Musical Style Research Articles

What do music listening tests assess? Applying the Rasch testlet models to the HKDSE data

This study examines the adequacy of a general proficiency in music listening for describing how examinees responded to exam questions on various music genres based on an empirical investigation of a public music listening examination. In the Hong Kong Diploma of Secondary Education Examination (HKDSE), Music consists of three mandatory components: listening, performing, and creating. Notably, the listening component is evaluated through a written examination, allowing for objective scoring. A candidate’s performance on this exam has been summarized as a single score, a method that is effective for evaluation purposes, while the underlying conceptualization may oversimplify the depth of knowledge required for assessment. Consequently, we conducted a reevaluation of the HKDSE Music listening test, employing the Rasch testlet model to explore potential multidimensional structures. Our findings highlighted distinctive attributes in questions related to Cantonese opera, setting them apart from others. We discuss the implications of these findings for music assessment.

Study on the Emotional Expression and Playing Skills of Mozart's Piano Trio in B Flat Major

This paper focuses on the study of the emotional expression and playing skills of Mozart's piano trio in B flat Major (K.502), aiming to deeply analyze the artistic connotation and performance practice of this classical music masterpiece. Mozart played a role in establishing the "template" in the piano trio music genre. In his creation, the three instruments in the piano trio were truly independent and unique, and the theme of the three voices began to resonate and depend on each other, one after another. He also established the three-movement pattern of piano trio writing and the musical structure of the first and last movements. Through the detailed analysis of the emotional level and performance skills of the work, it provides a more abundant understanding and deductive perspective for music scholars, performers and lovers. The purpose of this study is to systematically explore the ways of emotional expression and the subtleties of performance skills through literature review, music score analysis and field performance experiments. The research process integrates the multi-disciplinary methods of music theory, emotional psychology and performance practice. The conclusion is that Mozart's piano trio in B flat major not only shows his outstanding creative talent, but also provides a broad stage for players to show their personal emotional understanding and technical level.

Artificial neural networks and medieval music

Abstract This article delves into the integration of deep learning and medieval musicology. The article explores the use of deep learning in analyzing and transcribing medieval music manuscripts, offering insights into historical musical styles and compositions. It addresses the challenges and opportunities of applying AI in medieval musicology, and provides a critical analysis of deep learning’s potential in enhancing our understanding of medieval music heritage. The discussion encompasses the study of medieval music and its cultural context, emphasizing the significance of manuscripts, treatises and iconography in musicological research. The integration of digital humanities and computational advancements in medieval musicology is highlighted, revealing how technological progress has been incorporated systematically into the study of complex cultural objects.

Establish a novel framework for enhancing minority music genre identification

The objective of this study is to develop a new framework based on Waterwheel Plant optimization for improving minority music genre classification using Layer-tuned Long Short-Term Memory (WP-LT-LSTM). Chinese minority music includes various musical styles of different ethnic groups in China. It depends on the specific instrumentalities, the distribution of pitch classes and rhythms, and the culture. Specifically, the proposed framework will enhance the ability to efficiently detect under represented music genres, which could have applicability for cultural sustainability and more personalized music recommendation services. For this, we collected a dataset that includes a wide range of different minority music samples in audio format. These include genre labels, artist information and audio features necessary for the training of our suggested model. Using K-fold cross validation to enhances the accuracy. Min-max Normalization is used on the obtained data to perform pre-processing. To extract the important features from the processed data, we used Mel-frequency cepstral coefficients (MFCCs). In our proposed model, the WP algorithm dynamically adjusts LT-LSTM’s internal parameters, enhancing model adaptability. LT-LSTM processes sequential audio data, capturing temporal dependencies crucial for genre classification in minority music genres. The implemented model is executed in Python software. It evaluates the model’s performance across a range of parameters throughout the result analysis phase. We also performed comparison studies using standard methods. The results collected indicate the excellence and effectiveness of the proposed framework for music genre identification.

