Antoine, A

Miranda, ER

Williams, D

Searching for audio samples within a library can be a tedious
and time-consuming task. In this paper, we report on the design of a pilot automatic classification system that utilises timbral properties to automatically classify audio samples. At
this stage of the study, we have decided to work only with
orchestral audio samples. In addition, we conducted a perceptual experiment to evaluate the performance of the system
across five timbral attributes: breathiness, brightness, dullness, roughness and warmth. Promising classification results
indicate that this approach may be suitable for further work
that could also benefit some music production tasks

Miranda, Eduardo

Plymouth Electronic Archive and Research Library

Proceedings of the 2nd AES Workshop on Intelligent Music Production, London, UK, 13 September 2016TOWARDS A TIMBRAL CLASSIFICATION SYSTEM FORMUSICAL EXCERPTSAure´lien Antoine, Duncan Williams and Eduardo MirandaInterdisciplinary Centre for Computer Music Research (ICCMR)University of Plymouth, UKaurelien.antoine@postgrad.plymouth.ac.ukABSTRACTSearching for audio samples within a library can be a tediousand time-consuming task. In this paper, we report on the de-sign of a pilot automatic classification system that utilises tim-bral properties to automatically classify audio samples. Atthis stage of the study, we have decided to work only withorchestral audio samples. In addition, we conducted a per-ceptual experiment to evaluate the performance of the systemacross five timbral attributes: breathiness, brightness, dull-ness, roughness and warmth. Promising classification resultsindicate that this approach may be suitable for further workthat could also benefit some music production tasks.1. INTRODUCTIONSearching for audio samples within a large library can be atedious and time-consuming task for composers and sounddesigners. To assist them in this process, they can utilise newdevelopments in music information retrieval (MIR)–for a sur-vey of MIR systems the reader is referred to [1]. The sys-tem presented in this paper utilises timbre properties to auto-matically classify audio samples. Timbre represents a com-plex musical property that is often defined as all the soundproperties except pitch, loudness and duration, which allowus to distinguish and recognise the sound of two differentsounds [2]. Furthermore, several works have demonstratedthe importance of acoustic features in the definition of thismultidimensional attribute [3, 4].Our initial aim for developing this system was to over-come the time-consuming task of listening to a large soundfile database when composing. This search and listening taskmay be part of the compositional process, but it can also be asituation that composers would prefer to circumvent or makemore efficient. However, a timbre-based classification systemcould also have applications for music production. For exam-ple, it could aid sound engineers in evaluating their mixings.The perceptual evaluation of timbral qualities utilised in thissystem could also be implemented in intelligent systems formusic production.In order to make this system user-friendly, we targetedverbal descriptors of timbral qualities by means of their un-derlying acoustic correlates, which can sometimes be com-plex and potentially overlapping. Terms like brightness orroughness are words from everyday language used to describeperceived musical timbres. These terms are more intuitivethan their acoustic correlates (e.g. spectral centroid, criticalbands).2. THE CLASSIFICATION SYSTEMThe prototype system is implemented in the Matlab envi-ronment with a simple user interface. It currently integratesfive timbral attributes: breathiness, brightness, dullness, rough-ness and warmth. We calculate the timbral index as follow:Breathiness. Fundamental amplitude against noise content andthe spectral slope [5]. The bigger the ratio between fundamentalamplitude and the noise content, the breathier the sound.Brightness. Spectral centroid [6]. The higher the spectral cen-troid, the brighter the sound.Dullness. Spectral centroid [7]. A low spectral centroid valueindicates that the sound is dull.Roughness. Distance between adjacent partials in critical band-widths and also the energy above the 6th harmonic [3].Warmth. Spectral centroid and energy in its first three harmon-ics [2]. A low spectral centroid and a high energy in the first threeharmonics indicate that the sound is warm.To use the system, the user must first define the directorycontaining the audio files. Next, the system analyses eachfile and uses the acoustic correlates mentioned previously tocalculate its timbral index for each attribute. File names andtimbral indexes are then stored in matrices (one matrix perattribute), which are sorted in ascending order at the end, toreturn the best results at the top.3. PILOT STUDYIn order to evaluate the accuracy of our classification system,we decided to run an experiment with human participants todetermine the correlation between the humans’ responses andthe system’s ratings. This perceptual evaluation was also de-signed to test the efficiency of the acoustic analysis whenworking with polyphonic timbre (timbral mixture emergingfrom several instruments).3.1. MethodTraining files. As training sources for the implementation ofthe system, we used 90 sound files generated beforehand withLicensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).Proceedings of the 2nd AES Workshop on Intelligent Music Production, London, UK, 13 September 2016Orchids1, a computer-aided orchestration program devel-oped by IRCAM2. These sound files consisted of short