krisny

• Danielsen, Anne; Nymoen, Kristian; Langerød, Martin Torvik; Jacobsen, Eirik; Johansson, Mats Sigvard & London, Justin (2021). Sounds familiar(?): Expertise with specific musical genres modulates timing perception and micro-level synchronization to auditory stimuli. Attention, Perception & Psychophysics. ISSN 1943-3921. doi: 10.3758/s13414-021-02393-z. Full text in Research Archive Show summary

  Musical expertise improves the precision of timing perception and performance – but is this expertise generic, or is it tied to the specific style(s) and genre(s) of one’s musical training? We asked expert musicians from three musical genres (folk, jazz, and EDM/hip-hop) to align click tracks and tap in synchrony with genre-specific and genre-neutral sound stimuli to determine the perceptual center (“P-center”) and variability (“beat bin”) for each group of experts. We had three stimulus categories – Organic, Electronic, and Neutral sounds – each of which had a 2 Å~ 2 design of the acoustic factors Attack (fast/slow) and Duration (short/long). We found significant effects of Genre expertise, and a significant interaction for both P-center and P-center variability: folk and jazz musicians synchronize to sounds typical of folk and jazz in a different manner than the EDM/hip-hop producers. The results show that expertise in a specific musical genre affects our low-level perceptions of sounds as well as their affordance(s) for joint action/synchronization. The study provides new insights into the effects of active long-term musical enculturation and skill acquisition on basic sensorimotor synchronization and timing perception, shedding light on the important question of how nature and nurture intersect in the development of our perceptual systems.

• Wallace, Benedikte; Martin, Charles Patrick; Tørresen, Jim & Nymoen, Kristian (2021). Exploring the Effect of Sampling Strategy on Movement Generation with Generative Neural Networks, EvoMUSART 2021: Artificial Intelligence in Music, Sound, Art and Design. Springer Nature. ISSN 978-3-030-72913-4. p. 344–359. doi: 10.1007/978-3-030-72914-1_23.
• Wallace, Benedikte; Martin, Charles Patrick; Tørresen, Jim & Nymoen, Kristian (2021). Learning Embodied Sound-Motion Mappings: Evaluating AI-Generated Dance Improvisation, C&C'21: Proceedings of the 13th Conference on Creativity and Cognition. Association for Computing Machinery (ACM). ISSN 9781450383769. doi: 10.1145/3450741.3465245.
• Sioros, Georgios & Nymoen, Kristian (2021). Accurate shape and phase averaging of time series through Dynamic Time Warping. arXiv. Show summary

  Time series averaging is a crucial part of series summarization with important applications in data mining, signal noise suppression and information retrieval. Current averaging methods employ dynamic time warping (DTW) to correct for nonlinear phase variation between the sequences. They consider phase differences to be accidental and reduce durational information to sequential. Thus, they introduce time distortion that hinders the accurate estimation of the mean shape of the signals, its distinctive durational features, and the mean location of landmarks, all of which are required in many summarization applications. To address this problem, we propose a novel averaging method which preserves durational information due to a simple conversion of the output of DTW into a time sequence and an innovative iterative averaging process. We show that our method accurately estimates the ground truth mean sequences and mean temporal location of landmarks in synthetic and real-world datasets and outperforms state-of-the-art methods.

• 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.
• 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.
• Wallace, Benedikte; Martin, Charles Patrick & Nymoen, Kristian (2019). Tracing from Sound to Movement with Mixture Density Recurrent Neural Networks. In Coleman, Grisha (Eds.), Proceedings of the 6th International Conference on Movement and Computing. ACM Publications. ISSN 978-1-4503-7654-9. doi: 10.1145/3347122.3371376.
• Becker, Artur; Herrebrøden, Henrik; Gonzalez Sanchez, Victor Evaristo; Nymoen, Kristian; Dal Sasso Freitas, Carla Maria & Tørresen, Jim [Show all 7 contributors for this article] (2019). Functional Data Analysis of Rowing Technique Using Motion Capture Data. In Coleman, Grisha (Eds.), Proceedings of the 6th International Conference on Movement and Computing. ACM Publications. ISSN 978-1-4503-7654-9. doi: 10.1145/3347122.3347135.
• London, Justin; Nymoen, Kristian; Langerød, Martin Torvik; Thompson, Marc Richard; Code, David Løberg & Danielsen, Anne (2019). A comparison of methods for investigating the perceptual center of musical sounds. Attention, Perception & Psychophysics. ISSN 1943-3921. 81(6), p. 2088–2101. doi: 10.3758/s13414-019-01747-y. Full text in Research Archive
• Danielsen, Anne; Nymoen, Kristian; Anderson, Evan; Câmara, Guilherme Schmidt; Langerød, Martin Torvik & Thompson, Marc R. [Show all 7 contributors for this article] (2019). Where is the beat in that note? Effects of attack, duration, and frequency on the perceived timing of musical and quasi-musical sounds. Journal of Experimental Psychology: Human Perception and Performance. ISSN 0096-1523. 45(3), p. 402–418. doi: 10.1037/xhp0000611. Show summary

  The perceptual center (P-center) of a sound is typically understood as the specific moment at which it is perceived to occur. Using matched sets of real and artificial musical sounds as stimuli, we probed the influence of attack (rise time), duration, and frequency (center frequency) on perceived P-center location and P-center variability. Two different methods to determine the P-centers were used: Clicks aligned in-phase with the target sounds via the method of adjustment, and tapping in synchrony with the target sounds. Attack and duration were the primary cues for P-center location and P-center variability; P-center variability was found to be a useful measure of P-center shape. Consistent interactions between attack and duration were also found. Probability density distributions for each stimulus display a systematic pattern of P-center shapes ranging from narrow peaks close to the onset of sounds with fast attack and short duration, to wider and flatter shapes indicating a range synchronization points for sounds with slow attack and long duration. The results support the conception of P-centers as not simple time points, but "beat bins" with characteristic shapes, and the shapes and locations of these beat bins are dependent upon both the stimulus and the synchronization task.

