Olivier Lartillot

Segmentation, Transcription, Analysis and Visualisation of the Norwegian Folk Music Archive
Presented at the 9th International Conference on Digital Libraries for Musicology, part of the annual conference of the International Association of Music Libraries (IAML)
We present an ongoing project dedicated to the transmutation of a collection of field recordings of Norwegian folk music established in the 1960s into an easily accessible online catalogue augmented with advanced music technology and computer musicology tools. We focus in particular on a major highlight of this collection: Hardanger fiddle music.

MusicLab 8: Synaesthesia

We explored the synaesthesia of guitarist and artistic researcher Bjørn Charles Dreyer in a multimodal and interactive concert based on new technologies specifically developed for this collaboration. You can watch the concert, followed by a panel discussion and a technical demonstration:

MIRAGE Symposium #1: Computational Musicology

The 1st MIRAGE Symposium, which took place on 8-9 June, 2021, is available on replay.

New method for computational analysis of sound chosen as JASA highlight

Article about new method for attack detection chosen as a highlight by The Journal of the Acoustical Society of America (JASA).

Computational Musicological Analysis of Notated Music: A Brief Overview

A chapter from the newly released book "Notated Music in the Digital Sphere: Possibilities and Limitations", Nota Bene 15

Artificial intelligence can help you understand music better

Algorithms and technology have so far helped listeners to more of the same music. Now, UiO researchers are working on new technology that can get people interested in a greater musical variety.

• Danielsen, Anne; Brøvig, Ragnhild; Bøhler, Kjetil Klette; Câmara, Guilherme Schmidt; Haugen, Mari Romarheim & Jacobsen, Eirik [Show all 13 contributors for this article] (2024). There’s More to Timing than Time: Investigating Musical Microrhythm Across Disciplines and Cultures. Music Perception. ISSN 0730-7829. 41(3), p. 176–198. doi: 10.1525/mp.2024.41.3.176. Full text in Research Archive Show summary

  The TIME project: Timing and Sound in Musical Microrhythm (2017–2022) studied microrhythm; that is, how dynamic envelope, timbre, and center frequency, as well as the microtiming of a variety of sounds, affect their perceived rhythmic properties. The project involved theoretical work regarding the basic aspects of microrhythm; experimental studies of microrhythm perception, exploring both stimulus features and the participants’ enculturated expertise; observational studies of how musicians produce particular microrhythms; and ethnographic studies of musicians’ descriptions of microrhythm. Collectively, we show that: (a) altering the microstructure of a sound (“what” the sound is) changes its perceived temporal location (“when” it occurs), (b) there are systematic effects of core acoustic factors (duration, attack) on microrhythmic perception, (c) microrhythmic features in longer and more complex sounds can give rise to different perceptions of the same sound, and (d) musicians are highly aware of microrhythms and have developed vocabularies for describing them. In addition, our results shed light on conflicting results regarding the effect of microtiming on the “grooviness” of a rhythm. Our use of multiple, interdisciplinary methodologies enabled us to uncover the complexity of microrhythm perception and production in both laboratory and real-world musical contexts.

• Thedens, Hans-Hinrich & Lartillot, Olivier (2023). AudioSegmentor: A tool for disseminating archival recordings online. Studia Musicologica Norvegica. ISSN 0332-5024. 49(1), p. 92–101. doi: 10.18261/smn.49.1.7. Full text in Research Archive Show summary

  Much of the holdings of Norwegian folk music archives has already or will soon enter the public domain and can then be distributed freely to users through online catalogues. But for many the challenge arises that digitized tapes are not indexed with start times and duration of parts that should be associated with catalogue posts, like recordings of single songs and tunes. The music information retrieval project MIRAGE has now developed tools that can help this process through automatization. The tools will be available to all others who face similar challenges in their online presentation of sound recordings.

• Lartillot, Olivier; Johansson, Mats Sigvard; Elowsson, Anders; Monstad, Lars Løberg & Cyvin, Mattias Storås (2023). A Dataset of Norwegian Hardanger Fiddle Recordings with Precise Annotation of Note and Beat Onsets. Transactions of the International Society for Music Information Retrieval. ISSN 2514-3298. 6(1), p. 186–202. doi: 10.5334/TISMIR.139.
• Maidhof, Clemens; Müller, Viktor; Lartillot, Olivier; Agres, Kat; Bloska, Jodie & Asano, Rie [Show all 8 contributors for this article] (2023). Intra- and inter-brain coupling and activity dynamics during improvisational music therapy with a person with dementia: an explorative EEG-hyperscanning single case study. Frontiers in Psychology. ISSN 1664-1078. 14. doi: 10.3389/fpsyg.2023.1155732.
• Bishop, Laura; Høffding, Simon; Lartillot, Olivier Serge Gabriel & Laeng, Bruno (2023). Mental Effort and Expressive Interaction in Expert and Student String Quartet Performance. Music & Science. ISSN 2059-2043. 6. doi: 10.1177/20592043231208000.
• Szorkovszky, Alexander; Veenstra, Frank; Lartillot, Olivier Serge Gabriel; Jensenius, Alexander Refsum & Glette, Kyrre (2023). Embodied Tempo Tracking with a Virtual Quadruped, Proceedings of the Sound and Music Computing Conference 2023. SMC Network . ISSN 978-91-527-7372-7. doi: 10.5281/zenodo.10060970. Full text in Research Archive
• Lartillot, Olivier; Elovsson, Anders; Johansson, Mats Sigvard; Thedens, Hans-Hinrich & Monstad, Lars Alfred Løberg (2022). Segmentation, Transcription, Analysis and Visualisation of the Norwegian Folk Music Archive. In Pugin, Laurent (Eds.), DLfM '22: 9th International Conference on Digital Libraries for Musicology. Association for Computing Machinery (ACM). ISSN 978-1-4503-9668-4. p. 1–9. doi: https:/doi.org/10.1145/3543882.3543883. Full text in Research Archive Show summary

  We present an ongoing project dedicated to the transmutation of a collection of field recordings of Norwegian folk music established in the 1960s into an easily accessible online catalogue augmented with advanced music technology and computer musicology tools. We focus in particular on a major highlight of this collection: Hardanger fiddle music. The studied corpus was available as a series of 600 tape recordings, each tape containing up to 2 hours of recordings, associated with metadata indicating approximate positions of pieces of music. We first need to retrieve the individual recording associated with each tune, through the combination of an automated pre-segmentation based on sound classification and audio analysis, and a subsequent manual verification and fine-tuning of the temporal positions, using a home-made user interface. Note detection is carried out by a deep learning method. To adapt the model to Hardanger fiddle music, musicians were asked to record themselves and annotate all played note, using a dedicated interface. Data augmentation techniques have been designed to accelerate the process, in particular using alignment of varied performances of same tunes. The transcription also requires the reconstruction of the metrical structure, which is particularly challenging in this style of music. We have also collected ground-truth data, and are conceiving a computational model. The next step consists in carrying out detailed music analysis of the transcriptions, in order to reveal in particular intertextuality within the corpus. A last direction of research is aimed at designing tools to visualise each tune and the whole catalogue, both for musicologists and general public.

