Hasil untuk "Music"

Mohammad Shaharyar Ahsan

This paper presents a computational framework for analyzing how popular songwriting encodes and reflects cultural memory across seven decades. Using a corpus of 353 Grammy Song of the Year nominees from 1960 to 2025, this study employs a triangulated methodology combining sentiment analysis, lexical structure examination, and musicological feature analysis to trace systematic shifts in songwriting craft. The findings reveal a pronounced affective inversion: the lyrical optimism that peaked in the 1980s has given way to sustained pessimism in the 2010s and 2020s. This emotional shift is accompanied by measurable structural transformations, including increased lexical complexity, declining adherence to grammatical conventions, and heightened emphasis on repetitive hooks designed for viral memorability. Concurrently, analysis of sonic features demonstrates a migration towards high-energy, electronically produced soundscapes. By integrating digital humanities methods with close reading of case studies, this research demonstrates how peer-curated popular music serves as a quantifiable archive of generational sentiment, linguistic evolution, and cultural zeitgeist. The study contributes a replicable mixed-methods approach for understanding songwriting as both artistic craft and cultural artifact, offering insights into how popular music adapts to and reflects the emotional and linguistic landscape of its time. Cet article propose un cadre computationnel pour analyser la manière dont l’écriture de chansons populaires encode et reflète la mémoire culturelle au cours de sept décennies. En utilisant un corpus de 353 titres nommés pour le Grammy de la chanson de l’année entre 1960 et 2025, cette étude adopte une méthodologie triangulée combinant analyse de sentiment, examen de la structure lexicale et analyse de caractéristiques musicologiques afin de retracer les évolutions systématiques de l’artisanat d’écriture musicale. Les résultats révèlent une inversion affective marquée : l’optimisme lyrique ayant atteint son apogée dans les années 1980 a cédé la place à un pessimisme durable dans les années 2010 et 2020. Ce basculement émotionnel s’accompagne de transformations structurelles mesurables, notamment une complexification lexicale accrue, une diminution de l’adhésion aux conventions grammaticales et une insistance renforcée sur des refrains répétitifs conçus pour favoriser la mémorabilité virale. Parallèlement, l’analyse des caractéristiques sonores met en évidence une migration vers des paysages sonores énergétiques, produits électroniquement. En intégrant des méthodes propres aux humanités numériques à une lecture rapprochée d’études de cas, cette recherche démontre comment la musique populaire choisie par les pairs fonctionne comme une archive quantifiable du sentiment générationnel, de l’évolution linguistique et de l’esprit culturel d’une époque. L’étude propose une approche reproductible, fondée sur des méthodes mixtes, pour comprendre la création de chansons à la fois comme un artisanat artistique et comme un artefact culturel, offrant ainsi des perspectives sur la manière dont la musique populaire s’adapte et reflète le paysage émotionnel et linguistique de son époque.

Meng Yang, Jon McCormack, Maria Teresa Llano et al.

Recent advances in multimodal large language models (MLLM) for audio music have demonstrated strong capabilities in music understanding, yet symbolic music, a fundamental representation of musical structure, remains unexplored. In this work, we introduce MIDI-LLaMA, the first instruction-following MLLM for symbolic music understanding. Our approach aligns the MIDI encoder MusicBERT and Llama-3-8B via a two-stage pipeline comprising feature alignment and instruction tuning. To support training, we design a scalable annotation pipeline that annotates GiantMIDI-Piano with fine-grained metadata, resulting in a MIDI-text dataset. Compared with the baseline trained on converting MIDI into ABC notation under the same instruction-tuning procedure, MIDI-LLaMA substantially outperforms in captioning and semantic alignment in question answering. Human evaluation further confirms the advantages of MIDI-LLaMA in music understanding, emotion recognition, creativity, and overall preference. These findings demonstrate that incorporating symbolic music into large language models enhances their capacity for musical understanding.

Seonghyeon Go, Yumin Kim

Recently, the problem of music plagiarism has emerged as an even more pressing social issue. As music information retrieval research advances, there is a growing effort to address issues related to music plagiarism. However, many studies, including our previous work, have conducted research without clearly defining what the music plagiarism detection task actually involves. This lack of a clear definition has slowed research progress and made it hard to apply results to real-world scenarios. To fix this situation, we defined how Music Plagiarism Detection is different from other MIR tasks and explained what problems need to be solved. We introduce the Similar Music Pair dataset to support this newly defined task. In addition, we propose a method based on segment transcription as one way to solve the task. Our demo and dataset are available at https://github.com/Mippia/ICASSP2026-MPD.