• Watne, Åshild & Nymoen, Kristian (2018). Der æ so vent å vestoheio. Intonation in a "gammelstev" from Setesdal. Musikk og tradisjon. ISSN 1892-0772. 32, p. 7–30. Full text in Research Archive Show summary

  The intonation patterns in traditional Norwegian folk songs have been described and measured in various ways for more than a hundred years. This article provides a historical summary of research in this area and introduces a new software for measuring tone heights. This is exemplified through our analysis of an unaccompanied folk song, "Der æ so vent å vestoheio", recorded by the Norwegian Broadcasting Corporation (NRK) in 1951, performed by Gro Heddi Brokke (1910–1997) from the valley of Setesdal. The 5th and octave scale degrees stand out as the most stable throughout the tune, with a lot of variation in the thirds, sixths, and even the tonic. In spite of this variation, the performance comes forward as both confident and stable; the varying intonations appear controlled – they are not performer mistakes. Still, our findings suggest that tones of longer duration seem to vary less in intonation than shorter notes. We show how our software can be used in combination with manual analysis, and argue that automated pitch analysis may be useful also in the analysis of larger collections of Norwegian folk music.

• Watne, Åshild & Nymoen, Kristian (2018). Entreprenørskap i høyere norsk musikkutdanning. Nordisk musikkpedagogisk forskning : Årbok. ISSN 1504-5021. p. 367–385. Full text in Research Archive
• Nymoen, Kristian; Danielsen, Anne & London, Justin (2017). Validating Attack Phase Descriptors Obtained by the Timbre Toolbox and MIRtoolbox. In Lokki, Tapio; Pätynen, Jukka & Välimäki, Vesa (Ed.), Proceedings of the 14th Sound and Music Computing Conference 2017. Aalto University. ISSN 978-952-60-3729-5. p. 214–219. Full text in Research Archive
• Nymoen, Kristian (2017). Expert Commentary: Gesture Following Made Accessible. In Jensenius, Alexander Refsum & Lyons, Michael J. (Ed.), A NIME Reader: Fifteen Years of New Interfaces for Musical Expression. Springer Science+Business Media B.V.. ISSN 978-3-319-47213-3. p. 281–282. doi: 10.1007/978-3-319-47214-0_18. Show summary

  What is a musical instrument? What are the musical instruments of the future? This anthology presents thirty papers selected from the fifteen year long history of the International Conference on New Interfaces for Musical Expression (NIME). NIME is a leading music technology conference, and an important venue for researchers and artists to present and discuss their explorations of musical instruments and technologies. Each of the papers is followed by commentaries written by the original authors and by leading experts. The volume covers important developments in the field, including the earliest reports of instruments like the reacTable, Overtone Violin, Pebblebox, and Plank. There are also numerous papers presenting new development platforms and technologies, as well as critical reflections, theoretical analyses and artistic experiences. The anthology is intended for newcomers who want to get an overview of recent advances in music technology. The historical traces, meta-discussions and reflections will also be of interest for longtime NIME participants. The book thus serves both as a survey of influential past work and as a starting point for new and exciting future developments.

• Haugen, Mari Romarheim & Nymoen, Kristian (2016). Evaluating Input Devices for Dance Research. In Kronland-Martinet, Richard; Aramaki, Mitsuko & Ystad, Sølvi (Ed.), Music, Mind, and Embodiment :11th International Symposium, CMMR 2015, Plymouth, UK, June 16-19, 2015, Revised Selected Papers . Springer. ISSN 978-3-319-46281-3. p. 58–70. doi: 10.1007/978-3-319-46282-0. Full text in Research Archive
• Nymoen, Kristian; Chandra, Arjun & Tørresen, Jim (2016). Self-awareness in Active Music Systems. In Lewis, Peter R.; Platzner, Marco; Rinner, Bernhard; Tørresen, Jim & Yao, Xin (Ed.), Self-aware Computing Systems. Springer. ISSN 978-3-319-39674-3. p. 279–296. doi: 10.1007/978-3-319-39675-0_14.
• Wang, Shuo; Nebehay, Georg; Esterle, Lukas; Nymoen, Kristian & Minku, Leandro L. (2016). Common Techniques for Self-awareness and Self-expression. In Lewis, Peter R.; Platzner, Marco; Rinner, Bernhard; Tørresen, Jim & Yao, Xin (Ed.), Self-aware Computing Systems. Springer. ISSN 978-3-319-39674-3. p. 113–142. doi: 10.1007/978-3-319-39675-0_7.
• Godøy, Rolf Inge; Song, Min-Ho; Nymoen, Kristian; Haugen, Mari Romarheim & Jensenius, Alexander Refsum (2016). Exploring Sound-Motion Similarity in Musical Experience. Journal of New Music Research. ISSN 0929-8215. 45(3), p. 210–222. doi: 10.1080/09298215.2016.1184689. Show summary

  People tend to perceive many and also salient similarities between musical sound and body motion in musical experience, as can be seen in countless situations of music performance or listening to music, and as has been documented by a number of studies in the past couple of decades. The so-called motor theory of perception has claimed that these similarity relationships are deeply rooted in human cognitive faculties, and that people perceive and make sense of what they hear by mentally simulating the body motion thought to be involved in the making of sound. In this paper, we survey some basic theories of sound-motion similarity in music, and in particular the motor theory perspective. We also present findings regarding sound-motion similarity in musical performance, in dance, in so-called sound-tracing (the spontaneous body motions people produce in tandem with musical sound), and in sonification, all in view of providing a broad basis for understanding sound-motion similarity in music.