Human motion and musical expression: Exploring the link between physical movement and emotional resonance in performance

Musical performance has traditionally been understood as an auditory experience, with emotions conveyed primarily through sound. However, emerging research highlights the role of physical movement—gestures, posture, and body language—in enhancing emotional expression and creating a deeper connection between performers and their audiences. While existing studies have explored some aspects of this relationship, there is limited understanding of how specific movements modulate emotional responses across different musical genres. This study addresses these gaps by investigating the interaction between human motion and musical expression, focusing on the emotional resonance experienced by performers and audience members. The need for this study arises from the lack of comprehensive data on the correlation between movement and emotional engagement during musical performances, particularly concerning different genres. Using motion capture, biometric sensors, and facial recognition technology, the research analyzed the performances of professional and amateur musicians across classical, jazz, and contemporary music. Audience members’ emotional responses were captured through physiological data and post-performance surveys. One limitation of this study is that it focused solely on live performance environments, leaving digital or virtual performances unexplored. Key findings reveal that expressive movements increased emotional intensity ratings by 1.6 points (p < 0.001), with heart rate rising by 6.9 BPM (p < 0.0012) and electrodermal activity increasing by 1.6 µS (p < 0.0008). Movement synchronization with musical elements, especially during climaxes, strongly correlated with heightened emotional responses (r = 0.85, p < 0.01). Classical performances exhibited the highest synchronization between movement and emotion, with audience emotional intensity peaking at 8.3 on a Likert scale.

Biomechanical and machine learning approaches to automating the identification of musical styles and emotions through human motion analysis

This study explores the intricate relationship between biomechanical movements and musical expression, focusing on the identification of musical styles and emotions. Violin performance is characterized by complex interactions between physical actions—such as bowing techniques, finger placements, and posture—and the resulting acoustic output. Recent advances in motion capture technology and sound analysis have enabled a more objective examination of these processes. However, the current literature frequently addresses biomechanics and acoustic features in isolation, lacking an integrated understanding of how physical movements translate into specific musical expressions. Machine Learning (ML), particularly Long Short-Term Memory (LSTM) networks, provides a promising avenue for bridging this gap. LSTM models are adept at capturing temporal dependencies in sequential data, making them suitable for analyzing the dynamic nature of violin performance. In this work, they have proposed a comprehensive model that combines biomechanical analysis with Mel-spectrogram-based LSTM modeling to automate the identification of musical styles and emotions in violin performances. Using motion capture systems, Inertial Measurement Units (IMUs), and high-fidelity audio recordings, we collected synchronized biomechanical and acoustic data from violinists performing various musical excerpts. The LSTM model was trained on this dataset to learn the intricate connections between physical movements and the acoustic features of each performance. Key findings from the study demonstrate the effectiveness of this integrated approach. The LSTM model achieved a validation accuracy of 92.5% in classifying musical styles and emotions, with precision, recall, and F1-score reaching 94.3%, 92.6%, and 93.4%, respectively, by the 100th epoch. The analysis also revealed strong correlations between specific biomechanical parameters, such as shoulder joint angle and bowing velocity, and acoustic features, like sound intensity and vibrato amplitude.

Relationship between personality and music preference: Psychometric adaptation of the music preference scales in a Chinese sample

Individuals evidently have different tastes in and preferences for music. The purpose of this study was to develop two scales for measuring music preference based on genres (text-based) and excerpts (clip-based), using a Chinese sample to explore the relationship between music preference and personality. Specifically, we collected measures of people’s self-reported preferences for a wide range of music genres for further analysis in Study 1. In Study 2, we developed two music preference scales based on the genres identified in Study 1, one text- and the other clip-based. The results of two principal component analyses suggest three-dimensional psychological structures of music preference using both versions of the scale: (1) Traditional and Retro, (2) Exotic and Complex, and (3) Intense and Rebellious. Correlation and regression analyses of music preferences and personality also indicated that music preference is a relatively stable construct that reflects personality traits. Moreover, the three factors identified from the results of studying music preferences in a Chinese sample differ from those identified in studies of music preference in Western countries, suggesting that music preference is influenced by culture. The two scales yielded similar but slightly different outcomes reflecting their discrete emphases and characteristics; they can be used separately or together, depending on the research topic and question. In this study we verified the structure of music preference among a Chinese sample. It provides a reference point for theoretical research on music preference and personality and has practical applications.

