Search Results (40)

Folklore, as an expression of intangible cultural heritage, plays a key role in the transmission of collective memory, values, and cultural identity and has been widely recognized for its educational potential in early childhood. This study analyzes the perceptions of Early Childhood Education teachers in the province of León (Spain) regarding the pedagogical value of folklore and its use in classroom practice. A quantitative, cross-sectional, non-experimental design was employed using a validated questionnaire administered to a voluntary sample of 100 teachers from schools offering the second cycle of Early Childhood Education. The instrument assessed three dimensions: knowledge of folklore, perceived relevance of folk literature, and application in educational practice. The results indicate that teachers hold very positive attitudes toward folklore as a cultural and educational resource, particularly for children’s social, emotional, and cultural development. However, a clear gap emerges between this strong theoretical appreciation and its occasional classroom use. Significant differences were identified according to school environment, type of school, and teaching experience, indicating that contextual and professional factors influence folklore integration. Insufficient training and low self-confidence were identified as the main barriers. This study highlights the need to strengthen initial and in-service teacher education in cultural heritage and in the didactics of literature and music to promote a more meaningful and sustainable use of folklore in early childhood education. Full article

Socio-cultural tourism factors include folk music, cuisine and gastronomic brands, domestic handicrafts, crafts, folk customs, events, local tourist culture and cultural–historical heritage, language, social life of residents, and other factors. Important natural factors are the geographical and tourist location, features of relief, hydrographic potential, types of climates, plant and animal species, and others. Socio-cultural factors, together with natural factors, can create the basic characteristics of a destination. This research used the two landscapes of outstanding features (LOFs) that are part of the wider area of Serbia’s capital city, Belgrade. The selected areas are the main excursion and tourist centers, which possess significant natural and cultural characteristics for the development of sustainable tourism (STO). The main characteristics of these LOFs are forest ecosystems, which have an impact on tourism and recreation. The article used a quantitative methodology, based on the survey technique, which was used to collect data. A total of 1120 respondents were surveyed. Respondents expressed their views on claims related to space factors, which can influence the development of tourism and recreation. By analyzing the results, it can be concluded that there is an impact of factors on satisfaction with STO. Full article

This article examines two drawings by the Prussian artist Anton Möller the Elder (1563–1611), based in Danzig [Gdańsk]. In 1587, Möller spent time in Marienburg [Malbork] near Danzig, fresh from his post-apprenticeship travels. These drawings evidence his tuition in Northern Renaissance styles, subject matter, and disguised symbolism—embodying contemporary Lutheran ideologies of temperance, morality, and the powerful sway of music. While scholarship on Möller’s works is well established (mainly in Polish and German sources in brief catalogue-style entries), this article represents an in-depth analysis of the symbolism in his works—primarily missing from modern scholarship, especially in the English language. Möller’s Folk Fair before Marienburg is entertaining, sensational, and serves as a graphic warning not to fall prey to alcohol’s destruction of moral character. Möller directly copies figures from Northern Renaissance artists working in the folk fair genre—I discuss these connections and symbolism. Musicians are given an incendiary role in the scene, spurring revelers on to indulge in base emotions and vices. In stark contrast, Möller’s An Elegant Reception with Christburg [Dzierzgoń] Castle in the Background, places music at the center of the scene, depicts “active listening,” and provides a visual message on how music can cultivate a pure mind and heart, if one’s moral compass is properly attuned. Full article