• Barrett, Natasha & Nymoen, Kristian (2015). Investigations in coarticulated performance gestures using interactive parameter-mapping 3D sonification. Proceedings of the International Conference on Auditory Display. ISSN 2168-5126.
• Haugen, Mari Romarheim & Nymoen, Kristian (2015). Evaluating Input Devices for Dance Research, Proceedings of the 11th International Symposium on Computer Music Multidisciplinary Research. The Interdisciplinary Centre for Computer Music Research. ISSN 978-2-909669-24-3. p. 263–270. doi: 10.1007/978-3-319-46282-0_4. Full text in Research Archive
• Bojic, Iva & Nymoen, Kristian (2015). Survey on synchronization mechanisms in machine-to-machine systems. Engineering Applications of Artificial Intelligence. ISSN 0952-1976. 45, p. 361–375. doi: 10.1016/j.engappai.2015.07.007. Show summary

  People have always tried to understand natural phenomena. In computer science natural phenomena are mostly used as a source of inspiration for solving various problems in distributed systems such as optimization, clustering, and data processing. In this paper we will give an overview of research in field of computer science where fireflies in nature are used as role models for time synchronization. We will compare two models of oscillators that explain firefly synchronization along with other phenomena of synchrony in nature (e.g., synchronization of pacemaker cells of the heart and synchronization of neuron networks of the circadian pacemaker). Afterwards, we will present Mirollo and Strogatz׳s pulse coupled oscillator model together with its limitations. As discussed by the authors of the model, this model lacks of explanation what happens when oscillators are nonidentical. It also does not support mobile and faulty oscillators. Finally, it does not take into consideration that in communication among oscillators there are communication delays. Since these limitations prevent Mirollo and Strogatz׳s model to be used in real-world environments (such as Machine-to-Machine systems), we will sum up related work in which scholars investigated how to modify the model in order for it to be applicable in distributed systems. However, one has to bear in mind that there are usually large differences between mathematical models in theory and their implementation in practice. Therefore, we give an overview of both mathematical models and mechanisms in distributed systems that were designed after them.

• Nymoen, Kristian; Haugen, Mari Romarheim & Jensenius, Alexander Refsum (2015). MuMYO – Evaluating and Exploring the MYO Armband for Musical Interaction. In Berdahl, Edgar (Eds.), Proceedings of the International Conference on New Interfaces For Musical Expression. Louisiana State University. ISSN 2220-4792. Full text in Research Archive
• Nymoen, Kristian; Chandra, Arjun; Glette, Kyrre Harald & Tørresen, Jim (2015). Decentralized harmonic synchronization in mobile music systems. In Madani, Kurosh; Yen, Neil Y. & Manabe, Yusuke (Ed.), Awareness Science and Technology (iCAST), 2014 IEEE 6th International Conference on. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4799-7373-6. p. 51–56. doi: 10.1109/ICAwST.2014.6981832.
• Nymoen, Kristian; Chandra, Arjun; Glette, Kyrre Harald; Tørresen, Jim; Voldsund, Arve & Jensenius, Alexander Refsum (2014). PheroMusic: Navigating a Musical Space for Active Music Experiences. In Kouroupetroglou, Georgios & Georgaki, Anastasia (Ed.), Music Technology Meets Philosophy: From digital echos to virtual ethos Proceedings of the 40th International Computer Music Conference joint with the 11th Sound and Music Computing conference. International Computer Music Association. ISSN 978-0-9845274-3-4. p. 1715–1718. Full text in Research Archive
• Nymoen, Kristian; Tørresen, Jim; Song, Sichao & Hafting, Yngve (2014). Funky Sole Music: Gait Recognition and Adaptive Mapping. In Caramiaux, Baptiste; Tahiroğlu, Koray; Fiebrink, Rebecca & Tanaka, Atau (Ed.), Proceedings of the International Conference on New Interfaces For Musical Expression. Goldsmiths, University of London. ISSN 978-1-906897-29-1. p. 299–302. Full text in Research Archive
• Nymoen, Kristian; Chandra, Arjun & Tørresen, Jim (2014). The Challenge of Decentralised Synchronisation in Interactive Music Systems. In Scholtes, Ingo (Eds.), Self-Adaptation and Self-Organizing Systems Workshops (SASOW), 2013 IEEE 7th International Conference on. IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-4799-5086-7. p. 95–100. doi: 10.1109/SASOW.2013.18.
• Chandra, Arjun; Nymoen, Kristian; Voldsund, Arve; Jensenius, Alexander Refsum; Glette, Kyrre Harald & Tørresen, Jim (2013). Market-based Control in Interactive Music Environments. In Aramaki, Mitsuko; Barthet, Mathieu; Kronland-Martinet, Richard & Ystad, Sølvi (Ed.), From Sounds to Music and Emotions - 9th International Symposium, CMMR 2012, London, UK, June 19-22, 2012, Revised Selected Papers. Springer. ISSN 978-3-642-41247-9. p. 439–458. doi: 10.1007/978-3-642-41248-6_25.
• Skogstad, Ståle Andreas van Dorp; Nymoen, Kristian; Høvin, Mats Erling; Holm, Sverre & Jensenius, Alexander Refsum (2013). Filtering Motion Capture Data for Real-Time Applications. In Yeo, Woon Seung; Lee, Kyogu; Sigman, Alexander; Ji, Haru & Graham, Wakefield (Ed.), Proceedings of the International Conference on New Interfaces For Musical Expression. Korea Advance Institute of Science and Technology. ISSN 2220-4792. p. 142–147. Full text in Research Archive
• Nymoen, Kristian; Godøy, Rolf Inge; Jensenius, Alexander Refsum & Tørresen, Jim (2013). Analyzing Correspondence between Sound Objects and Body Motion. ACM Transactions on Applied Perception. ISSN 1544-3558. 10(2). doi: 10.1145/2465780.2465783. Full text in Research Archive Show summary