"Karabakh or Death, no Other Way!": Hip Hop and War in Post-Soviet Azerbaijan

In post-Soviet Azerbaijan, hip hop is the main musical genre that articulates the ongoing Nagorno-Karabakh conflict that escalated into two wars (1992-94; 2020). The creativity of hip hop artists is a prime example of intertextuality as they reference, quote, and remake texts of previous Azerbaijani hip hop songs, thus offering accounts of the way the conflict unfolded throughout history. Yet their creativity goes further than providing narratives: it becomes a powerful tool to promote nationalist imagery and inspire patriotism. In this paper, I rely on interviews and first-hand experience while doing fieldwork in Azerbaijan when the Second Karabakh War erupted, and analyze popular hip hop songs, focusing on discourses that arise as part of hip hop’s generic intertextuality. Specifically, I focus on the figure of the martyr that continuously reappears to accentuate the topic of sacrifice and to trigger affects at the core of mobilization and patriotism.

A multimodal understanding of the role of sound and music in gendered toy marketing.

Literature in music theory and psychology shows that, even in isolation, musical sounds can reliably encode gender-loaded messages. Musical material can be imbued with many ideological dimensions and gender is just one of them. Nonetheless, studies of the gendering of music within multimodal communicative events are sparse and lack an encompassing theoretical framework. The present study attempts to address this literature gap by employing a critical quantitative analysis of music in gendered toy marketing, which integrated a content analytical approach with multimodal affective and music-focused perceptual responses. Ratings were collected on a set of 606 commercials spanning a ten-year time frame and strong gender polarization was observed in nearly all of the collected variables. Gendered music styles in toy commercials exhibit synergistic design choices, as music in masculine-targeted adverts was substantially more abrasive-louder, more inharmonious, and more distorted-than in feminine-targeted ones. Thus, toy advertising music appeared deliberately and consistently in line with traditional gender norms. In addition, music perceptual scales and voice-related content analytical variables explain quite well the heavily polarized affective ratings. This study presents a empirical understanding of the gendering of music as constructed within multimodal discourse, reiterating the importance of the sociocultural underpinnings of music cognition. We provided a public repository with all code and data necessary to reproduce the results of this study on github.com/marinelliluca/music-role-gender-marketing.

Exploring Spotify's Music Popularity Dynamics and Forecasting with Machine Learning

Abstract. Spotify, one of the largest music streaming service providers, boasts a vast user base and an extensive music catalog. To gain a deeper understanding of Spotify users' music preferences and behavioral patterns, this paper conducts a thorough analysis and explores the correlation between music features and user behavior. It collects Spotify users' music listening data and extracts various music features such as energy, danceability, valence, and beats per minute (BPM). These features not only reflect the musical style and rhythm but also potentially correlate with users' preferences and listening habits. Then we employ two machine learning models, Decision Trees and Random Forests, to model and analyze Spotify users' behavior data. Through these models, we can accurately identify potential relationships between music features and user behavior. The experimental results indicate that music features such as energy, danceability, valence, and BPM have a significant impact on users' music selection and preferences. By analyzing the most popular songs on Spotify over different time periods, we discover that songs with high energy, danceability, and valence tend to be more popular among users. This finding not only validates the correlation between music features and user behavior but also reveals the user preferences and market demands behind music trends. For music streaming service providers, understanding users' music preferences and behavioral patterns is crucial. By deeply analyzing user data, music streaming services can better satisfy users' needs, enhance user experience, and stand out in the fiercely competitive market. These findings have important practical implications for music streaming service providers and provide new insights and methodologies for the research and development of the music industry.

Efficient Music Genre Recognition Using ECAS-CNN: A Novel Channel-Aware Neural Network Architecture.

In the era of digital music proliferation, music genre classification has become a crucial task in music information retrieval. This paper proposes a novel channel-aware convolutional neural network (ECAS-CNN) designed to enhance the efficiency and accuracy of music genre recognition. By integrating an adaptive channel attention mechanism (ECA module) within the convolutional layers, the network significantly improves the extraction of key musical features. Extensive experiments were conducted on the GTZAN dataset, comparing the proposed ECAS-CNN with traditional convolutional neural networks. The results demonstrate that ECAS-CNN outperforms conventional methods across various performance metrics, including accuracy, precision, recall, and F1-score, particularly in handling complex musical features. This study validates the potential of ECAS-CNN in the domain of music genre classification and offers new insights for future research and applications.