• Juslin, Patrik N.; Sakka, Laura S.; Barradas, Gonçalo T. & Lartillot, Olivier (2022). Emotions, mechanisms, and individual differences in music listening: A stratified random sampling approach. Music Perception. ISSN 0730-7829. 40(1), p. 55–86. doi: 10.1525/mp.2022.40.1.55. Full text in Research Archive
• Weisser, Stéphanie; Lartillot, Olivier & Sechehaye, Hélène (2021). Investiguer la grésillance. Pour une approche ethno-acoustique du timbre musical. Cahiers d'ethnomusicologie. ISSN 2235-7688. 34, p. 37–58. Show summary

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• Elovsson, Anders & Lartillot, Olivier (2021). A Hardanger Fiddle Dataset with Performances Spanning Emotional Expressions and Annotations Aligned using Image Registration, Proceedings of the 22nd International Society for Music Information Retrieval Conference, Online, Nov 7-12, 2021. International Society for Music Information Retrieval. ISSN 978-1-7327299-0-2. p. 174–181. Full text in Research Archive Show summary

  This paper presents a Hardanger fiddle dataset “HF1” with polyphonic performances spanning five different emotional expressions: normal, angry, sad, happy, and tender. The performances thus cover the four quadrants of the activity/valence-space. The onsets and offsets, together with an associated pitch, were human-annotated for each note in each performance by the fiddle players themselves. First, they annotated the normal version. These annotations were then transferred to the expressive performances using music alignment and finally human-verified. Two separate music alignment methods based on image registration were developed for this purpose; a B-spline implementation that produces a continuous temporal transformation curve and a Demons algorithm that produces displacement matrices for time and pitch that also account for local timing variations across the pitch range. Both methods start from an “Onsetgram” of onset salience across pitch and time and perform the alignment task accurately. Various settings of the Demons algorithm were further evaluated in an ablation study. The final dataset is around 43 minutes long and consists of 19 734 notes of Hardanger fiddle music, recorded in stereo. The dataset and source code are available online. The dataset will be used in MIR research for tasks involving polyphonic transcription, score alignment, beat tracking, downbeat tracking, tempo estimation, and classification of emotional expressions.

• Lartillot, Olivier (2021). Computational Musicological Analysis of Notated Music: a Brief Overview. Nota Bene. ISSN 1891-4829. 15, p. 142–161. Full text in Research Archive Show summary

  I present a short overview of computational methods for musicological analysis of notated music. We first need to clarify the various levels of computational representations of music: on one side, notated music, on the other, audio recordings, and in the middle, a note-level representa- tion of music performance where higher-level musical descriptions are absent. The article provides a synthetic and partial panorama of the different types of music analysis that have been systematised and auto- mated using computers. While pioneering works were mainly focused on statistical descriptions of the surface of music, other dimensions of music analysis such as harmony, metre and structure have been taken into consideration since. I conclude by sketching my personal vision of the future of computational music analysis.

• Lartillot, Olivier; Nymoen, Kristian; Câmara, Guilherme Schmidt & Danielsen, Anne (2021). Computational localization of attack regions through a direct observation of the audio waveform. Journal of the Acoustical Society of America. ISSN 0001-4966. 149(1), p. 723–736. doi: 10.1121/10.0003374. Show summary

  This article addresses the computational estimation of attack regions in audio recordings. Previous attempts to do so were based on the reduction of the audio waveform into an envelope curve, which decreases its temporal resolution. The proposed approach detects the attack region directly from the audio waveform. The attack region is modeled as a line starting from a low-amplitude point and intersecting one of the local maxima according to two principles: (1) maximizing the slope, while favoring, at the same time, a higher peak if the slope remains only slightly lower and (2) dismissing initial attack regions of relatively low amplitude. The attack start position is fine-tuned by intersecting the attack slope with the audio waveform. The proposed method precisely pinpoints the attack region in cases where it is unambiguously observable from the waveform itself. In such cases, previous methods selected a broader attack region due to the loss of temporal resolution. When attack regions are less evident, the proposed method’s estimation remains within the range of results provided by other methods. Applied to the prediction of judgments of P-center localization [Danielsen, Nymoen, Anderson, C^amara, Langerød, Thompson, and London, J. Exp. Psychol. Hum. Percept. Perform. 45, 402–418 (2019)], the proposed method shows a significant increase in precision, at the expense of recall.

• Bruford, Fred & Lartillot, Olivier (2020). Multidimensional similarity modelling of complex drum loops using the GrooveToolbox, Proceedings of the 21st International Society for Music Information Retrieval (ISMIR) Conference. McGill-Queen's University Press. ISSN 978-0-9813537-0-8. p. 263–270. Full text in Research Archive Show summary

  The GrooveToolbox is a new Python toolbox implementing various algorithms, new and pre-existing, for the analysis and comparison of symbolic drum loops, including rhythm features, similarity metrics and microtiming features. As part of the GrooveToolbox we introduce two new metrics of rhythm similarity and four features for describing the significant properties of microtiming deviations in drum loops. Based on a two-part perceptual evaluation, we show these four new microtiming features can each correlate to similarity perception, and be used with rhythm similarity metrics to improve personalized similarity models for drum loops. A new measure of structural rhythmic similarity is also shown to correlate more strongly to similarity perception of drum loops than the more com- monly used Hamming distance. These results point to the potential application of the GrooveToolbox and its new features in drum loop analysis for intelligent music production tools. The GrooveToolbox may be found at: https://github.com/fredbru/GrooveToolbox

• Lartillot, Olivier & Bruford, Fred (2020). Bistate reduction and comparison of drum patterns, Proceedings of the 21st International Society for Music Information Retrieval (ISMIR) Conference. McGill-Queen's University Press. ISSN 978-0-9813537-0-8. p. 318–324. Full text in Research Archive Show summary

  This paper develops the hypothesis that symbolic drum patterns can be represented in a reduced form as a sim- ple oscillation between two states, a Low state (commonly associated with kick drum events) and a High state (often associated with either snare drum or high hat). Both an onset time and an accent time is associated to each state. The systematic inference of the reduced form is formal- ized. This enables the specification of a rhythmic struc- tural similarity measure on drum patterns, where reduced patterns are compared through alignment. The two-state representation allows a low computational cost alignment, once the complex topological formalization is fully taken into account. A comparison with the Hamming distance, as well as similarity ratings collected from listeners on a drum loop dataset, indicates that the bistate reduction enables to convey subtle aspects that goes beyond surface-level com- parison of rhythmic textures.

• Lartillot, Olivier; Cancino-Chacón, Carlos & Brazier, Charles (2020). Real-Time Visualisation Of Fugue Played By A String Quartet. In Spagnol, Simone & Valle, Andrea (Ed.), Proceedings of the 17th Sound and Music Computing Conference. Axea sas/SMC Network. ISSN 978-88-945415-0-2. p. 115–122. Full text in Research Archive Show summary

  We present a new system for real-time visualisation of music performance, focused for the moment on a fugue played by a string quartet. The basic principle is to offer a visual guide to better understand music using strategies that should be as engaging, accessible and effective as possible. The pitch curves related to the separate voices are drawn on a space whose temporal axis is normalised with respect to metrical positions, and aligned vertically with respect to their thematic and motivic classification. Aspects related to tonality are represented as well. We describe the underlying technologies we have developed and the technical setting. In particular, the rhythmical and structural representation of the piece relies on real-time polyphonic audio-to-score alignment using online dynamic time warping. The visualisation will be presented at a concert of the Danish String Quartet, performing the last piece of The Art of Fugue by Johann Sebastian Bach.

