremolo 235.9 ± 91.7 111.9 ± 23.8 35.5 ± 31.7 118.4 ± 2.7 6 ± 0.6
whistle132.8 ± 29.5 98.2 ± 0.4 31.9 ± 13.3 98.4 ± 2.4
3
bassdrum22.6 ± 2327.8 ± 3.8 99.3 ± 21.6 30.5 ± 8.7 1 ± 0.3
gong.tuned
24.2 ± 17.8
61 ± 2.9
76.4 ± 31 68.2 ± 7.6 3 ± 0.2
sound

diss

Acknowledgments. This work has been supported by the Brazilian research agency
CNPq (National Council for Scientific and Technological Development).

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