Regular physical activity has multifarious benefits for physical and mental health, and music has been found to exert positive effects on physical activity. Summative literature reviews and conceptual models have hypothesized potential benefits and salient mechanisms associated with music listening in exercise and sport contexts, although no large-scale objective summary of the literature has been conducted. A multilevel meta-analysis of 139 studies was used to quantify the effects of music listening in exercise and sport domains. In total, 598 effect sizes from four categories of potential benefits (i.e., psychological responses, physiological responses, psychophysical responses, and performance outcomes) were calculated based on 3,599 participants. Music was associated with significant beneficial effects on affective valence (g = 0.48, CI [0.39, 0.56]), physical performance (g = 0.31, CI [0.25, 0.36]), perceived exertion (g = 0.22, CI [0.14, 0.30]), and oxygen consumption (g = 0.15, CI [0.02, 0.27]). No significant benefit of music was found for heart rate (g = 0.07, CI [-0.03, 0.16]). Performance effects were moderated by study domain (exercise > sport) and music tempo (fast > slow-to-medium). Overall, results supported the use of music listening across a range of physical activities to promote more positive affective valence, enhance physical performance (i.e., ergogenic effect), reduce perceived exertion, and improve physiological efficiency. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
J. Bradt, C. DiLeo, Katherine Myers-Coffman
et al.
BACKGROUND This is an update of the review published on the Cochrane Library in 2016, Issue 8. Having cancer may result in extensive emotional, physical and social suffering. Music interventions have been used to alleviate symptoms and treatment side effects in people with cancer. This review includes music interventions defined as music therapy offered by trained music therapists, as well as music medicine, which was defined as listening to pre-recorded music offered by medical staff. OBJECTIVES To assess and compare the effects of music therapy and music medicine interventions for psychological and physical outcomes in people with cancer. SEARCH METHODS We searched the Cochrane Central Register of Controlled Trials (CENTRAL; 2020, Issue 3) in the Cochrane Library, MEDLINE via Ovid, Embase via Ovid, CINAHL, PsycINFO, LILACS, Science Citation Index, CancerLit, CAIRSS, Proquest Digital Dissertations, ClinicalTrials.gov, Current Controlled Trials, the RILM Abstracts of Music Literature, http://www.wfmt.info/Musictherapyworld/ and the National Research Register. We searched all databases, except for the last two, from their inception to April 2020; the other two are no longer functional, so we searched them until their termination date. We handsearched music therapy journals, reviewed reference lists and contacted experts. There was no language restriction. SELECTION CRITERIA We included all randomized and quasi-randomized controlled trials of music interventions for improving psychological and physical outcomes in adults and pediatric patients with cancer. We excluded patients undergoing biopsy and aspiration for diagnostic purposes. DATA COLLECTION AND ANALYSIS Two review authors independently extracted the data and assessed the risk of bias. Where possible, we presented results in meta-analyses using mean differences and standardized mean differences. We used post-test scores. In cases of significant baseline difference, we used change scores. We conducted separate meta-analyses for studies with adult participants and those with pediatric participants. Primary outcomes of interest included psychological outcomes and physical symptoms and secondary outcomes included physiological responses, physical functioning, anesthetic and analgesic intake, length of hospitalization, social and spiritual support, communication, and quality of life (QoL) . We used GRADE to assess the certainty of the evidence. MAIN RESULTS We identified 29 new trials for inclusion in this update. In total, the evidence of this review rests on 81 trials with a total of 5576 participants. Of the 81 trials, 74 trials included adult (N = 5306) and seven trials included pediatric (N = 270) oncology patients. We categorized 38 trials as music therapy trials and 43 as music medicine trials. The interventions were compared to standard care. Psychological outcomes The results suggest that music interventions may have a large anxiety-reducing effect in adults with cancer, with a reported average anxiety reduction of 7.73 units (17 studies, 1381 participants; 95% confidence interval (CI) -10.02 to -5.44; very low-certainty evidence) on the Spielberger State Anxiety Inventory scale (range 20 to 80; lower values reflect lower anxiety). Results also suggested a moderately strong, positive impact of music interventions on depression in adults (12 studies, 1021 participants; standardized mean difference (SMD): -0.41, 95% CI -0.67 to -0.15; very low-certainty evidence). We found no support for an effect of music interventions on mood (SMD 0.47, 95% CI -0.02 to 0.97; 5 studies, 236 participants; very low-certainty evidence). Music interventions may increase hope in adults with cancer, with a reported average increase of 3.19 units (95% CI 0.12 to 6.25) on the Herth Hope Index (range 12 to 48; higher scores reflect greater hope), but this finding was based on only two studies (N = 53 participants; very low-certainty evidence). Physical outcomes We found a moderate pain-reducing effect of music interventions (SMD -0.67, 95% CI -1.07 to -0.26; 12 studies, 632 adult participants; very low-certainty evidence). In addition, music interventions had a small treatment effect on fatigue (SMD -0.28, 95% CI -0.46 to -0.10; 10 studies, 498 adult participants; low-certainty evidence). The results suggest a large effect of music interventions on adult participants' QoL, but the results were highly inconsistent across studies, and the pooled effect size was accompanied by a large confidence interval (SMD 0.88, 95% CI -0.31 to 2.08; 7 studies, 573 participants; evidence is very uncertain). Removal of studies that used improper randomization methods resulted in a moderate effect size that was less heterogeneous (SMD 0.47, 95% CI 0.06 to 0.88, P = 0.02, I2 = 56%). A small number of trials included pediatric oncology participants. The findings suggest that music interventions may reduce anxiety but this finding was based on only two studies (SMD -0.94, 95% CI -1.9 to 0.03; very low-certainty evidence). Due to the small number of studies, we could not draw conclusions regarding the effects of music interventions on mood, depression, QoL, fatigue or pain in pediatric participants with cancer. The majority of studies included in this review update presented a high risk of bias, and therefore the overall certainty of the evidence is low. For several outcomes (i.e. anxiety, depression, pain, fatigue, and QoL) the beneficial treatment effects were consistent across studies for music therapy interventions delivered by music therapists. In contrast, music medicine interventions resulted in inconsistent treatment effects across studies for these outcomes. AUTHORS' CONCLUSIONS This systematic review indicates that music interventions compared to standard care may have beneficial effects on anxiety, depression, hope, pain, and fatigue in adults with cancer. The results of two trials suggest that music interventions may have a beneficial effect on anxiety in children with cancer. Too few trials with pediatric participants were included to draw conclusions about the treatment benefits of music for other outcomes. For several outcomes, music therapy interventions delivered by a trained music therapist led to consistent results across studies and this was not the case for music medicine interventions. Moreover, evidence of effect was found for music therapy interventions for QoL and fatigue but not for music medicine interventions. Most trials were at high risk of bias and low or very low certainty of evidence; therefore, these results need to be interpreted with caution.
While there are many music datasets with emotion labels in the literature, they cannot be used for research on symbolic-domain music analysis or generation, as there are usually audio files only. In this paper, we present the EMOPIA (pronounced `yee-mo-pi-uh') dataset, a shared multi-modal (audio and MIDI) database focusing on perceived emotion in pop piano music, to facilitate research on various tasks related to music emotion. The dataset contains 1,087 music clips from 387 songs and clip-level emotion labels annotated by four dedicated annotators. Since the clips are not restricted to one clip per song, they can also be used for song-level analysis. We present the methodology for building the dataset, covering the song list curation, clip selection, and emotion annotation processes. Moreover, we prototype use cases on clip-level music emotion classification and emotion-based symbolic music generation by training and evaluating corresponding models using the dataset. The result demonstrates the potential of EMOPIA for being used in future exploration on piano emotion-related MIR tasks.
Octavia Leahy, Emily Kontaris, Natalie Gunasekara
et al.