• Câmara, Guilherme Schmidt; Nymoen, Kristian; Lartillot, Olivier & Danielsen, Anne (2020). Timing Is Everything... Or Is It? Effects of Instructed Timing Style, Reference and Pattern on Drum Kit Sound in Groove-Based Performance. Music Perception. ISSN 0730-7829. 38(1), p. 1–26. doi: 10.1525/mp.2020.38.1.1. Full text in Research Archive
• Câmara, Guilherme Schmidt; Nymoen, Kristian; Lartillot, Olivier & Danielsen, Anne (2020). Effects of instructed timing on electric guitar and bass sound in groove performance. Journal of the Acoustical Society of America. ISSN 0001-4966. 147(2), p. 1028–1041. doi: 10.1121/10.0000724. Show summary

  This paper reports on two experiments that investigated the expressive means through which musicians well versed in groove-based music signal the intended timing of a rhythmic event. Data were collected from 21 expert electric guitarists and 21 bassists, who were instructed to perform a simple rhythmic pattern in three different timing styles—“laidback,” “on-the-beat,” and “pushed”—in tandem with a metronome. As expected, onset and peak timing locations corresponded to the instructed timing styles for both instruments. Regarding sound, results for guitarists revealed systematic differences across participants in the duration and brightness [spectral centroid (SC)] of the guitar strokes played using these different timing styles. In general, laid-back strokes were played with a longer duration and a lower SC relative to on-the-beat and pushed strokes. Results for the bassists indicated systematic differences in intensity (sound-pressure level): pushed strokes were played with higher intensity than on-the-beat and laid-back strokes. These results lend further credence to the hypothesis that both temporal and sound-related features are important indications of the intended timing of a rhythmic event, and together these features offer deeper insight into the ways in which musicians communicate at the microrhythmic level in groove-based music.

• Alarcón Diaz, Ximena; Bojorquez, Lucia Nikolaia López; Lartillot, Olivier & Flamtermesky, Helga (2019). From collecting an archive to artistic practice in the intimal project lessons learned from listening to a colombian migrant women’s oral history archive. Acervo. ISSN 0102-700X. 32(3), p. 48–63.
• Lartillot, Olivier & Grandjean, Didier (2019). Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation. Applied Sciences. ISSN 2076-3417. 9(23). doi: 10.3390/app9235121. Full text in Research Archive Show summary

  We present a method for tempo estimation from audio recordings based on signal processing and peak tracking, and not depending on training on ground-truth data. First, an accentuation curve, emphasizing the temporal location and accentuation of notes, is based on a detection of bursts of energy localized in time and frequency. This enables the detection of notes in dense polyphonic texture, while ignoring spectral fluctuation produced by vibrato and tremolo. Periodicities in the accentuation curve are detected using an improved version of autocorrelation function. Hierarchical metrical structures, composed of a large set of periodicities in pairwise harmonic relationships, are tracked over time. In this way, the metrical structure can be tracked even if the rhythmical emphasis switches from one metrical level to another. This approach, compared to all the other participants to the Music Information Retrieval Evaluation eXchange (MIREX) Audio Tempo Extraction competition from 2006 to 2018, is the third best one among those that can track tempo variations. While the two best methods are based on machine learning, our method suggests a way to track tempo founded on signal processing and heuristics-based peak tracking. Moreover, the approach offers for the first time a detailed representation of the dynamic evolution of the metrical structure. The method is integrated into MIRtoolbox, a Matlab toolbox freely available.

• Alarcón Diaz, Ximena; Lopez Bojórquez, Lucia Nikolaia; Lartillot, Olivier & Flamtermesky, Helga (2019). From collecting an archive to artistic practice in the INTIMAL project: lessons learned from listening to a Colombian migrant women’s oral history archive. Acervo. ISSN 0102-700X. 32(3), p. 48–63. Full text in Research Archive Show summary

  This paper describes a multidisciplinary encounter with oral testimony archives and their incorporation in the artistic research project Intimal. It explores ways in which to creatively listen to stories which might be emotionally challenging, and to create and sustain empathy with these disembodied voices, which contain a shared history as fissures of sixty years of violence.

• Lartillot, Olivier (2019). Miningsuite: A Comprehensive Matlab Framework for Signal, Audio and Music Analysis, Articulating Audio and Symbolic Approaches. In Barbancho, Isabel; Tardón, Lorenzo J.; Peinado, Alberto & Barbancho, Ana M. (Ed.), SMC 2019 Proceedings of the 16th Sound & Music Computing Conference. Society for Sound and Music Computing. ISSN 978-84-09-08518-7. p. 489–489. Full text in Research Archive
• Lartillot, Olivier & Grandjean, Didier (2019). Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation. In Barbancho, Isabel; Tardón, Lorenzo J.; Peinado, Alberto & Barbancho, Ana M. (Ed.), SMC 2019 Proceedings of the 16th Sound & Music Computing Conference. Society for Sound and Music Computing. ISSN 978-84-09-08518-7. p. 174–181. doi: 10.3390/app9235121. Full text in Research Archive
• Lartillot, Olivier; Thedens, Hans-Hinrich & Jensenius, Alexander Refsum (2018). Computational model of pitch detection, perceptive foundations, and application to Norwegian fiddle music. In Parncutt, Richard & Sattmann, Sabrina (Ed.), Proceedings of ICMPC15/ESCOM10. Centre for Systematic Musicology, University of Graz. ISSN 978-3-200-05771-5. p. 252–255. Full text in Research Archive Show summary

  Background Automated detection of pitch in polyphonic music remains a difficult challenge (Benetos et al., 2013). Robust solutions can be found for simple cases such as monodies. Implementation of perceptive/cognitive models have been so far less successful than engineering methods, and in particular machine learning models. One reference model (Klapuri, 2006) preselects pitch candidates based on harmonic summation and searches for multiple pitches through cancellation. Aims The aim was to conceive a model for pitch detection in polyphonic music able to transcribe in details traditional Norwegian music played on Hardanger fiddle, where more than two strings are played at the same time. The new model should be applicable to other types of music as well. Perceptive and cognitive models should guide the improvement of the state of the art. Main Contribution The model is neither based on a machine-learning training on a given set of samples, nor explicitly relying on stylistic rules. Instead, the methodology consists in conceiving a set of rules as simple and general as possible while offering satisfying results for the chosen corpus of music. We follow some general principles of the model by (Klapuri 2006) while introducing new heuristics. We present a new method for harmonic summation that penalises harmonic series that are sparse, in particular when odd partials are absent, as it would indicate that the actual harmonic series is a multiple of the given pitch candidate. Besides, a multiple of a fundamental can be selected as pitch in addition to the fundamental itself if its attack phase is sufficiently distinctive. For that purpose, we introduce a concept of pitch percept that persists over the whole extent of the note, and that serves as a reference for the detection of higher pitches at harmonic intervals. Results The proposed method enables to obtain transcriptions of relatively good quality, with a low ratio of false positives and false negatives. The construction of the model is under refinement. We are applying this method to the analysis of recordings of Norwegian folk music, containing a large part of Hardinger fiddle pieces and a cappella singing. Implications Automated transcription is of high interest for musicology and music information retrieval. This enables for instance to build large corpora of scores for music analysis and opens news perspectives for computational musicology. By attempting to design computer models based on general rules as simple as possible rather than on machine learning, while resulting in a behaviour in terms of pitch detection that comes closer to human capabilities, we hypothesise that the underlying mechanisms thus modelled might suggest general computational capabilities that could be found in cognitive models as well. In the same time, an improvement of the model based on expertise in music perception and cognition is desired. References Benetos et al. (2013). Automatic music transcription: challenges and future directions. Journal of Intelligent Information Systems, 41, 407-434 Klapuri, Multiple Fundamental Frequency Estimation by Summing Harmonic Amplitudes. ISMIR 2006 Keywords: pitch, computational model, harmonic summation, Norwegian folk music, Hardanger fiddle.