Anti-Pastoral and the Prophetic Mode in Moddi’s Climate Songs

This article explores aspects of apocalypse and pastoral in the climate themed song lyrics of Norwegian indie folk artist Moddi. Through close listening and contextualizing of the songs “Noens ark” (“Someone’s ark”, 2013) and “En sang om fly” (“A song about planes”, 2013), apocalypse and pastoral emerge as key motifs. These are navigated in an exploration of a conflicted Norwegian response to global warming. The songs express critique of unrelenting pursuits of economic growth and personal consumption even as the future imposes itself on the present. In two different ways, elements of indie folk music are tailored to express an ontologically unsettling encounter with a global environmental risk scenario. “Noens ark” employs forceful instrumentation and polyrhythms to evoke a sense of alarm and urgency called for by the projection of future disaster. “En sang om fly” is a soft composition projecting nostalgia for the material conditions of the present. The desire for these conditions ultimately locks the singer in a cycle of repetition that destabilizes the very conditions he desires. The nuances afforded by this expression in musical performance allows song lyrics to sustain contradictory attitudes towards globalization. In their pursuit of these contradictions, Moddi’s climate songs can be seen as part of an anti-pastoral tradition. Features of musical genre and a prophetic lyric mode are employed to evoke and subvert a pastoral response to apocalypticism. By musically sustaining contradictions between pastoral and apocalypse, Moddi’s songs reflect a conflict between Norwegian identity and a global cultural imaginary in the face of global warming.

The Effect of Music Distraction on Dental Anxiety During Invasive Dental Procedures in Children and Adults: A Meta-Analysis.

Background: Dental anxiety and odontophobia are common issues, leading to challenges with oral hygiene and dental health. Music distraction offers an effective and side effect-free solution to alleviate pain and increase the acceptability of dental treatments. Our meta-analysis aimed to assess the efficacy of music distraction in reducing patient anxiety during invasive dental procedures in children and adults. Methods: The PubMed, Web of Science, and Embase databases were searched for clinically controlled trials, using the keywords "music" and "dental anxiety". The main outcome measured was the anxiety score. A meta-analysis was conducted using a random-effects model to estimate the standardized mean differences (SMDs). The subgroup analyses were conducted based on age groups, music preferences, and music styles. The research protocol has been registered with PROSPERO (Registration ID: CRD42022357961). Results: A total of 24 controlled clinical trials involving 1830 participants met the inclusion criteria for the meta-analysis. Music distraction significantly reduced dental anxiety during invasive procedures under local anesthesia (SMD, -0.50; 95% CI, -0.80 to -0.21; p = 0.0009; I2 = 83%). Our subgroup analysis revealed that music distraction was more effective in adults (SMD, -0.51; p = 0.0007) than in children (SMD, -0.47; p = 0.13) in reducing dental anxiety. Regarding music selection, music chosen by the participant (SMD, -1.01; p = 0.008) demonstrated more anxiolytic effects than by the author (SMD, -0.24; p = 0.02). Regarding music styles, classical music (SMD, -0.69; p = 0.009) was associated with better anxiolytic effects in adults. Conclusions: Our meta-analysis supports the use of music to alleviate dental anxiety during invasive procedures. Listening to classical or customized music can serve as an effective adjunct to outpatient surgical care in dental clinics.

Listen to your heart: The effect of music on heart rate variability

Abstract Background Heart rate variability (HRV) is indicative of autonomic balance. High HRV during sinus rhythm is associated with reduced morbidity and mortality across a wide range of health disorders. Listening to music has been shown to induce psychological and physiological responses that increase HRV, a mechanism currently employed in music therapy to reduce anxiety and post-operative pain. However, it remains unclear which music characteristics have the most positive effect on HRV. Purpose This study aimed to assess the impact of different music genres, white noise, tempo, and personal music preferences on HRV. Methods Adults without atrial fibrillation or pacemaker dependency were included during the Netherlands largest three-day music festival. Participants were subjected to three-minute fragments of white noise, and subsequently a randomized order of classical, rock, electronic and the subject’s most-listened personal ("Spotify Wrapped 2022") music; all having an either fast (130-140 BPM) or slow tempo (70-80 BPM), which was randomized as well. Afterwards, participants ranked all music fragments in order of their preference. A tachogram was collected using the PPG sensor of an Apple Watch Series 8. Data was pre-processed to exclude non-sinus rhythm beats and measurement artefacts. HRV time-domain (SDNN, RMSSD, NN50, pNN50, triangular index) and frequency-domain (LF, LFn, HF, HFn, LF/HF) metrics were calculated from the tachograms using Python-software. HRV outcomes were corrected for age, sex, heart rate and music tempo group and analyzed using general linear mixed models in SPSS. Results In total 242 participants with a median age of 28 years (IQR 24-32) were included, of which 136 (56%) female. Classical music showed a significantly higher HRV response (RMSSD) compared to electronic (p=0.025) and the subject’s personal music (p=0.030, see figure). No other significant differences in HRV were found between the different genres and personal music. White noise showed a significantly higher HRV for all time domains, as well as LF and HF, compared to the different genres and personal music. A total of 181 (74.8%) participants ranked their personal music fragment as the most preferred. There was no difference between the fast and slow tempo group when performing an interaction analysis for tempo and the different music genres except for classical and personal music (RMSSD, p = 0.047). Conclusions This large study highlights that classical music enhances HRV more than other music, suggesting its therapeutic potential. Remarkably, white noise showed the greatest increase in HRV. Although these were bitter pills to swallow; white noise or classical music once a day might keep the doctor away.