  Links between music and body motion can be studied through experiments called sound-tracing. One of the main challenges in such research is to develop robust analysis techniques that are able to deal with the multidimensional data that musical sound and body motion present. The article evaluates four different analysis methods applied to an experiment in which participants moved their hands following perceptual features of short sound objects. Motion capture data has been analyzed and correlated with a set of quantitative sound features using four different methods: (a) a pattern recognition classifier, (b) t-tests, (c) Spearman’s ρ correlation, and (d) canonical correlation. This article shows how the analysis methods complement each other, and that applying several analysis techniques to the same data set can broaden the knowledge gained from the experiment.

• Tørresen, Jim; Hafting, Yngve & Nymoen, Kristian (2013). A new wi-fi based platform for wireless sensor data collection. In Proceedings of the International Conference on New Interfaces For Musical Expression. In Yeo, Woon Seung; Lee, Kyogu; Sigman, Alexander; Ji, Haru & Graham, Wakefield (Ed.), Proceedings of the International Conference on New Interfaces For Musical Expression. Korea Advance Institute of Science and Technology. ISSN 2220-4792. p. 337–340.
• Godøy, Rolf Inge; Jensenius, Alexander Refsum; Voldsund, Arve; Glette, Kyrre Harald; Høvin, Mats Erling & Nymoen, Kristian [Show all 7 contributors for this article] (2012). Classifying Music-Related Actions. In Cambouropoulos, Emilios; Tsougras, Costas; Mavromatis, Panayotis & Pastiadis, Konstantinos (Ed.), Proceedings of the ICMPC-­ESCOM 2012 Joint Conference: 12th Biennial International Conference for Music Perception and Cognition, 8th Triennial Conference of the European Society for the Cognitive Sciences of Music. School of Music Studies, Aristotle University of Thessaloniki Thessaloniki, Hellas . ISSN 978-960-99845-1-5. p. 352–357. Full text in Research Archive
• Chandra, Arjun; Nymoen, Kristian; Voldsund, Arve; Jensenius, Alexander Refsum; Glette, Kyrre Harald & Tørresen, Jim (2012). Enabling Participants to Play Rhythmic Solos Within a Group via Auctions. In Kronland-Martinet, Richard; Ystad, Sølvi; Aramaki, Mitsuko; Barthet, Mathieu & Dixon, Simon (Ed.), Proceedings of the 9th International Symposium on Computer Music Modeling and Retrieval. Queen Mary, University of London. p. 674–689. Full text in Research Archive
• Jensenius, Alexander Refsum; Nymoen, Kristian; Skogstad, Ståle Andreas van Dorp & Voldsund, Arve (2012). A Study of the Noise-Level in Two Infrared Marker-Based Motion Capture Systems. In Sturm, Bob L.; Dahl, Sofia & Larsen, Jan (Ed.), Proceedings of the 9th Sound and Music Computing Conference - "Illusions". Logos Verlag Berlin. ISSN 9783832531805. p. 258–263. Full text in Research Archive

View all works in Cristin

• Danielsen, Anne; Langerød, Martin Torvik; Nymoen, Kristian & London, Justin (2021). Genre expertise modulates timing perception and micro-level synchronization to auditory stimuli.
• Danielsen, Anne; Langerød, Martin Torvik; Nymoen, Kristian & London, Justin (2021). Genre expertise modulates timing perception and micro-level synchronization to auditory stimuli.
• Wallace, Benedikte; Nymoen, Kristian; Martin, Charles Patrick & Tørresen, Jim (2020). Towards Movement Generation with Audio Features.
• Kjørstad, Elise & Nymoen, Kristian (2020). Kunstig intelligens har komponert sanger til Eurovision-inspirert konkurranse. [Internet]. forskning.no.
• Danielsen, Anne & Nymoen, Kristian (2019). Where is the beat in that note?
• Wallace, Benedikte; Nymoen, Kristian & Martin, Charles Patrick (2019). Tracing from Sound to Movement with Mixture Density Recurrent Neural Networks.
• Becker, Artur; Herrebrøden, Henrik; Gonzalez Sanchez, Victor Evaristo; Nymoen, Kristian; Dal Sasso Freitas, Carla Maria & Tørresen, Jim [Show all 7 contributors for this article] (2019). Functional Data Analysis of Rowing Technique Using Motion Capture Data. Show summary

  We present an approach to analyzing the motion capture data ofrowers using bivariate functional principal component analysis(bfPCA). The method has been applied on data from six elite rowersrowing on an ergometer. The analyses of the upper and lower bodycoordination during the rowing cycle revealed significant differences between the rowers, even though the data was normalized toaccount for differences in body dimensions. We make an argumentfor the use of bfPCA and other functional data analysis methods forthe quantitative evaluation and description of technique in sports.

