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      Associations between lyric and musical depth in Chinese songs: Evidence from computational modeling

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          Abstract

          Musical depth, which encompasses the intellectual and emotional complexity of music, is a robust dimension that influences music preference. However, there remains a dearth of research exploring the relationship between lyrics and musical depth. This study addressed this gap by analyzing linguistic inquiry and word count‐based lyric features extracted from a comprehensive dataset of 2372 Chinese songs. Correlation analysis and machine learning techniques revealed compelling connections between musical depth and various lyric features, such as the usage frequency of emotion words, time words, and insight words. To further investigate these relationships, prediction models for musical depth were constructed using a combination of audio and lyric features as inputs. The results demonstrated that the random forest regressions (RFR) that integrated both audio and lyric features yielded superior prediction performance compared to those relying solely on lyric inputs. Notably, when assessing the feature importance to interpret the RFR models, it became evident that audio features played a decisive role in predicting musical depth. This finding highlights the paramount significance of melody over lyrics in effectively conveying the intricacies of musical depth.

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          Most cited references68

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          An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

          Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing. (c) 2009 APA, all rights reserved.
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            Universality and diversity in human song

            What is universal about music, and what varies? We built a corpus of ethnographic text on musical behavior from a representative sample of the world’s societies, as well as a discography of audio recordings. The ethnographic corpus reveals that music (including songs with words) appears in every society observed; that music varies along three dimensions (formality, arousal, religiosity), more within societies than across them; and that music is associated with certain behavioral contexts such as infant care, healing, dance, and love. The discography—analyzed through machine summaries, amateur and expert listener ratings, and manual transcriptions—reveals that acoustic features of songs predict their primary behavioral context; that tonality is widespread, perhaps universal; that music varies in rhythmic and melodic complexity; and that elements of melodies and rhythms found worldwide follow power laws.
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              From everyday emotions to aesthetic emotions: towards a unified theory of musical emotions.

              The sound of music may arouse profound emotions in listeners. But such experiences seem to involve a 'paradox', namely that music--an abstract form of art, which appears removed from our concerns in everyday life--can arouse emotions - biologically evolved reactions related to human survival. How are these (seemingly) non-commensurable phenomena linked together? Key is to understand the processes through which sounds are imbued with meaning. It can be argued that the survival of our ancient ancestors depended on their ability to detect patterns in sounds, derive meaning from them, and adjust their behavior accordingly. Such an ecological perspective on sound and emotion forms the basis of a recent multi-level framework that aims to explain emotional responses to music in terms of a large set of psychological mechanisms. The goal of this review is to offer an updated and expanded version of the framework that can explain both 'everyday emotions' and 'aesthetic emotions'. The revised framework--referred to as BRECVEMA--includes eight mechanisms: Brain Stem Reflex, Rhythmic Entrainment, Evaluative Conditioning, Contagion, Visual Imagery, Episodic Memory, Musical Expectancy, and Aesthetic Judgment. In this review, it is argued that all of the above mechanisms may be directed at information that occurs in a 'musical event' (i.e., a specific constellation of music, listener, and context). Of particular significance is the addition of a mechanism corresponding to aesthetic judgments of the music, to better account for typical 'appreciation emotions' such as admiration and awe. Relationships between aesthetic judgments and other mechanisms are reviewed based on the revised framework. It is suggested that the framework may contribute to a long-needed reconciliation between previous approaches that have conceptualized music listeners' responses in terms of either 'everyday emotions' or 'aesthetic emotions'. © 2013 Elsevier B.V. All rights reserved.
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                Author and article information

                Contributors
                xuliang_psy@zju.edu.cn
                lihongting@zjut.edu.cn
                Journal
                Psych J
                Psych J
                10.1002/(ISSN)2046-0260
                PCHJ
                PsyCh Journal
                John Wiley & Sons Australia, Ltd (Melbourne )
                2046-0252
                2046-0260
                19 June 2024
                December 2024
                : 13
                : 6 ( doiID: 10.1002/pchj.v13.6 )
                : 915-926
                Affiliations
                [ 1 ] Department of Psychology, College of Education Zhejiang University of Technology Hangzhou China
                Author notes
                [*] [* ] Correspondence

                Liang Xu and Hongting Li, Department of Psychology, College of Education, Zhejiang University of Technology, Hangzhou 310014, China.

                Email: xuliang_psy@ 123456zju.edu.cn and lihongting@ 123456zjut.edu.cn

                Author information
                https://orcid.org/0000-0003-3889-927X
                Article
                PCHJ785
                10.1002/pchj.785
                11608776
                38898366
                35598977-fdab-4ed9-a5d1-019aa5b09583
                © 2024 The Author(s). PsyCh Journal published by Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 26 February 2024
                : 28 May 2024
                Page count
                Figures: 6, Tables: 2, Pages: 12, Words: 8800
                Funding
                Funded by: Humanities and Social Sciences Youth Foundation, Ministry of Education , doi 10.13039/501100017630;
                Award ID: 22YJC840026
                Funded by: Humanities and Social Sciences Foundation of Zhejiang University of Technology
                Award ID: SKY‐ZX‐20240008
                Funded by: National Natural Science Foundation of the People's Republic of China
                Award ID: 72371228
                Categories
                Original Article
                Original Article
                Custom metadata
                2.0
                December 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.5.1 mode:remove_FC converted:01.12.2024

                audio,lyric,machine learning,musical depth
                audio, lyric, machine learning, musical depth

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