IntroductionEnvironmental factors play a critical role in shaping social interactions, and emerging evidence suggests they may also influence inter-brain coupling (IBC). The main purpose of this paper is to systematically review how environmental variables influence IBC during hyperscanning studies of social interactions. Additionally, this article provides an overview of the experimental protocols employed and identifies both opportunities and challenges within this evolving field.MethodsFollowing PRISMA guidelines, we conducted a systematic literature search in the PubMed and Scopus databases to identify relevant articles. Of the 106 articles initially identified, 7 met the inclusion criteria for this review. The selected articles are original research published up to February 2025, each employing hyperscanning techniques to observe IBC in response to manipulated environmental factors. Articles were excluded based on factors such as the absence of environmental manipulation or not measuring IBC as an outcome.ResultsThe findings reveal that IBC is significantly influenced by environmental factors such as interpersonal distance, background noise, virtual reality, and music. These factors modulate neural synchrony in brain regions critical for social cognition.ConclusionThe limited number of studies in this area reflects both the emerging nature of this research field and the challenges associated with experimental protocols and funding. Despite these limitations, this review underscores the crucial role of environmental factors in shaping IBC during social interactions. This growing field holds great potential for guiding the design of supportive social settings and targeted interventions that promote social cohesion and mental wellbeing.
W artykule przedstawiam problematykę wykonania muzycznego w badaniach muzykologicznych. W pierwszej części artykułu analizuję zagadnienie wykonania muzycznego z perspektywy muzykologii jako prymarnej nauki o muzyce i jej źródłach. W drugiej części artykułu przeprowadzam filozoficzną analizę źródła. W trzeciej części artykułu analizuję główne aspekty wykonania muzycznego, znajdujące się w horyzoncie zainteresowania muzykologii filozoficznej. Celem artykułu jest zwrócenie uwagi na potrzebę interdyscyplinarnego dialogu muzykologiczno-filozoficznego wokół problematyki wykonania muzycznego.
Rifdah Fadhillah, Dewi Meyrasyawati, Johny Alfian Khusyairi
This study analyzes music's role in shaping visitors' hyperreal experiences in Kampung Jelita, Surabaya. Using a qualitative case study approach, the research focuses on the Japanese and Balinese thematic zones and applies Jean Baudrillard's theory of hyperreality. Data were collected through in-depth interviews, direct observation, literature review, and documentation conducted in the Kampung Jelita area, Manukan Lor IV E Street, RT 05/RW 01, Banjar Sugihan Subdistrict, Tandes District, Surabaya. The findings reveal that instrumental music is a cultural simulator that evokes illusion and fantasy, supporting Baudrillard's view that simulation can substitute reality. The soundscapes in each thematic zone, Japanese and Balinese, enhance the immersive experience by harmonizing with visual ornaments, prompting visitors to engage emotionally, experience nostalgia, and participate in performative acts such as renting traditional costumes. However, some visitors noted inconsistencies between the music and the intended cultural themes. Theoretically, this study contributes to hyperreality discourse by emphasizing the role of music in reinforcing sensory simulation within thematic tourism village contexts. Practically, the findings suggest that curating culturally coherent soundscapes can strengthen visitors' emotional attachment and enhance destination branding. Thus, music should be regarded as a strategic medium in constructing cultural identity and tourist experience.
Hiperrealitas Musik di Kampung Jelita: Studi Kasus Tematik Wisata di Surabaya
Abstrak
Penelitian ini menganalisis peran musik dalam membentuk pengalaman hiperreal pengunjung di Kampung Jelita, Surabaya. Dengan menggunakan pendekatan studi kasus kualitatif, penelitian ini berfokus pada zona tematik Jepang dan Bali serta menerapkan teori hiperrealitas Jean Baudrillard. Data dikumpulkan melalui wawancara mendalam, observasi langsung, studi pustaka, dan dokumentasi yang dilakukan di kawasan Kampung Jelita, Jalan Manukan Lor IV E, RT 05/RW 01, Kelurahan Banjar Sugihan, Kecamatan Tandes, Surabaya. Hasil penelitian menunjukkan bahwa musik instrumental berfungsi sebagai simulator budaya yang membangkitkan ilusi dan fantasi, mendukung pandangan Baudrillard bahwa simulasi dapat menggantikan realitas. Lanskap bunyi (soundscape) di setiap zona tematik Jepang dan Bali memperkuat pengalaman imersif dengan menciptakan keselarasan antara elemen audio dan visual, sehingga mendorong pengunjung untuk terlibat secara emosional, merasakan nostalgia, serta berpartisipasi dalam tindakan performatif seperti menyewa kostum tradisional. Namun, beberapa pengunjung mencatat adanya ketidaksesuaian antara musik yang diputar dengan tema budaya yang dimaksudkan. Secara teoretis, penelitian ini memberikan kontribusi terhadap wacana hiperrealitas dengan menekankan peran musik dalam memperkuat simulasi sensorik dalam konteks kampung tematik wisata. Secara praktis, temuan ini menyarankan bahwa pengelolaan lanskap bunyi yang selaras secara budaya dapat memperkuat keterikatan emosional pengunjung dan meningkatkan citra destinasi wisata. Dengan demikian, musik perlu dipandang sebagai media strategis dalam membangun identitas budaya dan pengalaman wisata.