View all works in Cristin

• Monstad, Lars Løberg & Lartillot, Olivier (2023). Automatic Transcription Of Multi-Instrumental Songs: Integrating Demixing, Harmonic Dilated Convolution, And Joint Beat Tracking. Show summary

  In the rapidly expanding field of music information retrieval (MIR), automatic transcription remains one of the most sought-after capabilities, especially for songs that employ multiple instruments. Musscribe emerges as a state-of-the-art transcription tool that addresses this challenge by integrating three distinct methodologies: demixing, harmonic dilated convolution, and joint beat tracking. Demixing is employed to isolate individual instruments within a song by separating overlapping audio sources, thus ensuring each instrument is transcribed distinctly. Beat tracking is then run as a parallel process to extract the joint beat and downbeat estimations. These processes results in an output midi file, which is then quantized using information derived from the beat tracking. As such, this method paves the way for more accurate and sophisticated analyses, bridging the gap between human and machine understanding of music. Together, these methodologies allow us to produce transcriptions that are not only accurate but also highly representative of the original compositions. Preliminary tests and evaluations showcase the potential in transcribing complex musical pieces with high fidelity, outperforming many contemporary tools in the market. This innovative approach not only has implications for music transcription but also for broader applications in audio analysis, remixing, and digital music production. The model has been instrumental in accelerating the composition process for several Norwegian television shows. Moreover, its efficacy can be observed in the Netflix series "A Storm for Christmas." Renowned composer Peter Baden harnessed this tool to enhance his workflow, proving the demand for innovative tools like this in the professional music industry.

• Maidhof, Clemens; Agres, Kat; Fachner, Jörg & Lartillot, Olivier (2023). Intra- and inter-brain coupling during music therapy.
• Wosch, Thomas; Vobig, Bastian; Lartillot, Olivier & Christodoulou, Anna-Maria (2023). HIGH-M (Human Interaction assessment and Generative segmentation in Health and Music).
• Lartillot, Olivier (2023). Music Therapy Toolbox, and prospects.
• Lartillot, Olivier; Swarbrick, Dana; Upham, Finn & Cancino-Chacón, Carlos Eduardo (2023). Video visualization of a string quartet performance of a Bach Fugue: Design and subjective evaluation.
• Bishop, Laura; Høffding, Simon; Laeng, Bruno & Lartillot, Olivier (2023). Mental effort and expressive interaction in expert and student string quartet performance.
• Lartillot, Olivier (2023). MIRAGE Symposium #2: Music, emotions, analysis, therapy ... and computer. Show summary

  The 2nd MIRAGE Symposium covers a broad range of topics related to the MIRAGE project, mainly related to music and emotion, music cognition in general, music analysis and music therapy. Featuring two keynotes by Patrik Juslin and Didier Grandjean.

• Lartillot, Olivier; Thedens, Hans-Hinrich; Mjelva, Olav Luksengård; Elovsson, Anders; Monstad, Lars Løberg & Johansson, Mats Sigvard [Show all 8 contributors for this article] (2023). Norwegian Folk Music & Computational Analysis. Show summary

  As a prélude for Norway's Constitution Day, this special event celebrated the Norwegian folk music tradition, showcasing our new online archive and demonstrating the richness of Hardanger fiddle music, with live performance. One aim of the project is to conceive new technologies allowing to better access, understand and appreciate Norwegian folk music. In this event, we introduced a new online version of the Norwegian Folk Music Archive and discuss underlying theoretical and technical challenges. A live concert/workshop, with the participation of Olav Luksengård Mjelva, offered a lively introduction to Hardanger fiddle music and its elaborate rhythm. The interests and challenges of automated transcription and analysis were discussed, with the public release of our new software Annotemus. The symposium was organised in the context of the MIRAGE project (RITMO, in collaboration with the National Library of Norway's Digital Humanities Laboratory).

• Lartillot, Olivier & Monstad, Lars Løberg (2023). Computational music analysis: Significance, challenges, and our proposed approach. Show summary

  Music is something that we mostly all appreciate, yet it remains a hidden and enigmatic concept for many of us. Music notation, in the form of music scores, facilitates practicing and enhances the understanding of the richness of musical works. However, acquiring musical scores for any music performance is a tedious and demanding task (called music transcription) that demands considerable proficiency. Hence the interest of computational automation. But music is not just notes, it is also melody, rhythm, themes, timbre, and very subtle aspects such as form. While many of us may not be consciously familiar with these concepts, they still have a subconscious influence on our aesthetic experience. Interestingly, it often happens that the more we consciously understand the underlying language of music, the more we tend to appreciate and enjoy it. Therefore, there is value in creating computational tools that can automate and enhance these types of analyses. The presenters' past work resulted in the creation of Matlab's MIRtoolbox, which measures a broad range of musical characteristics directly from audio through signal processing techniques. Currently, the MIRAGE project prioritises music transcription (with a particular focus on Norwegian folk music), blending neural-network-based deep learning with conventional rule-based models. Through this project, they highlight the importance of acknowledging the interconnectedness between all musical elements. Additionally, they have crafted animated visualisations to make analyses more accessible to the general public and are aiming to make music transcription technology available to the public, with support from UiO Growth House.