• Sioros, George; Câmara, Guilherme Schmidt; Danielsen, Anne & Nymoen, Kristian (2019). Timing and drummers’ movement: A novel methodology for performance analysis . Show summary

  Timing is an important aspect of groove music. The relationship between musicians’ body motion in performance and timing is, however, as yet not well understood Timing and drummers’ movement: A novel methodology for performance analysis . In the present study we recorded movement of 20 drummers performing the same rhythmic pattern under four different timing instructions: natural, on- the-beat, laid-back and pushed. Motion capture data synchronized to audio recordings of their performances were collected as part of a larger experimental project. This presentation focuses on our method for analyzing motion capture data. The aim of the analysis is a) to identify common movement strategies for sub-groups of drummers, and b) to identify strategies for achieving the four different timing conditions across drummers. In this presentation we focus on the movement of the left arm, and particularly on the preparation and rebound phase of the snare strokes. To explore and analyze the data without statistically testing a priori hypotheses about specific performance techniques, we combined existing practices from different disciplines into a novel methodology. First, we reduce the data into motion templates (Müller and Röder 2006). We design a set of 22 binary features to describe the movement of the arm. Second, we perform a phylogenetic analysis of the motion templates, in which we identify clusters within each timing condition. A comparison between clusters reveals differences in the coordination of the participants’ movements that correspond to the different performance strategies. Preliminary analysis has shown distinct clusters within all timing conditions that differ in specific features. For instance, we observe three groups of participants within the “natural” condition that differ in the flexion of the wrist and elbow. Besides our findings we will present the details of the methodology, which can be applied in the study of music-related movements beyond the scope of this project.

• Câmara, Guilherme Schmidt; Nymoen, Kristian & Danielsen, Anne (2019). Timing is Everything... Or is it? Part II: Effects of Instructed Timing Style and Timing Reference on Drum-Kit Sound in Groove Performance.
• 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.
• Danielsen, Anne; Nymoen, Kristian & London, Justin (2019). Noise in the click or click in the noise: Investigating probe-stimulus order in P-center estimation tasks.
• Nymoen, Kristian (2019). Musikalske maskiner.
• Jensenius, Alexander Refsum; Martin, Charles Patrick; Erdem, Cagri; Lan, Qichao; Fuhrer, Julian Peter & Gonzalez Sanchez, Victor Evaristo [Show all 8 contributors for this article] (2019). Self-playing Guitars. Show summary

  In this installation we explore how six self-playing guitars can entrain to each other. When they are left alone they will revert to playing a common pulse. As soon as they sense people in their surroundings they will start entraining to other pulses. The result is a fascinating exploration of a basic physical and cognitive concept, and the musically interesting patterns that emerge on the border between order and chaos.

• Lartillot, Olivier; Nymoen, Kristian & Danielsen, Anne (2018). Prediction of P-centers from audio recordings.
• Nymoen, Kristian (2018). Soundtracer - Et innblikk i innholdsbasert arkivsøk. Show summary

  Et musikkopptak inneholder store mengder informasjon. I fagfaltet Music Information Retrieval forskes det på teknikker for å hente ut informasjon fra selve lydsignalet. Disse teknikkene gir nye og spennende muligheter for søk i lydarkiv – enten som supplement til, eller i stedet for, metadata. Hvordan kan man søke i et lydarkiv etter et bestemt intonasjonsmønster eller en bestemt rytmikk? Og hva skal til for å søke i et arkiv ved å nynne, klappe, eller bevege seg?

• Nymoen, Kristian (2018). Music Information Retrieval.
• London, Justin; Danielsen, Anne & Nymoen, Kristian (2018). Where is the beat in that note? Effects of attack, frequency, and duration on the p-centers of musical and quasi-musical sounds.
• Danielsen, Anne; London, Justin & Nymoen, Kristian (2017). Mapping the Beat Bin: The Effects of Rise Time, Duration, and Frequency Range on the Perceived Timing of Musical Sounds.
• London, Justin; Nymoen, Kristian; Thompson, Marc; Code, David Loberg & Danielsen, Anne (2017). Where is the beat in that note? Comparing methods for identifying the p-center of musical sounds.
• London, Justin; Nymoen, Kristian; Thompson, Marc; Code, David Loberg & Danielsen, Anne (2017). Where is the beat in that note? Comparing methods for identifying the p-center of musical sounds.
• Danielsen, Anne; London, Justin & Nymoen, Kristian (2017). Mapping the beat bin: Effects of rise time, duration and frequency range on the perceived timing (P-center) of musical sounds.
• Nymoen, Kristian; Danielsen, Anne & London, Justin (2017). Validating Attack Phase Descriptors Obtained by the Timbre Toolbox and MIRtoolbox. Show summary

  The attack phase of sound events plays an important role in how sounds and music are perceived. Several approaches have been suggested for locating salient time points and critical time spans within the attack portion of a sound, and some have been made widely accessible to the research community in toolboxes for Matlab. While some work exists where proposed audio descriptors are grounded in listening tests, the approaches used in two of the most popular toolboxes for musical analysis have not been thoroughly compared against perceptual results. This article evaluates the calculation of attack phase descriptors in the Timbre toolbox and the MIRtoolbox by comparing their predictions to empirical results from a listening test. The results show that the default parameters in both toolboxes give inaccurate predictions for the sound stimuli in our experiment. We apply a grid search algorithm to obtain alternative parameter settings for these toolboxes that align their estimations with our empirical results.