Kata kunci: hiperrealitas musik; Kampung Jelita; musik instrumental tradisional; simulakra
Julia Barnett, Patrick O'Reilly, Jason Brent Smith
et al.
While research in AI methods for music generation and analysis has grown in scope and impact, AI researchers' engagement with the ethical consequences of this work has not kept pace. To encourage such engagement, many publication venues have introduced optional or required ethics statements for AI research papers. Though some authors use these ethics statements to critically engage with the broader implications of their research, we find that the majority of ethics statements in the AI music literature do not appear to be effectively utilized for this purpose. In this work, we conduct a review of ethics statements across ISMIR, NIME, and selected prominent works in AI music from the past five years. We then offer suggestions for both audio conferences and researchers for engaging with ethics statements in ways that foster meaningful reflection rather than formulaic compliance.
Baptiste Hilaire, Emmanouil Karystinaios, Gerhard Widmer
Interpretability is essential for deploying deep learning models in symbolic music analysis, yet most research emphasizes model performance over explanation. To address this, we introduce MUSE-Explainer, a new method that helps reveal how music Graph Neural Network models make decisions by providing clear, human-friendly explanations. Our approach generates counterfactual explanations by making small, meaningful changes to musical score graphs that alter a model's prediction while ensuring the results remain musically coherent. Unlike existing methods, MUSE-Explainer tailors its explanations to the structure of musical data and avoids unrealistic or confusing outputs. We evaluate our method on a music analysis task and show it offers intuitive insights that can be visualized with standard music tools such as Verovio.
Solo piano music, despite being a single-instrument medium, possesses significant expressive capabilities, conveying rich semantic information across genres, moods, and styles. However, current general-purpose music representation models, predominantly trained on large-scale datasets, often struggle to captures subtle semantic distinctions within homogeneous solo piano music. Furthermore, existing piano-specific representation models are typically unimodal, failing to capture the inherently multimodal nature of piano music, expressed through audio, symbolic, and textual modalities. To address these limitations, we propose PianoBind, a piano-specific multimodal joint embedding model. We systematically investigate strategies for multi-source training and modality utilization within a joint embedding framework optimized for capturing fine-grained semantic distinctions in (1) small-scale and (2) homogeneous piano datasets. Our experimental results demonstrate that PianoBind learns multimodal representations that effectively capture subtle nuances of piano music, achieving superior text-to-music retrieval performance on in-domain and out-of-domain piano datasets compared to general-purpose music joint embedding models. Moreover, our design choices offer reusable insights for multimodal representation learning with homogeneous datasets beyond piano music.
Symbolic music analysis tasks are often performed by models originally developed for Natural Language Processing, such as Transformers. Such models require the input data to be represented as sequences, which is achieved through a process of tokenization. Tokenization strategies for symbolic music often rely on absolute MIDI values to represent pitch information. However, music research largely promotes the benefit of higher-level representations such as melodic contour and harmonic relations for which pitch intervals turn out to be more expressive than absolute pitches. In this work, we introduce a general framework for building interval-based tokenizations. By evaluating these tokenizations on three music analysis tasks, we show that such interval-based tokenizations improve model performances and facilitate their explainability.