audiosamples of orchestration (≈ 2 to 4 seconds) and composedof several orchestral instruments, including violins, flutes andtrumpets.Stimuli. For the listening test, we decided to use 3 soundfiles for each timbral attribute. These stimuli were taken fromthe 90 orchestral sound files mentioned previously. For eachattribute, we have the selected the files rated as the best, medi-um and worst results according to the classification performedby the system.Procedure. We conducted the perceptual evaluation with20 individuals, in the same room and using the same playbackequipment (circumaural headphones). Each participant had tolisten to 15 sound samples in total, which consisted of 3 soundsamples (≈ 2 to 4 seconds each) for each timbral attribute.For each attribute, participants were asked to rate the 3 au-dio samples using the Verbal Attribute Magnitude Estimation(VAME) rating method [8], which quantifies the applicabilityof the descriptor (e.g., bright ↔ not bright), on a five-pointLikert scale.3.2. Results and DiscussionsDue to space limitations, graphic representations of this stu-dy’s results are available on the internet at the following ad-dress: http://goo.gl/YkdOA6.For the timbral attribute breathiness, brightness and dull-ness, participants’ responses and the system’s rating were sim-ilar for each sample. It appears to be a correlation betweenthe participants’ responses and the classification system. Themean and standard deviation for each sample for breathiness,brightness and dullness support this interpretation.For the attribute warmth, we can note that participants re-sponses were similar to the system’s rating for the sampleidentified as best result. However, there is a difference for thetwo other samples. This will require further investigation onour calculations for the warmth index and also on the soundsamples selected in order to identify the reason participantsrated these two samples similarly while there was a differencein the system’s rating.Finally, for the attribute roughness, participants rated thesample identified as medium result as the roughest sound whilethe system suggested a different sample. We can note that par-ticipants’ responses and the system’s rating were similar forthe sample identified as the least rough. However, these re-sults do not validate our calculations for the attribute rough-ness and further investigations are required to improve oursystem’s rating for this attribute.1http://www.forumnet.ircam.fr/product/orchids-en/2Institut de Recherche et Coordination Acoustique/Musique4. CONCLUSIONS AND FURTHERWORKIn this paper, we have presented a computer system developedto classify musical excerpts according to five verbal descriptorof timbral qualities. The prototype classification system is de-veloped in Matlab with five timbral attributes currently im-plemented: breathiness, brightness, dullness, roughness andwarmth. To evaluate the performances of the system, we rana preliminary perceptual study with 20 individuals, using or-chestral audio samples generated by Orchids. However, thesame method could be applied with any source sound set (e.g.synthesised sounds).Listener’s ratings were in good agreement with the sys-tem’s classification for the attributes breathiness, brightnessand dullness. For the attributes roughness and warmth, theresults showed a high amount of inter-participant variationin the listeners responses. This could be addressed by in-creasing the number of participants but at the pilot stage ofthis research, the high number of participants which wouldbe required (likely hundreds) is not practical. Therefore wepropose that this variation be addressed in further work byimproving the classification system in response to participantratings.Nevertheless, these early results are promising with re-spect to the success of four of the five selected attributes, par-ticularly given the absence of fully agreed or realised metrics.We think such a system facilitating the use of a particular au-dio descriptor to estimate attributes could be useful in musicalcomposition as well as in intelligent music production tasks.5. REFERENCES[1] M. Schedl, E. Go´mez, and J. Urbano, “Music information retrieval: Re-cent developments and applications,” Foundations and Trends R© in In-formation Retrieval, vol. 8, no. 2-3, pp. 127–261, 2014.[2] R. Pratt and P. Doak, “A subjective rating scale for timbre,” Journal ofSound and Vibration, vol. 45, no. 3, pp. 317–328, 1976.[3] H. Fastl and E. Zwicker, Psychoacoustics: Facts and models, vol. 22.Springer Science & Business Media, 2007.[4] J. F. Schouten, “The perception of timbre,” in Reports of the 6th Interna-tional Congress on Acoustics, vol. 76, p. 10, 1968.[5] M. A. Landera and R. Shrivastav, “Effects of spectral slope on perceivedbreathiness in vowels,” The Journal of the Acoustical Society of America,vol. 119, no. 5, pp. 3339–3340, 2006.[6] E. Schubert, J. Wolfe, and A. Tarnopolsky, “Spectral centroid and timbrein complex, multiple instrumental textures,” in Proceedings of the inter-national conference on music perception and cognition, North WesternUniversity, Illinois, pp. 112–116, 2004.[7] D. B. Bolger, Computational Models of Musical Timbre and the Analysisof its structure in Melody. PhD thesis, University of Limerick, 2004.[8] R. A. Kendall and E. C. Carterette, “Verbal attributes of simultaneouswind instrument timbres: I. von bismarck’s adjectives,” Music Percep-tion: An Interdisciplinary Journal, vol. 10, no. 4, pp. 445–467, 1993.Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Towards a Timbral Classification System for Musical Excerpts

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