The purpose of this research was to investigate whether music empirically associated with therapeutic effects contains intrinsic informational structures that differentiate it from other sound sequences. Drawing on ontology, phenomenology, nonlinear dynamics, and complex systems theory, we hypothesize that therapeutic relevance may be linked to persistent structural patterns embedded in musical signals rather than to stylistic or genre-related attributes. This paper introduces the Discriminating Music Sequences (DiMuSes) method, an unsupervised, structure-oriented analytical framework designed to detect such patterns. The method applies 24 scalar evaluators derived from statistics, fractal geometry, nonlinear physics, and complex systems, transforming sound sequences into multidimensional vectors that characterize their global temporal organization. Principal Component Analysis (PCA) reduces this feature space to three dominant components (PC1–PC3), enabling visualization and comparison in a reduced informational space. Unsupervised k-Means clustering is subsequently applied in the PCA space to identify groups of structurally similar sound sequences, with cluster quality evaluated using Silhouette and Davies–Bouldin indices. Beyond clustering, DiMuSe implements ranking procedures based on relative positions in the PCA space, including distance to cluster centroids, inter-item proximity, and stability across clustering configurations, allowing melodies to be ordered according to their structural proximity to the therapeutic cluster. The method was first validated using synthetically generated nonlinear signals with known properties, confirming its capacity to discriminate structured time series. It was then applied to a dataset of 39 music and sound sequences spanning therapeutic, classical, folk, religious, vocal, natural, and noise categories. The results show that therapeutic music consistently forms a compact and well-separated cluster and ranks highly in structural proximity measures, suggesting shared informational characteristics. Notably, pink noise and ocean sounds also cluster near therapeutic music, aligning with independent evidence of their regulatory and relaxation effects. DiMuSe-derived rankings were consistent with two independent studies that identified the same musical pieces as highly therapeutic.The present research remains at a theoretical stage. Our method has not yet been tested in clinical or experimental therapeutic settings and does not account for individual preference, cultural background, or personal music history, all of which strongly influence therapeutic outcomes. Consequently, DiMuSe does not claim to predict individual efficacy but rather to identify structural potential at the signal level. Future work will focus on clinical validation, integration of biometric feedback, and the development of personalized extensions that combine intrinsic informational structure with listener-specific response data. Full article

As a vital carrier of human intangible culture, dance plays an important role in cultural transmission through digital generation. However, existing dance generation methods rely heavily on high-precision motion capture and manually annotated datasets, and they fail to effectively model the culturally distinctive movements of Chinese ethnic folk dance, resulting in semantic distortion and cross-modal mismatch. Building on the Chinese traditional ethnic Helou Dance, this paper proposes a culture-aware Chinese ethnic folk dance generation framework, CAFE-Dance, which dispenses with manual annotation and automatically generates dance sequences that achieve high cultural fidelity, precise music synchronization, and natural, fluent motion. To address the high cost and poor scalability of cultural annotation, we introduce a Zero-Manual-Label Cultural Data Construction Module (ZDCM) that performs self-supervised cultural learning from raw dance videos, using cross-modal semantic alignment and a knowledge-base-guided automatic annotation mechanism to construct a high-quality dataset of Chinese ethnic folk dance covering 108 classes of curated cultural attributes without any frame-level manual labels. To address the difficulty of modeling cultural semantics and the weak interpretability, we propose a Culture-Aware Attention Mechanism (CAAM) that incorporates cultural gating and co-attention to adaptively enhance culturally key movements. To address the challenge of aligning the music–motion–culture tri-modalities, we propose a Tri-Modal Alignment Network (TMA-Net) that achieves dynamic coupling and temporal synchronization of tri-modal semantics under weak supervision. Experimental results show that our framework improves Beat Alignment and Cultural Accuracy by 4.0–5.0 percentage points and over 30 percentage points, respectively, compared with the strongest baseline (Music2Dance), and it reveals an intrinsic coupling between cultural embedding density and motion stability. The code and the curated Helouwu dataset are publicly available. Full article