Sumit Kumar, Suraj Jaiswal, Parampreet Singh et al.

The advancement of machine learning in audio analysis has opened new possibilities for technology-enhanced music education. This paper introduces a framework for automatic singing mistake detection in the context of music pedagogy, supported by a newly curated dataset. The dataset comprises synchronized teacher learner vocal recordings, with annotations marking different types of mistakes made by learners. Using this dataset, we develop different deep learning models for mistake detection and benchmark them. To compare the efficacy of mistake detection systems, a new evaluation methodology is proposed. Experiments indicate that the proposed learning-based methods are superior to rule-based methods. A systematic study of errors and a cross-teacher study reveal insights into music pedagogy that can be utilised for various music applications. This work sets out new directions of research in music pedagogy. The codes and dataset are publicly available.

Jiajia Li, Jiliang Hu, Ziyi Pan et al.

Recently, there have been significant advancements in music generation. However, existing models primarily focus on creating modern pop songs, making it challenging to produce ancient music with distinct rhythms and styles, such as ancient Chinese SongCi. In this paper, we introduce SongSong, the first music generation model capable of restoring Chinese SongCi to our knowledge. Our model first predicts the melody from the input SongCi, then separately generates the singing voice and accompaniment based on that melody, and finally combines all elements to create the final piece of music. Additionally, to address the lack of ancient music datasets, we create OpenSongSong, a comprehensive dataset of ancient Chinese SongCi music, featuring 29.9 hours of compositions by various renowned SongCi music masters. To assess SongSong's proficiency in performing SongCi, we randomly select 85 SongCi sentences that were not part of the training set for evaluation against SongSong and music generation platforms such as Suno and SkyMusic. The subjective and objective outcomes indicate that our proposed model achieves leading performance in generating high-quality SongCi music.

Jinhan Xu, Xing Tang, Houpeng Yang et al.

Symbolic music generation is a challenging task in multimedia generation, involving long sequences with hierarchical temporal structures, long-range dependencies, and fine-grained local details. Though recent diffusion-based models produce high quality generations, they tend to suffer from high training and inference costs with long symbolic sequences due to iterative denoising and sequence-length-related costs. To deal with such problem, we put forth a diffusing strategy named SMDIM to combine efficient global structure construction and light local refinement. SMDIM uses structured state space models to capture long range musical context at near linear cost, and selectively refines local musical details via a hybrid refinement scheme. Experiments performed on a wide range of symbolic music datasets which encompass various Western classical music, popular music and traditional folk music show that the SMDIM model outperforms the other state-of-the-art approaches on both the generation quality and the computational efficiency, and it has robust generalization to underexplored musical styles. These results show that SMDIM offers a principled solution for long-sequence symbolic music generation, including associated attributes that accompany the sequences. We provide a project webpage with audio examples and supplementary materials at https://3328702107.github.io/smdim-music/.

Xiaohao Liu, Teng Tu, Yunshan Ma et al.

Music profoundly enhances video production by improving quality, engagement, and emotional resonance, sparking growing interest in video-to-music generation. Despite recent advances, existing approaches remain limited in specific scenarios or undervalue the visual dynamics. To address these limitations, we focus on tackling the complexity of dynamics and resolving temporal misalignment between video and music representations. To this end, we propose DyViM, a novel framework to enhance dynamics modeling for video-to-music generation. Specifically, we extract frame-wise dynamics features via a simplified motion encoder inherited from optical flow methods, followed by a self-attention module for aggregation within frames. These dynamic features are then incorporated to extend existing music tokens for temporal alignment. Additionally, high-level semantics are conveyed through a cross-attention mechanism, and an annealing tuning strategy benefits to fine-tune well-trained music decoders efficiently, therefore facilitating seamless adaptation. Extensive experiments demonstrate DyViM's superiority over state-of-the-art (SOTA) methods.