• Christodoulou, Anna-Maria; Lartillot, Olivier & Anagnostopoulou, Christina (2023). Greek Folk Music Dataset.
• Lartillot, Olivier (2023). Dynamic Visualisation of Fugue Analysis, Demonstrated in a Live Concert by the Danish String Quartet.
• Lartillot, Olivier (2023). Towards a comprehensive model for computational music transcription and analysis: a necessary dialog between machine learning and rule-based design?
• Lartillot, Olivier & Monstad, Lars Løberg (2023). MIRAGE - A Comprehensive AI-Based System for Advanced Music Analysis.
• Lartillot, Olivier (2023). Computational audio and musical features extraction: from MIRtoolbox to the MiningSuite.
• Bishop, Laura; Høffding, Simon; Lartillot, Olivier Serge Gabriel & Laeng, Bruno (2023). Mental effort and expressive interaction in expert and student string quartet performance.
• Christodoulou, Anna-Maria; Lartillot, Olivier & Anagnostopoulou, Christina (2023). Computational Analysis of Greek Folk Music of the Aegean.
• Lartillot, Olivier (2023). Towards a Comprehensive Modelling Framework for Computational Music Transcription/Analysis. Show summary

  Computational music analysis, still in its infancy, lacking overarching reliable tools, can be seen at the same time as a promising approach to fulfill core epistemo- logical needs. Analysis in the audio domain, although approaching music in its entirety, is doomed to superficiality if it does not fully embrace the underlying symbolic system, requiring a complete automated transcription and scaffolding of metrical, modal/harmonic, voicing and formal structures on top of the layers of elementary events (such as notes). Automated transcription enables to get over the polarity between sound and music notation, providing an interfacing semiotic system that combines the advantages of both domains, and surpassing the limitation of traditional approaches based on graphic representations. Deep learning and signal processing approaches for the discretisation of the continuous signal are compared and discussed. The multi-dimensional music transcription and analysis framework (where both tasks are actually deeply intertwined) requires to take into account the far-reaching interdependencies between dimensions, for instance between motivic and metrical analysis. We propose an attempt to build such a comprehensive framework, founded on general musical and cognitive principles and an attempt to build music analysis capabilities through a combina- tion of simple and general operators. The validity of the analyses is addressed in close discussion with music experts. The potential capability to produce valid analyses for a very large corpus of music would make such a complex system a potentially relevant blueprint for a cognitive modelling of music understanding. We try to address a large diversity of music cultures and their specific challenges: among others, maqam modes (with Mondher Ayari), Norwegian Hardanger fiddle rhythm (with Mats Johansson and Hans-Hinrich Thedens), djembe drumming from Mali (with Rainer Polak) or electroacoustic music (Towards a Toolbox des objets musicaux, with Rolf Inge Godøy). We aim at making the framework fully transparent, collaborative and open.

• Lartillot, Olivier (2022). Computational music analysis: Application to music & emotion.
• Lartillot, Olivier (2022). The MIRAGE project: Unlocking new computational abilities in computational music analysis.
• Lartillot, Olivier & Thedens, Hans-Hinrich (2022). Online Norwegian Folk Music Archive.
• Lartillot, Olivier; Elovsson, Anders; Johansson, Mats Sigvard; Thedens, Hans-Hinrich & Monstad, Lars Alfred Løberg (2022). Segmentation, Transcription, Analysis and Visualisation of the Norwegian Folk Music Archive. Show summary

  We present an ongoing project dedicated to the transmutation of a collection of field recordings of Norwegian folk music established in the 1960s into an easily accessible online catalogue augmented with advanced music technology and computer musicology tools. We focus in particular on a major highlight of this collection: Hardanger fiddle music. The studied corpus was available as a series of 600 tape recordings, each tape containing up to 2 hours of recordings, associated with metadata indicating approximate positions of pieces of music. We first need to retrieve the individual recording associated with each tune, through the combination of an automated pre-segmentation based on sound classification and audio analysis, and a subsequent manual verification and fine-tuning of the temporal positions, using a home-made user interface. Note detection is carried out by a deep learning method. To adapt the model to Hardanger fiddle music, musicians were asked to record themselves and annotate all played note, using a dedicated interface. Data augmentation techniques have been designed to accelerate the process, in particular using alignment of varied performances of same tunes. The transcription also requires the reconstruction of the metrical structure, which is particularly challenging in this style of music. We have also collected ground-truth data, and are conceiving a computational model. The next step consists in carrying out detailed music analysis of the transcriptions, in order to reveal in particular intertextuality within the corpus. A last direction of research is aimed at designing tools to visualise each tune and the whole catalogue, both for musicologists and general public.

• Lartillot, Olivier; Godøy, Rolf Inge & Christodoulou, Anna-Maria (2022). Computational detection and characterisation of sonic shapes: Towards a Toolbox des objets sonores. Show summary

  Computational detection and analysis of sound objects is of high importance both for musicology and sound design. Yet Music Information Retrieval technologies have so far been mostly focusing on transcription of music into notes in a classical sense whereas we are interested in detecting sound objects and their feature categories, as was suggested by Pierre Schaeffer’s typology and morphology of sound objects in 1966, reflecting basic sound-producing action types. We propose a signal-processing based approach for segmentation, based on a tracking of the salient characteristics over time, and dually Gestalt-based segmentation decisions based on changes. Tracking of pitched sound relies on partial tracking, whereas the analysis of noisy sound requires tracking of larger frequency bands possibly varying over time. The resulting sound objects are then described based on Schaeffer’s taxonomy and morphology, expressed first in the form of numerical descriptors, each related to one type of taxonomy (percussive/sustained/iterative, stable/moving pitch vs unclear pitch) or morphology (such as grain). This multidimensional feature representation is further divided into discrete categories related to the different classes of sounds. The typological and morphological categorisation is driven by the theoretical and experimental framework of the morphodynamical theory. We first experiment on isolated sounds from the Solfège des objets sonores—which features a large variety of sound sources—before considering more complex configurations featuring a succession of sound objects without silence or with simultaneous sound objects. Analytical results are visualised in the form of graphical representations, aimed both for musicology and music pedagogy purposes. This will be applied to the graphical descriptions of and browsing within large music catalogues. The application of the analytical descriptions to music creation is also investigated.

• Danielsen, Anne; Câmara, Guilherme Schmidt; Lartillot, Olivier; Leske, Sabine Liliana & Spiech, Connor (2022). Musical rhythm. Behavioural, computational and neurophysiological perspectives.
• Elovsson, Anders & Lartillot, Olivier (2021). HF1: Hardanger fiddle dataset. Show summary

  HF1 is a Hardanger fiddle dataset with polyphonic performances spanning five different emotional expressions. The onsets and offsets, together with an associated pitch, were human-annotated for each note in each performance by the fiddle players themselves. The dataset is around 43 minutes long and consists of 19 734 notes of Hardanger fiddle music, recorded in stereo.

• Tidemann, Aleksander; Lartillot, Olivier & Johansson, Mats Sigvard (2021). Towards New Analysis And Visualization Software For Studying Performance Patterns in Hardanger Fiddle Music. Show summary

  Analyzing musical performances is a challenging and emergent field of computational music research, aiming to reveal performance patterns and link them to musical contexts. There exists a modest amount of computational research on Hardanger fiddle performances. The MIRAGE research project is currently contributing to this scientific body, developing advanced MIR frameworks that build on recent musicological research. This paper presents the development and evaluation of two Max/MSP/Jitter software applications for music analysis and data visualization that integrate contemporary research perspectives on the complex rhythmical structuring of springar performances, investigating how we can design user-friendly computational tools that explore performance patterns in Hardanger fiddle music, in collaboration with MIRAGE. Based on a small questionnaire and a few operational tests, the study shows an interest in more effective software tools capable of revealing complex interrelations between musical dimensions in Hardanger fiddle performances. Additionally, the study highlights design considerations for tools aiming to increase the availability of computational music research in the field of musicology, such as cross-compatibility and integrated features that actively facilitate nuanced interpretation processes.