Stochastic properties of musical time series

Musical sequences are correlated dynamical processes that may differ depending on musical styles. We aim to quantify the correlations through power spectral analysis of pitch sequences in a large corpus of musical compositions as well as improvised performances. Using a multitaper method we extend the power spectral estimates down to the smallest possible frequencies optimizing the tradeoff between bias and variance. The power spectral densities reveal a characteristic behavior; they typically follow inverse power laws (1/f β-noise), yet only down to a cutoff frequency, where they end in a plateau. Correspondingly the pitch autocorrelation function exhibits slow power law decays only up to a cutoff time, beyond which the correlations vanish. We determine cutoff times between 4 and 100 quarter note units for the compositions and improvisations of the corpus, serving as a measure for the degree of persistence and predictability in music. The histogram of exponents β for the power law regimes has a pronounced peak near β = 1 for classical compositions, but is much broader for jazz improvisations.

Reality TV, Nigerian Idol, and cultural preferences for Afrobeats music

ABSTRACT Reality TV, particularly Music Reality Shows (MRSs) like Nigerian Idol, has emerged as a significant dimension of television programming, providing a platform for cultural representation and interactivity. Afrobeats, a music genre that originated in West Africa, has gained global acceptance and recognition in recent years with great influence on the global cultural landscape. This study examined the cultural preferences of viewers towards the use of Afrobeats music in the Nigerian Idol reality TV show. Cultivation theory was adopted as the theoretical framework to investigate the level of viewership of Nigerian Idol among residents of Lagos, Nigeria, and explore their cultural preferences for Afrobeats music. Through a mixed methods approach involving a quantitative survey and focus group discussions (FGD), the study gathered data from residents of Lagos, Nigeria. The results reveal a reduction in viewership of Nigerian Idol compared to previous seasons, indicating shifting audience interests. However, there is a significant cultural preference for Afrobeats music compared to other genres of music, particularly those of foreign origin, among viewers who have encountered the Nigerian Idol. This also confirms the growing influence and acceptance of the fast-rising Afrobeats genre. The findings underscore the role of media, particularly television programmes such as Reality TV Shows, in shaping cultural identity and preferences. The correlation analysis confirms a strong relationship between viewership levels and cultural preferences for Afrobeats music in Nigerian Idol, highlighting the impact of television programming on audience perceptions and tastes for cultural artifacts like music. The study contributes to understanding cultural dynamics in the context of Reality TV Shows, music, and cultural entertainment, emphasizing the need for producers to adapt to evolving audience preferences and embrace digital innovations to sustain viewer interest.

Research on music genre recognition method based on deep learning

In this paper, we explore music genre recognition using deep learning methods, examining the application of feature extraction, model construction, and performance evaluation for different music genres. During the data preparation and preprocessing stages, data augmentation and normalization techniques were employed to enhance the model’s generalization capabilities. By constructing multilayer convolutional neural networks (CNNs) and recurrent neural networks (RNNs), we achieved automatic recognition of music genres. In the experimental results analysis, we compared the accuracy and training time of different models, validating the effectiveness of deep learning in the field of music genre recognition. The limitations of deep learning methods and future research directions are also discussed, providing a reference for further studies in music information processing. This study delves into the issue of music genre recognition and proposes a deep learning-based approach. This method leverages neural networks to extract features and learn from audio data, enabling accurate classification of different music genres. Extensive experiments have demonstrated that our method achieves highly satisfactory results in music genre recognition tasks. Furthermore, we optimized the deep learning models, improving their generalization capabilities and accuracy. Our research offers the music industry an efficient and accurate method for music genre recognition, providing new perspectives and technical support for research and applications in the music field.