Zhanhong He, Roberto Togneri, Xiangyu Zhang

Recently, automatic music transcription has made it possible to convert musical audio into accurate MIDI. However, the resulting MIDI lacks music notations such as tempo, which hinders its conversion into sheet music. In this paper, we investigate state-of-the-art tempo estimation techniques and evaluate their performance on solo instrumental music. These include temporal convolutional network (TCN) and recurrent neural network (RNN) models that are pretrained on massive of mixed vocals and instrumental music, as well as TCN models trained specifically with solo instrumental performances. Through evaluations on drum, guitar, and classical piano datasets, our TCN models with the new training scheme achieved the best performance. Our newly trained TCN model increases the Acc1 metric by 38.6% for guitar tempo estimation, compared to the pretrained TCN model with an Acc1 of 61.1%. Although our trained TCN model is twice as accurate as the pretrained TCN model in estimating classical piano tempo, its Acc1 is only 50.9%. To improve the performance of deep learning models, we investigate their combinations with various post-processing methods. These post-processing techniques effectively enhance the performance of deep learning models when they struggle to estimate the tempo of specific instruments.

Sakhiseni Joseph Yende

Framed within the Sociomusicology Theory, this article argued that through the revival of Khoisan indigenous music, South Africa can promote social cohesion, bridging gaps between different ethnic groups and creating a more inclusive society. The Khoisan people, also known as Bushmen or San, are one of the oldest indigenous groups in Africa, and their music is an integral part of their identity and history. However, Khoisan indigenous music in South Africa has been forgotten and marginalised. This can be attributed to various factors including colonialism. In recent years, there has been a quest for revitalising the overlooked and marginalised Khoisan indigenous music in South Africa. Notwithstanding, minimal attention has been given to reviving the forgotten and marginalised Khoisan indigenous music in South Africa and this has become a matter of great concern. This article thus sought to understand the importance of reviving the forgotten and marginalised Khoisan indigenous music in South Africa. In this article, a qualitative content analysis was employed to successfully analyse the purpose of this paper. The findings demonstrated that the extinction of Khoisan indigenous music is attributed to various historical, social, and cultural factors, including colonialism, cultural assimilation, and modernisation. The paper concluded by affirming that the Khoisan indigenous music holds a deep cultural significance for the Khoisan people and South Africa as a whole. It serves as a potent vehicle for the expression and preservation of their great connection to nature, spiritual beliefs, and rich cultural history.

Gao Yan, Li Xiaobing

The scope of aesthetics is often bound by the limits of sensory perception. In appreciating music and painting, information is typically processed through distinct perceptual channels. However, at the cerebral cortex and nervous system level, these modes of perception can interact and integrate, influenced by collected audiovisual information, thereby enriching the aesthetic experience and improving aesthetic efficiency. The domain of audio-visual interactive aesthetics holds significant potential in aesthetic education, yet current research in this area has made limited substantial advancements. This paper explores the functionality and impact of an audio-visual interactive aesthetic model, combining music and painting, within practical aesthetic education through a series of applied experiments. By incorporating Chinese traditional music and painting into children’s regular curriculum, we conduct a quantitative evaluating and analysis of the benefits of audio-visual multi-sensory aesthetics over single-channel perception in enhancing children’s emotional, aesthetic experiences, and cognitive abilities.

Ying Li, Yuanyuan Wang, Lei Gao et al.

PurposeTo investigate the effects of nonpharmacological interventions (NPIs) on poststroke depression (PSD) in stroke patients.MethodsComputer searches were conducted on the PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang databases from their establishment to December 2023. The selection was made using the inclusion and exclusion criteria, and 40 articles were included to compare the effects of the 17 NPIs on patients with PSD.ResultsForty studies involving seventeen interventions were included. The network findings indicated that compared with conventional therapy (COT), superior PSD improvement was observed for cognitive behavioral therapy (CBT) + acupoint acupuncture (CBTA) (mean difference [MD], −4.25; 95% CI, −5.85 to −2.65), team positive psychotherapy (MD, −4.05; 95% CI, −5.53 to −2.58), music therapy (MT) + positive psychological intervention (MD, −2.25; 95% CI, −3.65 to −0.85), CBT (MD, −1.52; 95% CI, −2.05 to −0.99), mindfulness-based stress reduction (MD, −1.14; 95% CI, −2.14 to −0.14), MT (MD, −0.95; 95% CI, −1.39 to −0.52), acupoint acupuncture + MT (AAMT) (MD, −0.69; 95% CI, −1.25 to −0.14). Furthermore, CBT (MD, −3.87; 95% CI, −4.57 to −3.17), AAMT (MD, −1.02; 95% CI, −1.41 to −0.62), acupressure + MT (MD, −0.91; 95% CI, −1.27 to −0.54), and narrative care + acupressure (MD, −0.74; 95% CI, −1.19 to −0.29) demonstrated superior Pittsburgh Sleep Quality Index (PSQI) improvement compared with COT.ConclusionEvidence from systematic reviews and meta-analyses suggests that CBTA improves depression in patients with PSD. Moreover, CBT improves sleep in these patients. Additional randomized controlled trials are required to further investigate the efficacy and mechanisms of these interventions.

