Ines Schindler, G. Hosoya, Winfried Menninghaus
et al.
Aesthetic perception and judgement are not merely cognitive processes, but also involve feelings. Therefore, the empirical study of these experiences requires conceptualization and measurement of aesthetic emotions. Despite the long-standing interest in such emotions, we still lack an assessment tool to capture the broad range of emotions that occur in response to the perceived aesthetic appeal of stimuli. Elicitors of aesthetic emotions are not limited to the arts in the strict sense, but extend to design, built environments, and nature. In this article, we describe the development of a questionnaire that is applicable across many of these domains: the Aesthetic Emotions Scale (Aesthemos). Drawing on theoretical accounts of aesthetic emotions and an extensive review of extant measures of aesthetic emotions within specific domains such as music, literature, film, painting, advertisements, design, and architecture, we propose a framework for studying aesthetic emotions. The Aesthemos, which is based on this framework, contains 21 subscales with two items each, that are designed to assess the emotional signature of responses to stimuli’s perceived aesthetic appeal in a highly differentiated manner. These scales cover prototypical aesthetic emotions (e.g., the feeling of beauty, being moved, fascination, and awe), epistemic emotions (e.g., interest and insight), and emotions indicative of amusement (humor and joy). In addition, the Aesthemos subscales capture both the activating (energy and vitality) and the calming (relaxation) effects of aesthetic experiences, as well as negative emotions that may contribute to aesthetic displeasure (e.g., the feeling of ugliness, boredom, and confusion).
The COVID-19 pandemic has created an unprecedented worldwide public health concern. Characterized by rapid and high frequency human-to-human transmission, the World Health Organization has recommended implementation of public health measures, including isolation of all suspected infectious individuals for a 14-day quarantine period, while governments have introduced “social distancing” and “lock-downs” of varying severity to curtail COVID-19 spread. Recent COVID-19 research further suggests there are major sleep problems and psychological disorders (e.g., stress, anxiety, depression) associated with the reduction of movement and activities, as well as the reduced social interaction. There have been no studies examining the effect of physical activity at home during such periods of isolation. However, based on previous research, potential tactics to overcome these negative effects include home-based exercise, exergaming, dancing to music, and participation in yoga. Adults should accumulate at least 150 min of moderate-intensity and at least 75 min of vigorous-intensity of activity divided in to 5–7 sessions per week. This training volume could be reduced by 30% for children and adolescents if replaced by recess or active play in and around the home. Additionally, exercises should be adapted to the fitness level of the participant and a progressive model of intensity and training volume should be utilized, preferably monitored by telephone applications and wearable sensors.
Celia Moreno-Morales, Raul Calero, Pedro Moreno-Morales
et al.
Background: Dementia is a neurological condition characterized by deterioration in cognitive, behavioral, social, and emotional functions. Pharmacological interventions are available but have limited effect in treating many of the disease's features. Several studies have proposed therapy with music as a possible strategy to slow down cognitive decline and behavioral changes associated with aging in combination with the pharmacological therapy. Objective: We performed a systematic review and subsequent meta-analysis to check whether the application of music therapy in people living with dementia has an effect on cognitive function, quality of life, and/or depressive state. Methods: The databases used were Medline, PubMed Central, Embase, PsycINFO, and the Cochrane Library. The search was made up of all the literature until present. For the search, key terms, such as “music,” “brain,” “dementia,” or “clinical trial,” were used. Results: Finally, a total of eight studies were included. All the studies have an acceptable quality based on the score on the Physiotherapy Evidence Database (PEDro) and Critical Appraisal Skills Program (CASP) scales. After meta-analysis, it was shown that the intervention with music improves cognitive function in people living with dementia, as well as quality of life after the intervention and long-term depression. Nevertheless, no evidence was shown of improvement of quality of life in long-term and short-term depression. Conclusion: Based on our results, music could be a powerful treatment strategy. However, it is necessary to develop clinical trials aimed to design standardized protocols depending on the nature or stage of dementia so that they can be applied together with current cognitive-behavioral and pharmacological therapies.
The discourse concerning musical expression hinges on a fundamental analogy between music and language. While the extant literature commonly compares music to language, this essay takes the reverse direction, following Wittgenstein’s approach. The discussion contrasts the theoretical underpinnings of the “music as language” simile with those of the “language as music” simile. The emphasis on characterization, performance, mutual tuning-in relationships, the interaction between language and music, and the open-ended effort to reorient ourselves as we draw in significance challenges the “informing paradigm” that has been paramount across contemporary philosophical theories of musical expression. One of the most promising philosophical avenues that emerges as we move beyond the “informing paradigm” is recognizing the inseparable relationship between musical expression and our power to shape our own language.
Keon Ju Maverick Lee, Jeff Ens, Sara Adkins
et al.
The Musical Instrument Digital Interface (MIDI), introduced in 1983, revolutionized music production by allowing computers and instruments to communicate efficiently. MIDI files encode musical instructions compactly, facilitating convenient music sharing. They benefit Music Information Retrieval (MIR), aiding in research on music understanding, computational musicology, and generative music. The GigaMIDI dataset contains over 1.4 million unique MIDI files, encompassing 1.8 billion MIDI note events and over 5.3 million MIDI tracks. GigaMIDI is currently the largest collection of symbolic music in MIDI format available for research purposes under fair dealing. Distinguishing between non-expressive and expressive MIDI tracks is challenging, as MIDI files do not inherently make this distinction. To address this issue, we introduce a set of innovative heuristics for detecting expressive music performance. These include the Distinctive Note Velocity Ratio (DNVR) heuristic, which analyzes MIDI note velocity; the Distinctive Note Onset Deviation Ratio (DNODR) heuristic, which examines deviations in note onset times; and the Note Onset Median Metric Level (NOMML) heuristic, which evaluates onset positions relative to metric levels. Our evaluation demonstrates these heuristics effectively differentiate between non-expressive and expressive MIDI tracks. Furthermore, after evaluation, we create the most substantial expressive MIDI dataset, employing our heuristic, NOMML. This curated iteration of GigaMIDI encompasses expressively-performed instrument tracks detected by NOMML, containing all General MIDI instruments, constituting 31% of the GigaMIDI dataset, totalling 1,655,649 tracks.
