Toward E-Motion-Based Music Retrieval a Study of Affective Gesture Recognition

Amelynck, Denis; Grachten, Maarten; Noorden, Leon van; Leman, Marc

doi:10.1109/t-affc.2011.39

Cited by 17 publications

(12 citation statements)

References 29 publications

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“…The results showed that subjects are able to consistently relate the musical excerpts with the reaction forces, even if the FEI metaphor seems to be more suitable for describing expressive cues in simple musical excerpts, where the expressive content is mainly related to performance cues, than in complex musical stimuli, where musical structure is more relevant. Amelynck et al (2012) asked the subjects to express the affective character of a set of musical fragments by arm movements, while listening to the fragments. The subjects' arm motion was captured using a wireless handheld device equipped with 3D inertial sensors.…”

Section: Music and Actionmentioning

confidence: 99%

Is Vivaldi smooth and takete? Non-verbal sensory scales for describing music qualities

Murari

Rodà

Canazza

et al. 2015

Journal of New Music Research

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Studies on the perception of music qualities (such as induced or perceived emotions, performance styles, or timbre nuances) make a large use of verbal descriptors. Although many authors noted that particular music qualities can hardly be described by means of verbal labels, few studies have tried alternatives. This paper aims at exploring the use of non-verbal sensory scales, in order to represent different perceived qualities in Western classical music. Musically trained and untrained listeners were required to listen to six musical excerpts in major key and to evaluate them from a sensorial and semantic point of view (Experiment 1). The same design (Experiment 2) was conducted using musically trained and untrained listeners who were required to listen to six musical excerpts in minor key. The overall findings indicate that subjects’ ratings on non-verbal sensory scales are consistent throughout and the results support the hypothesis that sensory scales can convey some specific sensations that cannot be described verbally, offering interesting insights to deepen our knowledge on the relationship between music and other sensorial experiences. Such research can foster interesting applications in the field of music information retrieval and timbre spaces explorations together with experiments applied to different musical cultures and contexts

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Section: Music and Actionmentioning

confidence: 99%

Is Vivaldi smooth and takete? Non-verbal sensory scales for describing music qualities

Murari

Rodà

Canazza

et al. 2015

Journal of New Music Research

View full text Add to dashboard Cite

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“…Furthermore, we assume that rhythmicity is more related to arousal whereas expressive properties are more related to valence which has been shown to me more diicult to predict by movement (cf. Amelynck et al [2012] or Irrgang and Egermann [2016]). Another restriction to predict complex and tonal properties stems from the fact that we did not model all changes over time in accelerometer motion data and music features since both were averaged over time.…”

Section: Discussionmentioning

confidence: 99%

“…In the beginning, participants chose one of their own songs to warm up and get familiar with the study's procedure. They were 1 www.optitrack.com 2 www.airdroid.com instructed as follows: łMove the smartphone to the music. You can move the rest of the body intuitively along with it but keep in mind that the characteristic motion must be captured by the phone.…”

Section: Main Studymentioning

confidence: 99%

“…Accordingly, the authors conclude that emotion expression in music and movement seem to be based on the same universal features. Amelynck, Grachten, van Noorden, and Leman [1] investigated whether motion features can be used to predict experienced emotion of listeners. They asked participants to perform arm gestures while holding a Ωii remote controller in order to describe their music listening experience.…”

Section: Introductionmentioning

confidence: 99%

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Smartphone-Assessed Movement Predicts Music Properties

Irrgang

Steffens

Egermann

2018

Proceedings of the 5th International Conference on Movement and Computing

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Numerous studies have shown a close relationship between movement and music [7], [17], [11], [14], [16], [3], [8]. That is why Leman calls for new mediation technologies to query music in a corporeal way [9]. Thus, the goal of the presented study was to explore how movement captured by smartphone accelerometer data can be related to musical properties. Participants (N = 23, mean age = 34.6 yrs, SD = 13.7 yrs, 13 females, 10 males) moved a smartphone to 15 musical stimuli of 20s length presented in random order. Motion features related to tempo, smoothness, size, regularity, and direction were extracted from accelerometer data to predict the musical qualities łrhythmicity", łpitch level + range" and "complexitył assessed by three music experts. Motion features selected by a 20-fold lasso predicted the musical properties to the following degrees łrhythmicity" (R 2 : .47), pitch level and range (R 2 : .03) and complexity (R 2 : .10). As a consequence, we conclude that music properties can be predicted from the movement it evoked, and that an embodied approach to Music Information Retrieval is feasible. CCS CONCEPTS • Human-centered computing → Empirical studies in interaction design; Gestural input; • Information systems → Music retrieval; • Computing methodologies → Cognitive science;

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“…Gesture Controlled MP3 player [7] developed using image processing in mat lab and edge detection algorithm. E-Motion based music retrieval [1] presented a foundation for a motion based user interface for music retrieval using wireless handheld device equipped with inertial sensors [9]. A system is developed for controlling music player [2] using the webcam for capturing the hand gestures, identifying them and finally accessing the music player.…”

Section: Introductionmentioning

confidence: 99%

Emotion based Music Retrieval for Gesture Recognition

Deepa¹,

Ram²

2013

IJCA

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Emotion based music retrieval is a music player based on user's emotion. Many music devices and mobile music players are used to listen to music. A practical problem is selection of desired music. Nowadays many devices are integrated with cameras. This paper gives how to take advantage of these one camera systems. In this proposed system emotion is derived automatically from hand gestures where hand gestures are captured by camera. The data recorded from hand gestures are coupled with musical emotion. Users can search through music collection based on emotional character of music which expressed from hand gestures. The main aim of this project is to design emotion based user interface for music retrieval. The algorithms were implemented in the system using C++. The system uses the Intel OpenCV library for image processing. This paper is implemented by using ARM9 micro controller. The camera is connected to the controller through USB device. The microcontroller is connected to PC through serial communication. The controller processes the information and monitors the results as controlled songs on PC.

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Toward E-Motion-Based Music Retrieval a Study of Affective Gesture Recognition

Cited by 17 publications

References 29 publications

Is Vivaldi smooth and takete? Non-verbal sensory scales for describing music qualities

Is Vivaldi smooth and takete? Non-verbal sensory scales for describing music qualities

Smartphone-Assessed Movement Predicts Music Properties

Emotion based Music Retrieval for Gesture Recognition

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