Intangible cultural heritage (ICH) constitutes a vital component of cultural diversity and a defining element of regional identity. Understanding its spatial patterns and determinants is fundamental to informing robust conservation strategies and ensuring its continuity across generations. This research employs kernel density analysis, average nearest neighbor analysis, and Poisson regression to examine the spatial distribution patterns and determinants of 3576 national, provincial, and municipal ICH items across 21 prefecture-level cities in Guangdong Province, China. The research results show the following: (1) All ICH categories in Guangdong province exhibit a significant spatial clustering, with Quyi (Chinese folk performing arts) demonstrating the most pronounced agglomeration, followed by traditional opera and traditional music. (2) Kernel density estimates display pronounced hotspots in the Guangzhou–Foshan core of the Pearl River Delta (PRD) and in Eastern Guangdong’s Chaozhou–Shantou corridor, while each heritage category displays its own geographically distinct footprint. (3) From the perspective of natural factors, ICH items are predominantly located in areas characterized by flat topography, proximity to rivers, and a mild subtropical climate, notably the coastal regions of the PRD, Eastern Guangdong, and Western Guangdong. These areas also possess superior resource endowments and transportation infrastructure. (4) Regarding socioeconomic factors, the analysis results point out distinct socioeconomic influences. Specifically, a larger registered population and higher per capita Gross Domestic Product (GDP) correspond to more ICH items. However, two factors demonstrate negative relationships: the total resident population and the level of dialect diversity. This study systematically elucidates the spatial distribution characteristics of ICH in Guangdong Province and their key influencing factors. The outcomes offer critical empirical evidence, thereby informing the design and implementation of optimized ICH conservation measures, promoting coordinated regional cultural development, and achieving the sustainable utilization of ICH resources. Full article

Romanian popular music has had a storied history across the last century and a half. Incorporating different influences at different times, today it boasts a wide range of both autochthonous and imported genres, such as traditional folk music, rock, rap, pop, and manele, to name a few. We aim to trace the linguistic differences between the lyrics of these genres using natural language processing and a computational linguistics approach by studying the prosodic, stylistic, syntactic, and sentiment-based features of each genre. For this purpose, we have crawled a dataset of ~14,000 Romanian songs from publicly available websites along with the user-provided genre labels, and characterized each song and each genre, respectively, with regard to these features, discussing similarities and differences. We improve on existing tools for Romanian language natural language processing by building a lexical analysis library well suited to song lyrics or poetry which encodes a set of 17 linguistic features. In addition, we build lexical analysis tools for profanity-based features and improve the SentiLex sentiment analysis library by manually rebalancing its lexemes to overcome the limitations introduced by it having been machine translated into Romanian. We estimate the accuracy gain using a benchmark Romanian sentiment analysis dataset and register a 25% increase in accuracy over the SentiLex baseline. The contribution is meant to describe the characteristics of the Romanian expression of autochthonous as well as international genres and provide technical support to researchers in natural language processing, musicology or the digital humanities in studying the lyrical content of Romanian music. We have released our data and code for research use. Full article

This article closely examines the Georgian folk tale Master and Pupil, focusing on the intermedial transformation of its sequential narrative structure as an instance of oral storytelling. The tale is analyzed within the broader discourses of performativity, voice, and narrative subjectivity through the lenses of performance theory, media formalism, and the Aarne–Thompson–Uther (ATU) classification system (Type 325). The study reveals a transition in the tale from silence to vocal authority; here, voice functions not only as a means of communication but also as a vehicle for resistance, transformation, and the negotiation of identity. Master and Pupil emerges, beyond a magical apprenticeship narrative, as a multilayered performance of disembodiment and symbolic transmission through an intermedial perspective; in this context, musicality and vocality operate as liminal forces. The pupil’s acquisition of voice signifies both a narrative rupture and a restructuring of hierarchical relations. Furthermore, the article situates the tale within the broader matrix of the Georgian oral storytelling tradition, demonstrating how recurring motifs surrounding the transformation of voice reflect culturally embedded patterns of media convergence and embodied knowledge. By foregrounding the tale’s intermedial dynamics, this study reframes folk tales as a fluid site of aesthetic, cultural, and epistemic negotiations. Full article