Marco Barbaresi, Davide Nardo, Sabrina Fagioli

Background: The human sensorimotor system can naturally synchronize with environmental rhythms, such as light pulses or sound beats. Several studies showed that different styles and tempos of music, or other rhythmic stimuli, have an impact on physiological rhythms, including electrocortical brain activity, heart rate, and motor coordination. Such synchronization, also known as the “entrainment effect”, has been identified as a crucial mechanism impacting cognitive, motor, and affective functioning. Objectives: This review examines theoretical and empirical contributions to the literature on entrainment, with a particular focus on the physiological mechanisms underlying this phenomenon and its role in cognitive, motor, and affective functions. We also address the inconsistent terminology used in the literature and evaluate the range of measurement approaches used to assess entrainment phenomena. Finally, we propose a definition of “physiological entrainment” that emphasizes its role as a fundamental mechanism that encompasses rhythmic interactions between the body and its environment, to support information processing across bodily systems and to sustain adaptive motor responses. Methods: We reviewed the recent literature through the lens of the “embodied cognition” framework, offering a unified perspective on the phenomenon of physiological entrainment. Results: Evidence from the current literature suggests that physiological entrainment produces measurable effects, especially on neural oscillations, heart rate variability, and motor synchronization. Eventually, such physiological changes can impact cognitive processing, affective functioning, and motor coordination. Conclusions: Physiological entrainment emerges as a fundamental mechanism underlying the mind–body connection. Entrainment-based interventions may be used to promote well-being by enhancing cognitive, motor, and affective functions, suggesting potential rehabilitative approaches to enhancing mental health.

Florian Grötschla, Luca Strässle, Luca A. Lanzendörfer et al.

Music recommender systems frequently utilize network-based models to capture relationships between music pieces, artists, and users. Although these relationships provide valuable insights for predictions, new music pieces or artists often face the cold-start problem due to insufficient initial information. To address this, one can extract content-based information directly from the music to enhance collaborative-filtering-based methods. While previous approaches have relied on hand-crafted audio features for this purpose, we explore the use of contrastively pretrained neural audio embedding models, which offer a richer and more nuanced representation of music. Our experiments demonstrate that neural embeddings, particularly those generated with the Contrastive Language-Audio Pretraining (CLAP) model, present a promising approach to enhancing music recommendation tasks within graph-based frameworks.

Luca Comanducci, Paolo Bestagini, Stefano Tubaro

Text-To-Music (TTM) models have recently revolutionized the automatic music generation research field. Specifically, by reaching superior performances to all previous state-of-the-art models and by lowering the technical proficiency needed to use them. Due to these reasons, they have readily started to be adopted for commercial uses and music production practices. This widespread diffusion of TTMs poses several concerns regarding copyright violation and rightful attribution, posing the need of serious consideration of them by the audio forensics community. In this paper, we tackle the problem of detection and attribution of TTM-generated data. We propose a dataset, FakeMusicCaps that contains several versions of the music-caption pairs dataset MusicCaps re-generated via several state-of-the-art TTM techniques. We evaluate the proposed dataset by performing initial experiments regarding the detection and attribution of TTM-generated audio.