• Nymoen, Kristian (2017). Xsens Motion Capture as Musical Instrument.
• Watne, Åshild & Nymoen, Kristian (2017). Entrepreneurship in Higher Music Education in Norway.
• Danielsen, Anne; Nymoen, Kristian; Haugen, Mari Romarheim & Câmara, Guilherme Schmidt (2017). Project presentation: Timing and Sound in Musical Microrhythm (TIME).
• Jensenius, Alexander Refsum & Nymoen, Kristian (2016). Muskel- og bevegelsesmusikk.
• Jensenius, Alexander Refsum & Nymoen, Kristian (2016). Velkommen til Institutt for musikkvitenskap.
• Jensenius, Alexander Refsum; Zeiner-Henriksen, Hans T. & Nymoen, Kristian (2016). Music Moves: Why does music make you move? Show summary

  Learn about the psychology of music and movement, and how researchers study music-related movements, with this free online course.

• Godøy, Rolf Inge; Song, Min-Ho; Nymoen, Kristian; Haugen, Mari Romarheim & Jensenius, Alexander Refsum (2016). ¿Por qué marcamos el ritmo de la música con los pies? [Internet]. BBC Mundo. Show summary

  ¿Alguna vez has estado en un bar o en un restaurante, sentado en la calle o en un salón cuando suena música y tú y otros empiezan a golpear el piso con el pie al ritmo de la música?

• Nymoen, Kristian; Tørresen, Jim; Drury, Jim & Chen, Roselle (2015). Sole to soul - foot tapping to a new beat. [Internet]. Reuters video.
• Haugen, Mari Romarheim & Nymoen, Kristian (2015). Evaluating Input Devices for Dance Research.
• Nymoen, Kristian; Haugen, Mari Romarheim & Jensenius, Alexander Refsum (2015). MuMYO – Evaluating and Exploring the MYO Armband for Musical Interaction. Show summary

  The MYO armband from Thalmic Labs is a complete and wireless motion and muscle sensing platform. This paper evaluates the armband’s sensors and its potential for NIME applications. This is followed by a presentation of the prototype instrument MuMYO. We conclude that, despite some shortcomings, the armband has potential of becoming a new “standard” controller in the NIME community.

• Jensenius, Alexander Refsum; Nymoen, Kristian; Tveit, Anders; Haugen, Mari Romarheim; Solberg, Ragnhild Torvanger & Eikeland, Håkon Bachken [Show all 7 contributors for this article] (2015). OMO på Komdagen.
• Jensenius, Alexander Refsum; Nymoen, Kristian; Haugen, Mari Romarheim; Andersen, Ida & Evertsson, Henrik (2015). Ubevegelig norgesrekord. [Newspaper]. Universitas. Show summary

  Torsdag 12. mars var det duket for historiens andre norgesmesterskap i stillstand.

• Nymoen, Kristian & Sigurjonsdottir, Sol (2015). Hackere skal utvikle fremtidens musikkløsninger på Bylarm. [Internet]. Aftenposten/Osloby.
• Olaisen, Rolf Petter & Nymoen, Kristian (2015). Konkurranse på By:Larm. [TV]. NRK Dagsrevyen 21, NRK Distriktsnyheter Østlandssendingen. Show summary

  På by:Larm 2015 arrangeres Norges aller første Music Hackathon, et konsept som årlig avholdes på flere av verdens største musikkfestivaler.

• Nymoen, Kristian (2015). MYO-music, demo.
• Nymoen, Kristian; Chandra, Arjun; Glette, Kyrre Harald & Tørresen, Jim (2014). Decentralized harmonic synchronization in mobile music systems.
• Vogt, Yngve; Abajo, Carlos Gómez; Tørresen, Jim & Nymoen, Kristian (2014). Una 'app' capta tu estado de ánimo y le pone la música adecuada, a través del smartphone. [Internet]. Tendencias21.
• Vogt, Yngve; Tørresen, Jim & Nymoen, Kristian (2014). Novo software promete revolucionar forma de ouvir música. [Internet]. sapo.pt.
• Tørresen, Jim; Nymoen, Kristian & Vogt, Yngve (2014). Tecnologia – Software em desenvolvimento na Noruega promete revolucionar forma de ouvir música. [Internet]. Portal Bragança.
• Vogt, Yngve; Tørresen, Jim & Nymoen, Kristian (2014). Você e seu celular vão mudar o ritmo da música. [Internet]. Inovação Tecnológica.
• Vogt, Yngve; Tørresen, Jim & Nymoen, Kristian (2014). Music software Reads Body Language to create Playlist based on Mood. [Internet]. Macedonian International News Agency.
• Tørresen, Jim; Nymoen, Kristian & Vogt, Yngve (2014). Software uses smartphone sensors to manipulate music. [Internet]. Phys.org.
• Tørresen, Jim; Nymoen, Kristian & Vogt, Yngve (2014). Una app capta tu estado de ánimo y le asocia una música. [Internet]. modetvideo.com.uy.
• Nymoen, Kristian & Vogt, Yngve (2014). New mobile tech makes music to suit moods. [Internet]. The Local.
• Vogt, Yngve; Abajo, Carlos Gómez; Tørresen, Jim & Nymoen, Kristian (2014). Una app capta tu estado de ánimo y le pone la música adecuada. [Internet]. La Razon.
• Nymoen, Kristian & Knoppers, Rijkert (2014). Smartphone past muziek aan aan gemoed. [Newspaper]. NRC Handelsblad.
• Vogt, Yngve; Tørresen, Jim & Nymoen, Kristian (2014). Music Will Never Be the Same. [Internet]. ACM TechNews.
• Vogt, Yngve; Tørresen, Jim & Nymoen, Kristian (2014). Musikken blir aldri den samme. [Business/trade/industry journal]. Apollon.
• Tørresen, Jim; Nymoen, Kristian & Vogt, Yngve (2014). Musikken blir aldri den samme. [Internet]. forskning.no.
• Jensenius, Alexander Refsum & Nymoen, Kristian (2014). MakerCon demo: Motion capture and music.
• Nymoen, Kristian (2014). Musikkteknologi som beveger: Fremtidens musikkinstrumenter. Show summary