• Lartillot, Olivier & Lilleslåtten, Mari (2021). Olivier Lartillot utvikler verktøy for å forstå musikk bedre. [Internet]. Det humanistiske fakultet UiO YouTube account. Show summary

  Kunstig intelligens kan hjelpe deg å forstå musikk bedre. UiO-forsker Olivier Lartillot jobber for at ny teknologi kan åpne folks ører for ny musikk.

• Lartillot, Olivier & Lilleslåtten, Mari (2021). Artificial intelligence can help you understand music better. [Internet]. RITMO News. Show summary

  Algorithms and technology have so far helped listeners to more of the same music. Now, UiO researchers are working on new technology that can get people interested in a greater musical variety.

• Godøy, Rolf Inge & Lartillot, Olivier (2021). Acoustic substrates of musique concrète features: Towards a Toolbox de l'objet musical?
• Lartillot, Olivier; Guldbrandsen, Erling Eliseus & Cancino-Chacón, Carlos Eduardo (2021). Dynamics analysis, and application to a comparative study of Bruckner performances.
• Lartillot, Olivier; Elovsson, Anders & Mjelva, Olav Luksengård (2021). A new software for computer-assisted annotation of music recordings, with a focus on transcription.
• Lartillot, Olivier & Johansson, Mats Sigvard (2021). Tracking beats in Hardanger fiddle tunes .
• Lartillot, Olivier (2021). Presentation of MIRAGE project.
• Elovsson, Anders & Lartillot, Olivier (2021). A Hardanger Fiddle Dataset with Performances Spanning Emotional Expressions and Annotations Aligned using Image Registration. Show summary

  This paper presents a Hardanger fiddle dataset “HF1” with polyphonic performances spanning five different emotional expressions: normal, angry, sad, happy, and tender. The performances thus cover the four quadrants of the activity/valence-space. The onsets and offsets, together with an associated pitch, were human-annotated for each note in each performance by the fiddle players themselves. First, they annotated the normal version. These annotations were then transferred to the expressive performances using music alignment and finally human-verified. Two separate music alignment methods based on image registration were developed for this purpose; a B-spline implementation that produces a continuous temporal transformation curve and a Demons algorithm that produces displacement matrices for time and pitch that also account for local timing variations across the pitch range. Both methods start from an “Onsetgram” of onset salience across pitch and time and perform the alignment task accurately. Various settings of the Demons algorithm were further evaluated in an ablation study. The final dataset is around 43 minutes long and consists of 19 734 notes of Hardanger fiddle music, recorded in stereo. The dataset and source code are available online. The dataset will be used in MIR research for tasks involving polyphonic transcription, score alignment, beat tracking, downbeat tracking, tempo estimation, and classification of emotional expressions.

• Lartillot, Olivier & Weisser, Stéphanie (2021). Roughness, Crackliness, Buzzingness, ...: Characterizations of Sonic Unsteadiness and Application to the Analysis of Traditional Music from Ethiopia, Kenya, Morocco and India.
• Tidemann, Aleksander & Lartillot, Olivier (2021). Interactive tools for exploring performance patterns in hardanger fiddle music.
• Dalgard, Joachim; Lartillot, Olivier; Vuoskoski, Jonna Katariina & Guldbrandsen, Erling Eliseus (2021). Absorption - Somewhere between the heart and the brain.
• Bruford, Fred & Lartillot, Olivier (2020). Multidimensional similarity modelling of complex drum loops using the GrooveToolbox. Show summary

  The GrooveToolbox is a new Python toolbox implementing various algorithms, new and pre-existing, for the analysis and comparison of symbolic drum loops, including rhythm features, similarity metrics and microtiming features. As part of the GrooveToolbox we introduce two new metrics of rhythm similarity and four features for describing the significant properties of microtiming deviations in drum loops. Based on a two-part perceptual evaluation, we show these four new microtiming features can each correlate to similarity perception, and be used with rhythm similarity metrics to improve personalized similarity models for drum loops. A new measure of structural rhythmic similarity is also shown to correlate more strongly to similarity perception of drum loops than the more com- monly used Hamming distance. These results point to the potential application of the GrooveToolbox and its new features in drum loop analysis for intelligent music production tools. The GrooveToolbox may be found at: https://github.com/fredbru/GrooveToolbox

• Lartillot, Olivier & Toiviainen, Petri (2020). Read about the Matlab MIRtoolbox. Young Acousticians Network (YAN) Newsletter. p. 4–10. Show summary

  MIRtoolbox is a Matlab toolbox dedicated to the analysis of music and sound from audio recordings and to the extraction of musical features such as tonality, rhythm, or structures. It has also been used for non- musical applications, such as in Non Destructive Testing, and with non-audio signals. In this issue of the newsletter, the YAN discusses the MIRtoolbox with Olivier Lartillot (RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway) and Petri Toiviainen (University of Jyväskylä, Finland) You can also check out the MIRtoolbox website at: shorturl.at/oA038

• Lartillot, Olivier & Toiviainen, Petri (2020). Read about the Matlab MIRtoolbox. [Internet]. Young Acousticians Network Newsletter. Show summary

  MIRtoolbox is a Matlab toolbox dedicated to the analysis of music and sound from audio recordings and to the extraction of musical features such as tonality, rhythm, or structures. It has also been used for non- musical applications, such as in Non Destructive Testing, and with non-audio signals. In this issue of the newsletter, the YAN discusses the MIRtoolbox with Olivier Lartillot (RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway) and Petri Toiviainen (University of Jyväskylä, Finland) You can also check out the MIRtoolbox website at: shorturl.at/oA038

• Lartillot, Olivier & Bruford, Fred (2020). Bistate reduction and comparison of drum patterns. Show summary

  This paper develops the hypothesis that symbolic drum patterns can be represented in a reduced form as a sim- ple oscillation between two states, a Low state (commonly associated with kick drum events) and a High state (often associated with either snare drum or high hat). Both an onset time and an accent time is associated to each state. The systematic inference of the reduced form is formal- ized. This enables the specification of a rhythmic struc- tural similarity measure on drum patterns, where reduced patterns are compared through alignment. The two-state representation allows a low computational cost alignment, once the complex topological formalization is fully taken into account. A comparison with the Hamming distance, as well as similarity ratings collected from listeners on a drum loop dataset, indicates that the bistate reduction enables to convey subtle aspects that goes beyond surface-level com- parison of rhythmic textures.

• Lartillot, Olivier; Cancino-Chacón, Carlos & Brazier, Charles (2020). Real-Time Visualisation Of Fugue Played By A String Quartet. Show summary

  We present a new system for real-time visualisation of music performance, focused for the moment on a fugue played by a string quartet. The basic principle is to offer a visual guide to better understand music using strategies that should be as engaging, accessible and effective as possible. The pitch curves related to the separate voices are drawn on a space whose temporal axis is normalised with respect to metrical positions, and aligned vertically with respect to their thematic and motivic classification. Aspects related to tonality are represented as well. We describe the underlying technologies we have developed and the technical setting. In particular, the rhythmical and structural representation of the piece relies on real-time polyphonic audio-to-score alignment using online dynamic time warping. The visualisation will be presented at a concert of the Danish String Quartet, performing the last piece of The Art of Fugue by Johann Sebastian Bach.