Despite progress in controllable symbolic music generation, data scarcity remains a challenge for certain control modalities. Composer-style music generation is a prime example, as only a few pieces per composer are available, limiting the modeling of both styles and fundamental music elements (e.g., melody, chord, rhythm). In this paper, we investigate how general music knowledge learned from a broad corpus can enhance the mastery of specific composer styles, with a focus on piano piece generation. Our approach follows a two-stage training paradigm. First, we pre-train a REMI-based music generation model on a large corpus of pop, folk, and classical music. Then, we fine-tune it on a small, human-verified dataset from four renowned composers, namely Bach, Mozart, Beethoven, and Chopin, using a lightweight adapter module to condition the model on style indicators. To evaluate the effectiveness of our approach, we conduct both objective and subjective evaluations on style accuracy and musicality. Experimental results demonstrate that our method outperforms ablations and baselines, achieving more precise composer-style modeling and better musical aesthetics. Additionally, we provide observations on how the model builds music concepts from the generality pre-training and refines its stylistic understanding through the mastery fine-tuning.
The design of meaningful audio features is a key need to advance the state-of-the-art in music emotion recognition (MER). This article presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology literature on the relations between eight musical dimensions (melody, harmony, rhythm, dynamics, tone color, expressivity, texture and form) and specific emotions. Based on this review, current gaps and needs are identified and strategies for future research on feature engineering for MER are proposed, namely ideas for computational audio features that capture elements of musical form, texture and expressivity that should be further researched. Previous MER surveys offered broad reviews, covering topics such as emotion paradigms, approaches for the collection of ground-truth data, types of MER problems and overviewing different MER systems. On the contrary, our approach is to offer a deep and specific review on one key MER problem: the design of emotionally-relevant audio features.
Rostyslav Hnatyshyn, Jiayi Hong, Ross Maciejewski
et al.
The development of cancer is difficult to express on a simple and intuitive level due to its complexity. Since cancer is so widespread, raising public awareness about its mechanisms can help those affected cope with its realities, as well as inspire others to make lifestyle adjustments and screen for the disease. Unfortunately, studies have shown that cancer literature is too technical for the general public to understand. We found that musification, the process of turning data into music, remains an unexplored avenue for conveying this information. We explore the pedagogical effectiveness of musification through the use of an algorithm that manipulates a piece of music in a manner analogous to the development of cancer. We conducted two lab studies and found that our approach is marginally more effective at promoting cancer literacy when accompanied by a text-based article than text-based articles alone.
Kevin C. Paul, Junaid Basha, Indrajit Karmakar
et al.
The development of an automated music transcription system represents a pivotal advancement in the domain of music technology, catering to diverse needs across musical education, production, research, and accessibility. This paper underscores the significance of such a system in preserving musical heritage, facilitating learning, fostering collaboration, and enhancing efficiency in transcribing music. Through an extensive review of existing literature, the paper examines various approaches and limitations in automated music transcription. Leveraging insights from prior work, the paper proposes a comprehensive model aimed at accurately converting audio recordings into sheet music or symbolic representations, while also incorporating functionalities such as vocal isolation and lyrics generation.
The music domain is among the most important ones for adopting recommender systems technology. In contrast to most other recommendation domains, which predominantly rely on collaborative filtering (CF) techniques, music recommenders have traditionally embraced content-based (CB) approaches. In the past years, music recommendation models that leverage collaborative and content data -- which we refer to as content-driven models -- have been replacing pure CF or CB models. In this survey, we review 55 articles on content-driven music recommendation. Based on a thorough literature analysis, we first propose an onion model comprising five layers, each of which corresponds to a category of music content we identified: signal, embedded metadata, expert-generated content, user-generated content, and derivative content. We provide a detailed characterization of each category along several dimensions. Second, we identify six overarching challenges, according to which we organize our main discussion: increasing recommendation diversity and novelty, providing transparency and explanations, accomplishing context-awareness, recommending sequences of music, improving scalability and efficiency, and alleviating cold start. Each article addresses one or more of these challenges is categorized according to the content layers of our onion model, the article's goal(s), and main methodological choices. Furthermore, articles are discussed in temporal order to shed light on the evolution of content-driven music recommendation strategies. Finally, we provide our personal selection of the persisting grand challenges which are still waiting to be solved in future research endeavors.