Manuel Martínez Burgos

Este artículo explora la noción de prosodia y su relación con la composición musical. El estudio lingüístico de la prosodia se ocupa de la energía, los ritmos y las entonaciones de los patrones del habla y cómo estos impactan en el significado de las expresiones. Está claro que hay correspondencias considerables entre los elementos prosódicos del lenguaje –sus ritmos, acentos y entonaciones– y la música; la prosodia es, con mucho, el elemento del lenguaje más cercano al sonido musical –en oposición al significado semántico o incluso pragmático–. El estudio de la prosodia revela muchas de las características del estado emocional o expresivo de un hablante, del mismo modo que inferimos el contenido emocional de la música a partir de la interpretación de una obra. ¿Qué hay de la relación entre prosodia y composición? El vínculo entre la prosodia y la composición musical ha sido muy significativo en momentos particulares de la historia, pero faltan estudios completos sobre esta conexión. Este artículo tiene como objetivo abordar esta falta de investigación tomando como punto de partida el análisis de mi propia obra. Con estas ideas en mente ofrezco una visión general de las conexiones entre la prosodia y la composición musical, y examino algunos trabajos psicolingüísticos actuales sobre el tema. A continuación reflexiono sobre mi enfoque compositivo utilizando la prosodia como fuente de inspiración en dos obras propias: I Have a Dream para oboe solo y Romancero gitano para pianista-recitador. Cierro el artículo con unas reflexiones finales. Palabras clave: análisis, composición, Federico García Lorca, Manuel Martínez Burgos, I Have a Dream, Romancero gitano Prosody as a source of inspiration in composition, analysis of two 0wn pieces: I Have a Dream and Romancero gitano Abstract This article explores the notion of prosody and its relationship to musical composition. The linguistic study of prosody is concerned with the energy, rhythms and intonations of speech patterns, and how these impact on the meaning of utterances. It is clear that there are considerable correspondences between the prosodic elements of language—its rhythms, stresses, and intonations—and music; it is by far the closest element of language to musical sound—as opposed to semantic or even pragmatic meaning. The study of prosody reveals many of the features of any speaker's emotional or expressive state, in the same way that we infer emotional content in music from the performance of a work. But what of the relationship between prosody and composition? The link between prosody and musical composition has been very significant at particular moments in history yet there is a lack of any thorough scholarship on this connection. This article aims to address this research gap taking two of my pieces as a starting point. With these ideas in mind, I provide an overview of the connections between prosody and music composition, and examine some current psycho-linguistic work on the subject. I then reflect on my compositional approach using prosody as a source of inspiration in two of my own works: I Have a Dream for solo oboe and Romancero gitano for pianist-reciter. I close the article with some final reflections. Keywords: analysis, composition, Federico García Lorca, Manuel Martínez Burgos, I Have a Dream, Romancero gitano

Humberto Amorim, Ricardo Dias, Paulo Martelli

O objetivo do artigo é apontar não somente o quão a produção de Sérgio Abreu como arranjador/transcritor pode revelar contribuições decisivas para o alargamento do repertório do violão, a partir do levantamento, catalogação, organização e edição do material adaptado (conhecido e desconhecido) encontrado em seu espólio, mas também esboçar o entendimento de alguns dos eventuais procedimentos e processos que orientaram o pensamento de Sérgio na confecção de tais trabalhos (o conceito de montagem e a agregação de compreensão e valor artístico às obras adaptadas, por exemplo). Para tanto, , realizamos uma breve discussão sobre a terminologia “transcrição/arranjo”, discutimos as perspectivas históricas e as funções socioculturais das transcrições e arranjos para violão (com foco no caso de Sérgio Abreu), até elencarmos os critérios estabelecidos para as edições diplomáticas de dois arranjos inéditos recentemente descobertos, que passaram por revisão geral de Eduardo Abreu: Pachelbel e Gluck.