• Lartillot, Olivier (2019). A comprehensive framework for computational music analysis. Show summary

  During this presentation, Dr. Olivier Lartillot will give an overview of MIRtoolbox - a Matlab application enabling the extraction of a large range of audio and musical descriptions from recordings. MIRtoolbox is designed to be easy to use both for teaching at any level and for advanced research in musicology, signal analysis and music cognition. One initial aim of MIRtoolbox was to study the relationship between musical features and emotions evoked by music. MIRtoolbox focuses on signal-processing-based approaches that offer limited understanding of music. Lartillot is also developing computational methods for the analysis of notated music, starting from motivic analysis and aiming at building a comprehensive framework where audio and score are combined together.

• Câmara, Guilherme Schmidt; Nymoen, Kristian; Lartillot, Olivier & Danielsen, Anne (2019). Timing is Everything... Or is it? Part I: Effects of Instructed Timing and Reference on Guitar and Bass Sound in Groove Performance.
• Lartillot, Olivier (2019). Computational analysis of tempo and metre: from signal processing to cognitive musicology. Show summary

  Computational models for the analysis of tempo, metre and the tracking of beats have made significant progress during the last decades. I first present a synthetic overview of the state of the art. Up to recently, classical approaches were based on signal processing, with the integration of heuristics based on assumptions related to music perception and cognition. The standard approach is to first detect percussive events through the establishment of an accentuation curve, followed by periodicity detection, and the construction and tracking of meter. Because rhythmic emphasis can develop on various metrical levels across time, it is necessary to track the metrical structure on multiple levels. I show the benefit of such detailed analysis with the use of a model I have developed, and which obtained one of the highest grades in the MIREX tempo estimation competition. New approaches based on deep learning have achieved impressive progress and have largely surpassed signal-processing-based approached (including mine) in the recent yearly editions of MIREX. One limitation of these approaches, at least in their current stages, is that they appear as black boxes able to imitate a particular behaviour for which they were trained on particular examples. As such, they hardly offer insight on the cognitive mechanisms underlying the perception of metre. I will discuss the limitations of signal processing approaches and highlight the complexity of the musical structure. Pulsation in music is not always expressed through a periodic repetition of percussive events, but may emerge from a subtle propagation of motivic or harmonic structures. I present an approach under development that models the different components of music analysis and combine them altogether, extending further Lerdahl and Jackendoff’s vision. Motivic repetition, which plays a core role, is also one of the dimensions that is the most difficult to model and automate.

• Sørbø, Solveig Isis; Bentham, John; Watson, Pia; Lartillot, Olivier; Gonzalez Sanchez, Victor Evaristo & González, María Isabel (2019). Aether Trouble. Show summary

  Music video for "Aether Trouble" by PYSJ. With: Pia Watson Víctor González (RITMO*) Torgeir Koppang (PYSJ) Solveig Sørbø (PYSJ) Cinematography: John Bentham Camera Operators: Morten Malerstuen (camera and additional cinematography) Kristoffer Haugen Lighting: Nicholas Blakstad Andresen Morten Malerstuen Breath data collection (using FLOW* by SweetZpot): Víctor González, RITMO* Visualization of breath data and audio: MIRAGE* and MIRtoolbox by Olivier Lartillot, RITMO* Editing: María Isabel González Story / Concept: Solveig Sørbø Olivier Lartillot Pia Watson Directed by: John Bentham Solveig Sørbø Consultant: Nicholas Blakstad Andresen Produced by: Solveig Sørbø Extras: The dogs Vips and Willy Special thanks: Sagar Sen Naín Mendoza Fonseca Turid Svensøy SweetZpot RITMO* Workaway.info Filter Musikk Thanks to F21 for letting us use their studio MUSIC: Find the audio track on your plattform of choice: https://fanlink.to/aether Find the audio track on your plattform of choice: https://fanlink.to/aether Aether Trouble by PYSJ Written by: Solveig Sørbø Performed by PYSJ Solveig Sørbø Torgeir Koppang Andreas Rødland Haga Stig Frogner Mixing: Stig Frogner Mastering: Björn Engelmann / The Cutting Room Production: Solveig Sørbø * RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo. This work was partially supported by the Research Council of Norway through its Centres of Excellence scheme, project number 262762. * Learn more about the visualization technologies on the MIRAGE project website: http://bit.ly/MirageProject * Data was collected using FLOW™ sensors from SweetZpot. For more information about this technology see: https://www.sweetzpot.com/flow

• Haugen, Mari Romarheim; Johansson, Mats Sigvard & Lartillot, Olivier (2019). Investigating rhythm production and perception in traditional scandinavian dance music in non-isochronous meter: A case study of norwegian telespringar.
• Lartillot, Olivier (2019). Miningsuite: A Comprehensive Matlab Framework for Signal, Audio and Music Analysis, Articulating Audio and Symbolic Approaches.
• Lartillot, Olivier & Grandjean, Didier (2019). Tempo and Metrical Analysis by Tracking Multiple Metrical Levels Using Autocorrelation.
• Lartillot, Olivier (2019). The MIRAGE project.
• Lartillot, Olivier (2018). The MiningSuite 0.10 (first beta version). Show summary

  The MiningSuite is a free open-source and comprehensive Matlab framework for the analysis of signals, audio recordings, music recordings and music scores (and soon video, and more to come later) under a common modular framework. The MiningSuite adds a syntactic layer on top of Matlab, so that advanced operations can be specified using a simple and adaptive syntax. This makes the Matlab environment very easy to use for beginners, and in the same time allows power users to design complex workflows in a modular and concise way through a simple assemblage of operators featuring a large set of options. The MiningSuite is an extension of MIRtoolbox, a Matlab toolbox that has become a reference tool in the Music Information Retrieval (MIR) research and academic community.

• Lartillot, Olivier (2018). MIRtoolbox 1.7.1. Show summary

  MIRtoolbox offers an integrated set of functions written in Matlab, dedicated to the extraction from audio files of musical features such as tonality, rhythm, structures, etc. The objective is to offer an overview of computational approaches in the area of Music Information Retrieval. The design is based on a modular framework: the different algorithms are decomposed into stages, formalized using a minimal set of elementary mechanisms. These building blocks form the basic vocabulary of the toolbox, which can then be freely articulated in new original ways. These elementary mechanisms integrates all the different variants proposed by alternative approaches - including new strategies we have developed -, that users can select and parametrize. This synthetic digest of feature extraction tools enables a capitalization of the originality offered by all the alternative strategies. Additionally to the basic computational processes, the toolbox also includes higher-level musical feature extraction tools, whose alternative strategies, and their multiple combinations, can be selected by the user. The choice of an object-oriented design allows a large flexibility with respect to the syntax: the tools are combined in order to form a sets of methods that correspond to basic processes (spectrum, autocorrelation, frame decomposition, etc.) and musical features. These methods can adapt to a large area of objects as input. For instance, the autocorrelation method will behave differently with audio signal or envelope, and can adapt to frame decompositions.