Lord Byron’s life and poetic works have inspired musical compositions across genres even during his lifetime. The English author’s fictional characters and themes impressed nineteenth-century European composers, especially since his Byronic heroes were often conflated with their creators’ own melancholy and revolutionary personas. In contrast to Byron-inspired songs and operas, instrumental programme music has raised doubts towards a direct correlation with its poetic sources. While epigraphs help direct listeners to specific ideas, their absence has prompted dismissals of intermedial relationships, even those proposed by the composers themselves. This essay explores major connections between Hector Berlioz’s Harold in Italy, a Symphony in Four Parts with Viola Obbligato (premiered 1834), and Byron’s semi-autobiographical narrative poem Childe Harold’s Pilgrimage: A Romaunt (published 1812–1818). Although Berlioz’s titles and memoirs partially identify Byron’s Childe Harold as his inspiration, other references, including his visits to the Abruzzi mountains, his fascination with Italian folk music, his reuse of earlier material, and his reflections on brigands and solitude, have fuelled ongoing debates about the work’s programmatic content. Combining historical-biographical research, melopoetics, and musical semiotics, this essay clarifies how indefinite elements were transmitted from poetic source to musical target. Particular focus is placed on the “Harold theme”, which functions as a Byronic microcosm: a structural, thematic, and gestural condensation of Byron’s poem into music. Observing the interactions between microcosmic motifs and macrocosmic forms in Berlioz’s symphony and their poetic analogues, this study offers a new reading of how Byron’s legacy is encoded in musical terms. Full article

This paper examines the emergence of vernacular liturgies in Latin America, particularly through the incorporation of folk music in Nicaraguan Masses following the Second Vatican Council (Vatican II). In response to the Romanization of the Catholic liturgy in the nineteenth century, folk songs in local languages became a form of theological and cultural resistance, offering an alternative to the Latin-dominated liturgical tradition. Despite Vatican disapproval of certain Mass settings due to their non-traditional texts, these vernacular liturgies transcended their missionary origins, enriching both devotional practice and theological discourse. The study explores key Vatican II documents on liturgical participation, examines the role of liberation theology in framing vernacular and multilingual Masses as tools for social and religious transformation, as well as historical precedent as a lens for understanding the progression of change in the setting of the Mass, particularly in Latin America. Using the Misa Campesina, by Carlos Mejía Godoy, as a case study, the paper demonstrates how Nicaraguan folk Masses embody the intersections of ecclesial reform, cultural identity, and social justice within the broader context of Latin American liturgical innovation. Full article

The segmentation of symbolic music phrases is crucial for music information retrieval and structural analysis. However, existing BiLSTM-CRF methods mainly rely on local semantics, making it difficult to capture long-range dependencies, leading to inaccurate phrase boundary recognition across measures or themes. Traditional Transformer models use static embeddings, limiting their adaptability to different musical styles, structures, and melodic evolutions. Moreover, multi-head self-attention struggles with local context modeling, causing the loss of short-term information (e.g., pitch variation, melodic integrity, and rhythm stability), which may result in over-segmentation or merging errors. To address these issues, we propose a segmentation method integrating local context enhancement and global structure awareness. This method overcomes traditional models’ limitations in long-range dependency modeling, improves phrase boundary recognition, and adapts to diverse musical styles and melodies. Specifically, dynamic note embeddings enhance contextual awareness across segments, while an improved attention mechanism strengthens both global semantics and local context modeling. Combining these strategies ensures reasonable phrase boundaries and prevents unnecessary segmentation or merging. The experimental results show that our method outperforms the state-of-the-art methods for symbolic music phrase segmentation, with phrase boundaries better aligned to musical structures. Full article

Significant computational research has been dedicated to automatic key and mode detection in Western tonal music, particularly within the major and minor modes. However, limited research has focused on identifying alternative diatonic modes in traditional and folk music contexts. This paper addresses this gap by comparing the effectiveness of various preprocessing techniques in unsupervised machine learning for diatonic mode detection. Using a dataset of Irish folk music that incorporates diatonic modes such as Ionian, Dorian, Mixolydian, and Aeolian, we assess how different preprocessing approaches influence clustering accuracy and mode distinction. By examining multiple feature transformations and reductions, this study highlights the impact of preprocessing choices on clustering performance, aiming to optimize the unsupervised classification of diatonic modes in folk music traditions. Full article

