Influence of personality on music-genre exclusivity

Bansal, Jotthi; Flannery, Maya B; Woolhouse, Matthew

doi:10.1177/0305735620953611

Cited by 13 publications

(8 citation statements)

References 35 publications

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“…Dunn et al (2012) measured self-reported preferences and analyzed listening behavior from a music streaming database over a 3-month period. Their results showed a positive correlation between these two measures, an important finding that supports the validity of existing research (that used self-report measures of music preference) and inferences made in studies, such as that of Bansal, Flannery, and Woolhouse (2020), who deduced musical preference based on user’s music collections.…”

Section: Introductionsupporting

confidence: 71%

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Musical Preference: Role of Personality and Music-Related Acoustic Features

Flannery

Woolhouse

2021

Music & Science

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Personality factors, typically determined by the Big Five Inventory (BFI), have been a primary method for investigating individual preferences in music. While these studies have yielded a number of insights into musical choices, weaknesses exist, owing to the methods by which music is characterized and categorized. For example, musical genre, music-preference dimensions (e.g., reflective and complex), and musical attributes (e.g., strong and mellow), reported within the literature, have arguably produced inconsistent and thus difficult to interpret results. We attempt to circumvent these inconsistencies by classifying music using objectively quantifiable acoustic features that are fundamental to Western music, such as tempo and register. Moreover, it is our contention that the link between musical preference and personality may operate primarily at the level of acoustic features and not at broader categorization levels, such as genre. This study attempts to address this issue. Ninety participants listened to and indicated preference for stimuli that were systematically manipulated by dynamics (attack rate), mode, register, and tempo. Personality was measured using the BFI, allowing for analysis of personality traits and preference for acoustic features. Results supported the link between personality and preference for certain acoustic features. Preference with respect to dynamics was related to openness and extraversion; mode to conscientiousness and extraversion; register to extraversion and neuroticism; and tempo to conscientiousness, extraversion, and neuroticism. Though significant, these associations were relatively weak; therefore, future research could expand the number of manipulated acoustic features. Specific attempts should also aim to disentangle the effects of genre versus acoustic features on musical preferences. Personality–preference relationships at the acoustic-feature level are discussed with respect to music recommender systems and other aspects of the literature.

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Section: Introductionsupporting

confidence: 71%

“…Openness was an additional factor in Bansal et al (2020) that positively correlated to breadth of musical taste. In our study, openness related to higher ratings of stimuli with piano dynamics and of Bach and Beethoven piece .…”

Section: Discussionmentioning

confidence: 99%

Musical Preference: Role of Personality and Music-Related Acoustic Features

Flannery

Woolhouse

2021

Music & Science

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“…Therefore, when people extract the original features of music signals, they mainly extract short-term features. There are three types of short-time features ( Bansal et al, 2021 ). The music feature time domain is a kind of parameter feature, and it is directly extracted from the time-domain waveform of the music signal.…”

Section: Methodsmentioning

confidence: 99%

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

2022

Front. Psychol.

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In order to study the application of the deep learning (DL) method in music genre recognition, this study introduces the music feature extraction method and the deep belief network (DBN) in DL and proposes the parameter extraction feature and the recognition classification method of an ethnic music genre based on the DBN with five kinds of ethnic musical instruments as the experimental objects. A national musical instrument recognition and classification network structure based on the DBN is proposed. On this basis, a music library classification retrieval learning platform has been established and tested. The results show that, when the DBN only contains one hidden layer and the number of neural nodes in the hidden layer is 117, the basic convergence accuracy is approximately 98%. The first hidden layer has the greatest impact on the prediction results. When the input sample feature size is one-third of the number of nodes in the first hidden layer, the network performance is basically convergent. The DBN is the best way for softmax to identify and classify national musical instruments, and the accuracy rate is 99.2%. Therefore, the proposed DL algorithm performs better in identifying music genres.

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“…Various metadata about users are also housed in the database, including user ID, total number of downloads and user country. Because of its size and the nature of the data it contains, the database is an excellent tool for cultural and social investigations relating to music [23][24][25][26].…”

Section: Description Of the Datamentioning

confidence: 99%

Modelling song popularity as a contagious process

2021

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Popular songs are often said to be ‘contagious’, ‘infectious’ or ‘viral’. We find that download count time series for many popular songs resemble infectious disease epidemic curves. This paper suggests infectious disease transmission models could help clarify mechanisms that contribute to the ‘spread’ of song preferences and how these mechanisms underlie song popularity. We analysed data from MixRadio, comprising song downloads through Nokia cell phones in Great Britain from 2007 to 2014. We compared the ability of the standard susceptible–infectious–recovered (SIR) epidemic model and a phenomenological (spline) model to fit download time series of popular songs. We fitted these same models to simulated epidemic time series generated by the SIR model. Song downloads are captured better by the SIR model, to the same extent that actual SIR simulations are fitted better by the SIR model than by splines. This suggests that the social processes underlying song popularity are similar to those that drive infectious disease transmission. We draw conclusions about song popularity within specific genres based on estimated SIR parameters. In particular, we argue that faster spread of preferences for Electronica songs may reflect stronger connectivity of the ‘susceptible community’, compared with the larger and broader community that listens to more common genres.

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Influence of personality on music-genre exclusivity

Cited by 13 publications

References 35 publications

Musical Preference: Role of Personality and Music-Related Acoustic Features

Musical Preference: Role of Personality and Music-Related Acoustic Features

Construction of Intelligent Recognition and Learning Education Platform of National Music Genre Under Deep Learning

Modelling song popularity as a contagious process

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