• Lartillot, Olivier & Jensenius, Alexander Refsum (2018). SoundTracer. Show summary

  Discover traditional Norwegian music by drawing gestures with your iPhone or iPad. First draw a gesture by moving your iPhone or iPad up and down, to indicate ascending and descending melody. A simple sound is played while you move your device, its pitch gets higher when you move up, and gets lower when you move down. You can also indicate the beginning of a new note by moving abruptly the phone up or down (a blue vertical line indicates the note position). It is also possible to indicate a particular note without indication of pitch by performing a quick movement down and up (a red vertical line indicates the note position). Once you have terminated your gesture (by tapping once), the piece of music that contains the melodic gesture closest to your gesture is presented. The title and genre of that piece of music is indicated, as well as the performer and the district in Norway where it comes from. A melodic curve of the specific melodic gesture from the song is displayed below your own gesture. The matching between your gesture and the melody is based on a temporal alignment of their contours: the ascending parts of your gesture is aligned with the ascending parts of the melody, and same for the descending parts. The temporal note locations you have specified (bleu and red vertical lines) are also tentatively aligned with corresponding note changes in the melody. Some parts may be skipped, and are shown in red in the curves. When the music is played, a cursor moves throughout both curves so that you can follow the music while looking at the gestures. SoundTracer uses Apple's ARKit technology, solely in order to precisely track the location of your iPhone or iPad. For that reason, there is no actual Augmented Reality, and the camera is solely used to improve the precision of the tracking, without displaying the actual frames of the camera. For the moment, the catalogue of music is composed of around 50 pieces, and only the initial part of the melody is considered. The catalogue includes a few tunes played on the traditional Norwegian Hardanger fiddle, and songs sung a cappella. SoundTracer will be progressively improved with more music. SoundTracer is an innovation project led by Alexander Refsum Jensenius, supported by the University of Oslo and developed in collaboration with the National Library of Norway. More information here: https://www.hf.uio.no/ritmo/english/projects/all/soundtracer/

• Lartillot, Olivier; Nymoen, Kristian & Danielsen, Anne (2018). Prediction of P-centers from audio recordings.
• Lartillot, Olivier (2018). MoCap Toolbox in the MiningSuite.
• Lartillot, Olivier (2018). Overview of signal-based rhythm analysis.
• Lartillot, Olivier (2018). Computational sound/music/gesture analysis and application to gesture-based query in music catalogue. Show summary

  In the first part of this talk, I will give a short and broad overview of the MiningSuite, a Matlab toolbox that combines sound, music and gesture analysis. I will give a quick tour of the various types of sound and music analyses that can be carried out using the toolbox, covering a large range of musical dimensions such as timbre, rhythm, harmony or structure. The MiningSuite, integrating previous toolboxes such as MIRtoolbox and MIDI toolbox, can be used both for the analysis of audio recordings and of “symbolic” representations such as MIDI files. I will also present the current integration of motion capture and gesture analysis (from the MoCap Toolbox), as well as other sensor data such as breathing. The benefit of articulating these different types of analyses into a single framework will be demonstrated. In the second part, I will present a project aimed at automatically extracting melodic gestures from a catalogue of folk music recordings from the National Library of Norway. While melodic lines can be easily extracted from a cappella songs, the task is more challenging for other types of music such as Hardanger fiddle music. In such cases, we need to automatically transcribe the recordings and track melodic voices throughout the counterpoint of each composition. I will also present an iPhone app that enables to draw a gesture in the air with the phone and to find pieces of music from the catalogue that is characterised by a similar musical gesture.

• Lartillot, Olivier (2018). Cookbook 1: Sound analysis in Matlab.
• Lartillot, Olivier (2018). Music and Voice Analysis using Matlab: MIRtoolbox and the MiningSuite.
• Lartillot, Olivier (2018). Analyse musicologique par ordinateur d'enregistrements audio et de partitions informatisées. Show summary

  Cette présentation offrira un panorama des diverses méthodes d’analyse musicale par ordinateur conçues et/ou développées par O. Lartillot, couvrant aussi bien l’analyse d’enregistrements sonores que représentations « symboliques » (c’est-à-dire partitions informatisées ou séquences MIDI). J’expliquerai aussi brièvement les enjeux musicologiques et épistémologiques sous-jacents. La boîte à outils MIRtoolbox produit un éventail assez large de descriptions audio et musicales de fichiers sons. Elle a été par exemple utilisée pour étudier le timbre dans la musique extra-européenne, en collaboration avec Stéphanie Weisser. J’ai également conçu de nouvelles techniques d’analyse mélodique et motivique de représentations symboliques, avec des applications notamment à l’analyse de musiques traditionnelles arabes et turques.

• Lartillot, Olivier (2018). MIRToolbox and MiningSuite.
• Lartillot, Olivier (2018). The MiningSuite - a Matlab toolbox for signal, audio and music analysis. Show summary

  This course gives an introduction to analysis of signals, audio recordings, music recordings and music scores with MiningSuite - a free open-source Matlab toolbox. The MiningSuite is a free open-source Matlab toolbox for the analysis of signals, audio recordings, music recordings and music scores (and soon video and motion trajectories) under a common modular framework. The MiningSuite adds a syntactic layer on top of Matlab, so that advanced operations can be specified using a simple and adaptive syntax. This makes the Matlab environment very easy to use for beginners, and in the same time allows power users to design complex workflows in a modular and concise way through a simple assemblage of operators featuring a large set of options. The MiningSuite is an extension of MIRtoolbox, a Matlab toolbox that has become a reference tool in the Music Information Retrieval (MIR) research and academic community. In this course, you will first learn the basic principles of the MiningSuite’s syntax, which enables you to quickly design analytic processes, from simple operators to complex pipelines. You will get a broad overview of the analytic tools available, from basic signal processing to auditory modelling to music analysis. All concepts will be explained from the ground up, so that no prior expertise in signal processing or in music analysis is required.

• Lartillot, Olivier (2018). Computational musicological analysis of notated music: a brief overview. Show summary

  Since the dawn of informatics, computer-based analysis of notated music has been attempted. I present a brief overview of the research in this area, covering the various tasks of music analysis, such as melodic, rhythmic, metrical, motivic or structural analyses, and evoking diverse methodological alternatives. I will discuss about the current state of the art, in particular the limitations of computational approaches so far, the technical challenges, and how I propose to transcend the current limitations. Practical applications of this area of research will be discussed, such as retrieval of musical queries in databases, automated identification (of genre, composer, etc.) or music performance and generation.

• Christodoulou, Anna-Maria; Anagnostopoulou, Christina & Lartillot, Olivier (2022). Computational Analysis of Greek folk music of the Aegean islands. National and Kapodistrian University of Athens.
• Lartillot, Olivier (2018). mirtempo 1.8: Tempo Estimation By Tracking a Complete Metrical Structure. Music Information Retrieval Evaluation eXchange (MIREX). Full text in Research Archive Show summary

  This paper describes the two variants OL1 and OL2 of the model submitted to the MIREX 2018 tempo estimation tasks, and compare them with respect to my previous submission for MIREX 2013.

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