Music generation by AI algorithms like Transformer is currently a research hotspot. Existing methods often suffer from issues related to coherence and high computational costs. To address these problems, we propose a novel Transformer-based model that incorporates a gate recurrent unit with root mean square norm restriction (TARREAN). This model improves the temporal coherence of music by utilizing the gate recurrent unit (GRU), which enhances the model’s ability to capture the dependencies between sequential elements. Additionally, we apply masked multi-head attention to prevent the model from accessing future information during training, preserving the causal structure of music sequences. To reduce computational overhead, we introduce root mean square layer normalization (RMS Norm), which smooths gradients and simplifies the calculations, thereby improving training efficiency. The music sequences are encoded using a compound word method, converting them into discrete symbol-event combinations for input into the TARREAN model. The proposed method effectively mitigates discontinuity issues in generated music and enhances generation quality. We evaluated the model using the Essen Associative Code and Folk Song Database, which contains 20,000 folk melodies from Germany, Poland, and China. The results show that our model produces music that is more aligned with human preferences, as indicated by subjective evaluation scores. The TARREAN model achieved a satisfaction score of 4.34, significantly higher than the 3.79 score of the Transformer-XL + REMI model. Objective evaluation also demonstrated a 15% improvement in temporal coherence compared to traditional methods. Both objective and subjective experimental results demonstrate that TARREAN can significantly improve generation coherence and reduce computational costs. Full article

This study focuses on the construction and evaluation of a high-quality Chinese Manchu music dataset designed to facilitate Artificial Intelligence (AI) research and applications within cultural heritage and ethnomusicology. Through a systematic collection and organization of diverse Manchu music resources, including folk songs, dance music, and ceremonial pieces, this dataset effectively represents the cultural breadth of Manchu music. The dataset includes digitized and preprocessed audio data, with comprehensive metadata annotations, such as essential information, musical features, and cultural context, creating a robust foundation for AI-based analysis. Experimental evaluations highlight the dataset’s utility across various AI-driven applications: in music classification, using a CNN model, an accuracy of 90% was achieved in the “folk ensemble” category, with an overall accuracy of 85.7% and a precision of 82.3%. For music generation, a Generative Adversarial Network (GAN) model yielded a quality score of 7.8/10 and a Fréchet Audio Distance (FAD) of 0.32. In emotion recognition, the Random Forest model achieved 87% accuracy in identifying the emotion “joy”. These results underscore the dataset’s potential in supporting digital preservation and expanding AI applications in ethnic music classification, generation, and emotional analysis, contributing to both cultural heritage preservation and AI advancement in ethnomusicology. Full article

With the processes of urbanization and population migration in China, local traditional folk beliefs are facing a crisis of inheritance, including the loss of believers and the decline of religious buildings. However, in the southeastern coastal areas of China, with the development of society and the advancement of science and technology, folk beliefs have shown a trend of modernization, gained widespread attention from young people, and shown a good trend of inheritance. This study focuses on the modernization of folk beliefs in the southeastern coastal areas of China, exploring how folk beliefs are adapted to contemporary life and the key role of information technology in the protection of religious buildings. The study found that the modernization of folk beliefs in China’s southeastern coastal areas is mainly reflected in the portrayal of gods in cartoonish images, the popularity of music, and the modernization of communication methods. By analyzing the modernization process of folk beliefs in the southeastern coastal areas of China, this study reveals the adaptation and development of folk beliefs in modern society. Based on the reality of contemporary Chinese society, this study also explores the future modernization trend of folk beliefs and discusses the possibilities and potential risks of the application of digital technology in folk belief inheritance. Full article