Pinocchio

Bruegge, Bernd; Teschner, Christoph; Lachenmaier, Peter; Fenzl, Eva; Schmidt, Dominik; Bierbaum, Simon

doi:10.1145/1255047.1255132

Cited by 21 publications

(5 citation statements)

References 4 publications

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“…Each experiment went for four bars. For simplicity, a few constants were chosen across the experiments: a 4 4 pattern, the most common pattern in conducting [9], and a speed comfortable to the conductor, 76 beats per minute (bpm).…”

Section: Data Collectionmentioning

confidence: 99%

“…Live Data Visualisation Combined, the system followed the movements of the baton tip. Figure 9 shows a capture of experimental use of the system, informally conducting a 4 4 path.…”

Section: Softwarementioning

confidence: 99%

“…Some attempts have been made to give conductors an individual feedback loop. One method attempted is to track a baton, using position sensors or accelerometers, and adjust an orchestra recording to match the conductor's current speed and volume [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have attempted to recreate parts of the feedback loop of the orchestra in solitary practice, primarily using mechanical and optical systems; one study varied the playback speed of a recording in line with the speed of a conductor's trajectory. By interpreting the speed of the movement of the baton, and using sharp directional changes to determine ictuses, a recording of an orchestra was varied to proportional speeds, using accelerometers [4] and pre-existing systems such as an eWatch [6]. The response of speed variation, and in some cases the visualisation of inconsistent speed, is a valuable pedagogical feedback loop.…”

Section: Introductionmentioning

confidence: 99%

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A Mechatronic System for the Visualisation and Analysis of Orchestral Conducting

Coates,

2023

Preprint

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This paper quantitatively analysed orchestral conducting patterns, and detected variations as a result of extraneous body movement during conducting, in the first experiment of its kind. A novel live conducting system featuring data capture, processing, and analysis was developed. Reliable data of an expert conductor's movements was collected, processed, and used to calculate average trajectories for different conducting techniques with various extraneous body movements; variations between extraneous body movement techniques and controlled technique were definitively determined in a novel quantitative analysis. A portable and affordable mechatronic system was created to capture and process live baton tip data, and was found to be accurate through calibration against a reliable reference. Experimental conducting field data was captured through the mechatronic system, and analysed against previously calculated average trajectories; the extraneous movement used during the field data capture was successfully identified by the system.

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Section: Data Collectionmentioning

confidence: 99%

“…Live Data Visualisation Combined, the system followed the movements of the baton tip. Figure 9 shows a capture of experimental use of the system, informally conducting a 4 4 path.…”

Section: Softwarementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Mechatronic System for the Visualisation and Analysis of Orchestral Conducting

Coates,

2023

Preprint

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“…With gesture recognition, one can link the identification of particular gestures, which might carry a semantic meaning, to the control of audio processing. Many machine learning algorithms have been applied to real-time gesture recognition, for example Hidden Markov Models (HMMs) [6,57], Dynamic Time Warping (DTW) [6], and Artificial Neural Networks (ANNs) [12]. While many methods for real-time gesture recognition have been proposed and distributed in the NIME community [40], most uses of gesture recognition are confined to discrete interaction paradigms such as triggering a musical event when a particular gesture is recognized [41].…”

Section: Interactive Machine Learning For Motion-sound Mappingmentioning

confidence: 99%

Motion-Sound Mapping through Interaction

Françoise

Bevilacqua

2018

ACM Trans. Interact. Intell. Syst.

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Technologies for sensing movement are expanding toward everyday use in virtual reality, gaming, and artistic practices. In this context, there is a need for methodologies to help designers and users create meaningful movement experiences. This article discusses a user-centered approach for the design of interactive auditory feedback using interactive machine learning. We discuss Mapping through Interaction, a method for crafting sonic interactions from corporeal demonstrations of embodied associations between motion and sound. It uses an interactive machine learning approach to build the mapping from user demonstrations, emphasizing an iterative design process that integrates acted and interactive experiences of the relationships between movement and sound. We examine Gaussian Mixture Regression and Hidden Markov Regression for continuous movement recognition and real-time sound parameter generation. We illustrate and evaluate this approach through an application in which novice users can create interactive sound feedback based on coproduced gestures and vocalizations. Results indicate that Gaussian Mixture Regression and Hidden Markov Regression can efficiently learn complex motion-sound mappings from few examples.

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