  Bevegelse har alltid vært en del av musikken – fra musikerens lydproduserende bevegelser til lytterens dans. Med moderne bevegelsesteknologi kan kroppsbevegelser fra f.eks. dansere fanges opp og prosesseres i datamaskiner. Kan vi designe et interaktivt musikksystem hvor dans lager musikk? Hva skal til for at kroppsbevegelsene virkelig passer til musikken som kommer ut? Og hvordan brukes egentlig sensorer til å fange kroppsbevegelser? Kristian viser hvordan en avansert drakt for bevegelsessporing (mocap-drakt) kan brukes til å gi helt nye musikkopplevelser.

• Nymoen, Kristian; Chandra, Arjun; Glette, Kyrre Harald; Tørresen, Jim; Voldsund, Arve & Jensenius, Alexander Refsum (2014). PheroMusic: Navigating a Musical Space for Active Music Experiences. Show summary

  We consider the issue of how a flexible musical space can be manipulated by users of an active music system. The musical space is navigated within by selecting transitions between different sections of the space. We take inspiration from pheromone trails in ant colonies to propose and investigate an approach that allows an artificial agent to navigate such musical spaces in accordance with the preferences of the user, and a set of boundaries specified by the designer of the musical space.

• Knutzen, Håkon; Glette, Kyrre Harald; Nymoen, Kristian; Chandra, Arjun & Tørresen, Jim (2014). PheroMusic. Show summary

  Listen to music and control it with your own motion and selections! Pheromusic provides five different musical scenarios called "soundscapes". In each scenario, music is generated automatically. - Control the way the music is generated by tiliting your iOS device. - Select a new soundscape on the screen. - If you don't make a selection within 20 seconds, your device makes one for you. - A machine learning algorithm is used to make the device remember your choices. - Press and hold on a soundscape, to schedule the next without moving there immediately. To navigate between soundscapes Pheromusic uses a nature-inspired algorithm to implement musical memory in an artificial agent. In nature, ants deposit pheromones when looking for food. The pheromone trails are used to locate the same food source in the future. In PheroMusic, an artificial agent navigates between a number of soundscapes. Users may make active choices and decide the next soundscape to which the agent moves. When a new soundscape is selected, an artificial pheromone trail is created, influencing the path taken by the user in the future. The research leading to the development of this app has received funding from the European Union Seventh Framework Programme under grant agreement no. 257906

• Nymoen, Kristian; Song, Sichao; Hafting, Yngve & Tørresen, Jim (2014). Funky Sole Music: Gait Recognition and Adaptive Mapping.
• Tørresen, Jim & Nymoen, Kristian (2014). Kunstig klok. Dagens næringsliv. ISSN 0803-9372. p. 35–35.
• Nymoen, Kristian; Chandra, Arjun; Ånonsen, Simon; Anthonsen, Ingeborg Olavsrud & Andresen, Ola Haukland (2013). Oslo mobile orchestra på Læring for framtiden.
• Skogstad, Ståle Andreas van Dorp; Nymoen, Kristian; Høvin, Mats Erling; Holm, Sverre & Jensenius, Alexander Refsum (2013). Filtering Motion Capture Data for Real-Time Applications. Show summary

  In this paper we present some custom designed filters for real-time motion capture applications. Our target application is motion controllers, i.e. systems that interpret hand motion for musical interaction. In earlier research we found effective methods to design nearly optimal filters for realtime applications. However, to be able to design suitable filters for our target application, it is necessary to establish the typical frequency content of the motion capture data we want to filter. This will again allow us to determine a reasonable cutoff frequency for the filters. We have therefore conducted an experiment in which we recorded the hand motion of 20 subjects. The frequency spectra of these data together with a method similar to the residual analysis method were then used to determine reasonable cutoff frequencies. Based on this experiment, we propose three cutoff frequencies for different scenarios and filtering needs: 5, 10 and 15 Hz, which correspond to heavy, medium and light filtering, respectively. Finally, we propose a range of real-time filters applicable to motion controllers. In particular, low-pass filters and low-pass differentiators of degrees one and two, which in our experience are the most useful filters for our target application.

• Nymoen, Kristian; Chandra, Arjun & Tørresen, Jim (2013). Firefly with me: distributed synchronization of musical agents. Awareness Magazine. doi: 10.2417/3201311.005187. Show summary

  A system where individual musical nodes are aware of others and adapt to reach a synchronous state provides a collaborative active music performance for smartphones.