Siti Nazla Raihana, Muhsan Syarafuddin

The development of fandoms is very rapid; all artists have their fandoms. Almost everyone joins a fandom for leisure, and it is very accessible. These fandom activities certainly impact the fan's life and relationship with the family. This study explores fandom culture's impact on family harmony and Islamic views of fandom. Fandom is an activity that describes the closeness between fans and their idols based on cultural products. This study used a qualitative case study approach with a purposive sampling of thirty informants. The result shows that fandom has five negative impacts on family harmony, namely: (1) neglecting family members from their obligations; (2) causing addiction, jealousy, and wastefulness; (3) children disobeying parental orders; (4) parents having difficulty taking care of children; and (5) poor communication between family members. Furthermore, fandom activities contradict Islamic teachings for five reasons, namely: (1) music and singing; (2) lousy consumerism; (3) wasting time; (4) the wrong community; and (5) bad lifestyle. Family members should advise each other so that no member joins the fandom, thereby endangering the harmony of the family.   Perkembangan fandom sangat pesat, semua artis memiliki fandom masing-masing. Hampir semua orang bergabung dengan fandom untuk mengisi waktu luang, dan itu sangat aksesibel. Kegiatan fandom ini tentunya punya dampak untuk kehidupan sang penggemar dan hubungannya dengan keluarga. Penelitian ini bertujuan mengeksplorasi dampak budaya fandom terhadap keharmonisan keluarga dan pandangan Islam terhadap fandom. Fandom merupakan aktivitas yang menggambarkan kedekatan antara penggemar dan idolanya yang didasari oleh produk budaya. Penelitian ini menggunakan pendekatan kualitatif bersifat studi kasus dengan pengambilan sampel secara purposive sampling dari tiga puluh informan. Penelitian ini menunjukkan hasil bahwa fandom memiliki lima dampak negatif terhadap keharmonisan keluarga yaitu: (1) melalaikan anggota keluarga dari kewajibannya; (2) menimbulkan kecanduan, iri hati, dan keborosan;(3) anak tidak mematuhi perintah orang tua; (4) orang tua kesulitan untuk mengurus anak-anak; (5) komunikasi yang tidak baik antar anggota keluarga. Selanjutnya, kegiatan fandom menyelisihi ajaran agama Islam dengan lima alasan yaitu: (1) musik dan nyanyian; (2) konsumerisme yang buruk; (3) membuang waktu; (4) komunitas yang buruk; (5) gaya hidup yang tidak baik. Hendaknya anggota keluarga saling menasehati satu sama lain agar tidak ada anggota yang bergabung dengan fandom, sehingga membahayakan keharmonisan keluarga tersebut.

Nhat Le, Thang Pham, Tuong Do et al.

Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group remains an open problem. In this paper, we present $\rm AIOZ-GDANCE$, a new large-scale dataset for music-driven group dance generation. Unlike existing datasets that only support single dance, our new dataset contains group dance videos, hence supporting the study of group choreography. We propose a semi-autonomous labeling method with humans in the loop to obtain the 3D ground truth for our dataset. The proposed dataset consists of 16.7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres. We show that naively applying single dance generation technique to creating group dance motion may lead to unsatisfactory results, such as inconsistent movements and collisions between dancers. Based on our new dataset, we propose a new method that takes an input music sequence and a set of 3D positions of dancers to efficiently produce multiple group-coherent choreographies. We propose new evaluation metrics for measuring group dance quality and perform intensive experiments to demonstrate the effectiveness of our method. Our project facilitates future research on group dance generation and is available at: https://aioz-ai.github.io/AIOZ-GDANCE/

Xin Jin, Wu Zhou, Jinyu Wang et al.

Computational aesthetics evaluation has made great achievements in the field of visual arts, but the research work on music still needs to be explored. Although the existing work of music generation is very substantial, the quality of music score generated by AI is relatively poor compared with that created by human composers. The music scores created by AI are usually monotonous and devoid of emotion. Based on Birkhoff's aesthetic measure, this paper proposes an objective quantitative evaluation method for homophony music score aesthetic quality assessment. The main contributions of our work are as follows: first, we put forward a homophony music score aesthetic model to objectively evaluate the quality of music score as a baseline model; second, we put forward eight basic music features and four music aesthetic features.

Shruti Dutta, Shashwat Mookherjee

This paper conducts an intricate analysis of musical emotions and trends using Spotify music data, encompassing audio features and valence scores extracted through the Spotipi API. Employing regression modeling, temporal analysis, mood transitions, and genre investigation, the study uncovers patterns within music-emotion relationships. Regression models linear, support vector, random forest, and ridge, are employed to predict valence scores. Temporal analysis reveals shifts in valence distribution over time, while mood transition exploration illuminates emotional dynamics within playlists. The research contributes to nuanced insights into music's emotional fabric, enhancing comprehension of the interplay between music and emotions through years.