Applying the stratified model of relevance interactions to music information retrieval

Weigl, David M.; Guastavino, Catherine

doi:10.1002/meet.14505001135

Cited by 3 publications

(3 citation statements)

References 10 publications

(12 reference statements)

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“…Twenty participants listened to randomized playlists of Weigl's (2016) dataset of 17 second excerpts from Rock/Pop/Electronic Dance Music (EDM) genres. These 24 stimuli fall equally into three categories of high, medium, and low beat salience.…”

Section: Methodsmentioning

confidence: 99%

Future Directions of Music Cognition

2021

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The Future Directions of Music Cognition conference developed parallel to many of the societal changes brought about by the COVID-19 pandemic. The result was an innovative approach to online, remote conferencing. This proceedings journal reflects the research presented at the conference and speaker series, but the virtual nature of the conference also provides opportunities for this journal to be multimedia in new and exciting ways. Most of the articles in this journal are accompanied by an online multimedia presence, with articles linked to videos of the authors presenting, slides, and access to original data and stimuli. This article reviews the story of how the conference and speaker series developed during the pandemic, some of the achievements of the conference, and how the journal is structured.

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Section: Methodsmentioning

confidence: 99%

Future Directions of Music Cognition

2021

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“…In the field of music information retrieval (MIR), research on music streaming services includes studies on improving recommendation algorithms [9][10][11], understanding user behavior and patterns of use [12][13][14][15][16], and studying user experiences and interfaces [17][18][19][20][21]. These studies aimed to enhance overall user satisfaction and engagement with music streaming services by providing personalized recommendations, improving the user interface, and identify-ing the factors that influenced user behaviors and preferences.…”

Section: Related Workmentioning

confidence: 99%

History of Curriculums of the Department of Korean Traditional Music in Seoul National University

Choi¹,

강사²

2019

Tongyang Ŭmak

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As sleep disorders are becoming more prevalent there is an urgent need to classify sleep stages in a less disturbing way. In particular, sleep-stage classification using simple sensors, such as single-channel electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), or electrocardiography (ECG) has gained substantial interest. In this study, we proposed a sleep model that predicts the next sleep stage and used it to improve sleep classification accuracy. The sleep models were built using sleep-sequence data and employed either statistical n-gram or deep neural network-based models. We developed beam-search decoding to combine the information from the sensor and the sleep models. Furthermore, we evaluated the performance of the n-gram and long short-term memory (LSTM) recurrent neural network (RNN)-based sleep models and demonstrated the improvement of sleep-stage classification using an EOG sensor. The developed sleep models significantly improved the accuracy of sleep-stage classification, particularly in the absence of an EEG sensor.

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“…Figure1: Means of groove ratings for each ofWeigl's (2016) beat salience categories Each participant's groove responses were averaged to create their Groove Perception (GP) score. The GP score indicates the participant's propensity to want to move with the music.…”

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confidence: 99%

Find your groove: a pilot study on the influences of beat salience and social connectedness on groove perception

Percival¹

2021

Future Directions of Music Cognition

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Is beat salience an accurate way to measure groove? Does personal identification with the music affect groove ratings? Weigl (2016) lists 24 17-second clips from Rock/Pop/Electronic Dance Music genres in categories of high, medium, and low beat salience. To determine if beat salience influences groove perception, participants in my pilot study completed a Likert scale for each of Weigl's clips, ranging from "1 = no need to move" to "7 = dance party!"; the results suggest that this phrasing is an effective operationalized proxy for groove and beat salience. Participants also filled out social surveys to see if a propensity towards social connectedness would influence groove perception (GP). There was a weak correlation between GP and feeling connected to the musical environment. This connected feeling was correlated with prior emotional engagement with music. Self-construal as an independent or interdependent person did not have any correlation with GP; this result suggests that the intersection of musical and social elements in groove should focus on the musical environment, rather than self-construal elements.

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Applying the stratified model of relevance interactions to music information retrieval

Cited by 3 publications

References 10 publications

Future Directions of Music Cognition

Future Directions of Music Cognition

History of Curriculums of the Department of Korean Traditional Music in Seoul National University

Find your groove: a pilot study on the influences of beat salience and social connectedness on groove perception

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