• Nymoen, Kristian; Chandra, Arjun & Tørresen, Jim (2013). The Challenge of Decentralised Synchronisation in Interactive Music Systems.
• Jensenius, Alexander Refsum; Nymoen, Kristian; Voldsund, Arve; Straume, Christopher; Rivocantus, Dositheos & Sandvik, Bjørnar [Show all 9 contributors for this article] (2012). OMO plays HTC.
• Jensenius, Alexander Refsum; Nymoen, Kristian; Chandra, Arjun; Halmrast, Tor; Rødseth, Alexander & Johansen, Lisa Schøne [Show all 9 contributors for this article] (2012). OMO performance: Squeeky.
• Godøy, Rolf Inge; Jensenius, Alexander Refsum; Voldsund, Arve; Glette, Kyrre Harald; Høvin, Mats Erling & Nymoen, Kristian [Show all 7 contributors for this article] (2012). Classifying Music-Related Actions. Show summary

  Our research on music-related actions is based on the conviction that sensations of both sound and body motion are inseparable in the production and perception of music. The expression "music-related actions" is here used to refer to chunks of combined sound and body motion, typically in the duration range of approximately 0.5 to 5 se¬conds. We believe that chunk-level music-related ac¬tions are highly signifi¬cant for the experience of music, and we are pres¬ently working on establishing a database of music-related actions in order to facilitate access to, and research on, our fast growing collection of motion capture data and related material. In this work, we are con¬fronted with a number of perceptual, concep¬tual and technological is¬sues regarding classification of music-related ac¬tions, issues that will be presented and discussed in this paper.

• Câmara, Guilherme Schmidt; Danielsen, Anne & Nymoen, Kristian (2021). Timing Is Everything . . . Or Is It? Investigating Timing and Sound Interactions in the Performance of Groove-Based Microrhythm. Universitetet i Oslo. Full text in Research Archive Show summary

  This thesis investigates the expressive means through which musicians well versed in groove-based music shape the timing of a rhythmic event, with a focus on the interaction between produced timing and sound features. In three performance experiments with guitarists, bassists, and drummers, I tested whether musicians systematically manipulate acoustic factors such as duration, intensity, and volume when they want to play with a specific microrhythmic style (pushed, on-the-beat, or laid-back). The results show that all three groups of instrumentalists indeed played pushed, on-the-beat, or laid-back relative to the reference pulse and in line with the instructed microrhythmic styles, and that there were systematic and consequential sound differences. Guitarists played backbeats with a longer duration and darker sound in relation to pushed and laid-back strokes. Bassists played pushed beats with higher intensity than on-the-beat and laid-back strokes. For the drummers, we uncovered different timing–sound combinations, including the use of longer duration (snare drum) and higher intensity (snare drum and hi-hat), to distinguish both laid-back and pushed from on-the-beat strokes. The metronome as a reference pulse led to less marked timing profiles than the use of instruments as a reference, and it led in general to earlier onset positions as well, which can perhaps be related to the phenomenon of “negative mean asynchrony.” We also conducted an in-depth study of the individual drummers’ onset and intensity profiles using hierarchical cluster analyses and phylogenetic tree visualizations and uncovered a diverse range of strategies. The results support the research hypothesis that both temporal and sound-related properties contribute to how we perceive the location of a rhythmic event in time. I discuss these results in light of theories and findings from other studies of the perception and performance of groove, as well as research into rhythm and microrhythmic phenomena such as perceptual centers and onset asynchrony/anisochrony.

• Nymoen, Kristian; Tørresen, Jim; Godøy, Rolf Inge; Jensenius, Alexander Refsum & Høvin, Mats Erling (2013). Methods and Technologies for Analysing Links Between Musical Sound and Body Motion. Akademisk Forlag. ISSN 1501-7710. Show summary

  There are strong indications that musical sound and body motion are related. For instance, musical sound is often the result of body motion in the form of sound-producing actions, and musical sound may lead to body motion such as dance. The research presented in this dissertation is focused on technologies and methods of studying lower-level features of motion, and how people relate motion to sound. Two experiments on so-called sound-tracing, meaning representation of perceptual sound features through body motion, have been carried out and analysed quantitatively. The motion of a number of participants has been recorded using state-of- the-art motion capture technologies. In order to determine the quality of the data that has been recorded, these technologies themselves are also a subject of research in this thesis. A toolbox for storing and streaming music-related data is presented. This toolbox allows synchronised recording of motion capture data from several systems, independently of system-specific characteristics like data types or sampling rates. The thesis presents evaluations of four motion tracking systems used in research on music-related body motion. They include the Xsens motion capture suit, optical infrared marker-based systems from NaturalPoint and Qualisys, as well as the inertial sensors of an iPod Touch. These systems cover a range of motion tracking technologies, from state-of-the-art to low-cost and ubiquitous mobile devices. Weaknesses and strengths of the various systems are pointed out, with a focus on applications for music performance and analysis of music-related motion. The process of extracting features from motion data is discussed in the thesis, along with motion features used in analysis of sound-tracing experiments, including time-varying features and global features. Features for realtime use are also discussed related to the development of a new motion-based musical instrument: The SoundSaber. Finally, four papers on sound-tracing experiments present results and methods of analysing people’s bodily responses to short sound objects. These papers cover two experiments, presenting various analytical approaches. In the first experiment participants moved a rod in the air to mimic the sound qualities in the motion of the rod. In the second experiment the participants held two handles and a different selection of sound stimuli was used. In both experiments optical infrared marker-based motion capture technology was used to record the motion. The links between sound and motion were analysed using four approaches. (1) A pattern recognition classifier was trained to classify sound-tracings, and the performance of the classifier was analysed to search for similarity in motion patterns exhibited by participants. (2) Spearman’s p correlation was applied to analyse the correlation between individual sound and motion features. (3) Canonical correlation analysis was applied in order to analyse correlations between combinations of sound features and motion features in the sound-tracing experiments. (4) Traditional statistical tests were applied to compare sound-tracing strategies between a variety of sounds and participants differing in levels of musical training. Since the individual analysis methods provide different perspectives on the links between sound and motion, the use of several methods of analysis is recommended to obtain a broad understanding of how sound may evoke bodily responses.

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