Sonic ontology of diasporicity: The case of Punjabi gangster music in Canada and Punjab

ABSTRACT Bhangra is a musical genre that indexes the traditional sonic identity of Punjab. The dhol and tumbi evoke an agrarian lifestyle that is the pride of the Punjabi Jatt, the traditional landlord caste. Since 2019, however, Punjabi gangster music has emerged to reflect a new conception of Punjabi diasporic subjectivity that has shifted the Punjabi sonic imagination of home from Punjabi fields to the Western city. This article contextualizes the rise of Punjabi gangster music within crises in agriculture and substance abuse among Punjabi youth, alongside an accelerating exodus from Punjab of young Punjabis seeking education and work in Canada.

Studying Abroad as a Means of Preparing Future Music Teachers in China

The concept of studying abroad as a means of preparing future music teachers in China has a rich and complex history, reflecting the nation’s evolving educational philosophies, political changes, and cultural exchanges. The purpose of this study is to present the genesis of using studying abroad as part of the professional training for future music teachers, detailing the goals and social mandates across various stages of Chinese state development. The article also draws attention to the organic "embeddedness" of such a means of training future music teachers in the Chinese system of professional and higher music-pedagogical education, especially of vocal practices that are not traditional for Chinese culture. Methodology. The study employs a historical-analytical approach, examining primary and secondary sources to trace the evolution of studying abroad as a practice for Chinese music educators. This includes an analysis of normative documents and policy papers available on official educational authority and institution websites. Additionally, qualitative data from interviews with contemporary music educators who have studied abroad provide insights into current practices and perceptions. The findings reveal that the practice of studying abroad for music education, primarily for personal music career development and secondarily for teaching, has been prevalent throughout Chinese history, and has undergone significant transformations over the past century. In the modern period, China continues to send a significant number of students abroad, supported by initiatives such as the China Scholarship Council and ‘One Belt – One Road’ Initiative. These programs facilitate grant support and promote cultural exchange and international cooperation, enriching the professional development of future music teachers in China. The study concludes that international educational experiences have profoundly impacted Chinese music education. Educators trained abroad have introduced diverse musical styles, innovative teaching methodologies, and a global perspective that enhance the learning experience of Chinese students. The integration of international practices has elevated the standards of music education in China, and gained global recognition. However, challenges remain in balancing traditional Chinese music with Western influences, and ensuring equitable access to international education opportunities. The study highlights the need for continuous adaptation and the development of strategies to address these challenges, ensuring that the benefits of studying abroad are fully realised in the Chinese educational context.

PENGELOLAAN KOLEKSI MUSEUM MUSIK INDONESIA BERBASIS APLIKASI OPEN SOURCE

The Indonesian Music Museum stores various music collections in the form of cassettes, compact discs, vinyl records, and music-related books. This self-managed museum has the potential to become an institution that stores musical works from various countries. Management has been carried out properly, starting from inventory of collections to registration, but still manually. The implementation of the Senayan Library Management System (SLiMS), which is an open source based application, is one solution for managing collections more quickly and making it possible to access them remotely. The method used in this research is a qualitative study using observations and interviews so that it can produce an appropriate management system for the museum. The main informants are museum managers who are considered to understand the metadata needs of their museum collections. All results of observations and interviews are used as guidelines in determining any changes to the applications used to manage collections at the Indonesian Music Museum. This research produces an open source application-based museum collection inventory system. Several changes and adjustments were made to this application so that it could be implemented in the museum. Terms referring to library collections were changed and adapted to the use of terms relevant to music collections. The grouping of collections based on music genres and artists' regions of origin which is usually carried out by the museum is still maintained to become the characteristic of this museum itself. The Indonesian Music Museum can implement this application in a sustainable and long-term manner. Using this system helps the museum to obtain data records of collections and their movements, as well as inventory data on collections currently owned.