Aditya Bhattacharjee, Ivan Meresman Higgs, Mark Sandler
et al.
Automatic sample identification (ASID), the detection and identification of portions of audio recordings that have been reused in new musical works, is an essential but challenging task in the field of audio query-based retrieval. While a related task, audio fingerprinting, has made significant progress in accurately retrieving musical content under "real world" (noisy, reverberant) conditions, ASID systems struggle to identify samples that have undergone musical modifications. Thus, a system robust to common music production transformations such as time-stretching, pitch-shifting, effects processing, and underlying or overlaying music is an important open challenge. In this work, we propose a lightweight and scalable encoding architecture employing a Graph Neural Network within a contrastive learning framework. Our model uses only 9% of the trainable parameters compared to the current state-of-the-art system while achieving comparable performance, reaching a mean average precision (mAP) of 44.2%. To enhance retrieval quality, we introduce a two-stage approach consisting of an initial coarse similarity search for candidate selection, followed by a cross-attention classifier that rejects irrelevant matches and refines the ranking of retrieved candidates - an essential capability absent in prior models. In addition, because queries in real-world applications are often short in duration, we benchmark our system for short queries using new fine-grained annotations for the Sample100 dataset, which we publish as part of this work.
We describe our investigations concerning the sonification of measured data from experiments involving various mesoscopic mechanical oscillator systems cooled down close to their quantum ground states, and music generation from a programmed quantum computer that subjects a single quantum bit ("qubit") to various unitary rotations, composed in order to test for the breakdown of macroscopic realism as expressed by the violation of the Leggett-Garg inequality. "Listening'' to data via their resulting sonifications facilitates the discovery of signals that might otherwise be hard to detect in common graphic (i.e., visual) representations, and for the quantum computer music experiment provides a complementary way to discern when the measured qubit data violates macroscopic realism with some prior listening training. The resulting soundscapes and music also provide a complementary window into the quantum realm that is accessible to non-experts with open ears.
ABSTRACT Purpose: This review aims to reveal how music and dance participation relates to key social determinants of health, and acts as population wellbeing and health promotion and preventive tools for people without pre-existing health issues in diverse social groups. Methods: A qualitative systematic literature review addresses the questions: 1) How might music and dance cultural practices relate to social determinants of health?; 2) What gaps exist in the current body of research examining how healthy individuals/populations maintain and promote good health and wellbeing through music and dance participation?; 3) What are the implications of the current body of literature for healthcare, policy and research? Results: Music and dance relate to key social determinants of health, from social and cultural, and physical and mental health perspectives. A number of gaps in the literature exist, including underrepresentation of middle aged participants, men, and Indigenous, LGBTQIAP+, and migrant populations. There is a lack of consistency in theoretical and methodological approaches, and a language for effective communication across arts and health audiences. Conclusions: A significant opportunity exists for cross-disciplinary collaboration to advance healthcare and arts policy, generate cost-effective approaches to preventive healthcare practice, and enhance the wellbeing and health of large and diverse populations.
T. Stegemann, M. Geretsegger, Eva Phan Quoc
et al.
Background: In pediatric health care, non-pharmacological interventions such as music therapy have promising potential to complement traditional medical treatment options in order to facilitate recovery and well-being. Music therapy and other music-based interventions are increasingly applied in the clinical treatment of children and adolescents in many countries world-wide. The purpose of this overview is to examine the evidence regarding the effectiveness of music therapy and other music-based interventions as applied in pediatric health care. Methods: Surveying recent literature and summarizing findings from systematic reviews, this overview covers selected fields of application in pediatric health care (autism spectrum disorder; disability; epilepsy; mental health; neonatal care; neurorehabilitation; pain, anxiety and stress in medical procedures; pediatric oncology and palliative care) and discusses the effectiveness of music interventions in these areas. Results: Findings show that there is a growing body of evidence regarding the beneficial effects of music therapy, music medicine, and other music-based interventions for children and adolescents, although more rigorous research is still needed. The highest quality of evidence for the positive effects of music therapy is available in the fields of autism spectrum disorder and neonatal care. Conclusions: Music therapy can be considered a safe and generally well-accepted intervention in pediatric health care to alleviate symptoms and improve quality of life. As an individualized intervention that is typically provided in a person-centered way, music therapy is usually easy to implement into clinical practices. However, it is important to note that to exploit the potential of music therapy in an optimal way, specialized academic and clinical training and careful selection of intervention techniques to fit the needs of the client are essential.
Debussy’s ballet for children La Boîte à joujoux (1913) contains a fake “Old Hindu chant”. But the comic fakery seems to provide the key to understanding the deep meaning of the ballet, in which Debussy strives to rejuvenate and revitalize Western music by referring to the so-called spontaneity of both childhood and foreign traditions, for the sake of recovered authenticity.
The aim of this research is to identify the relationship between perception of Kyrgyz music and motivation for healthy lifestyle, and to understand the mechanisms of music impact on people's behavior within the context of healthy lifestyle. The research methods included literature review, questionnaire development and statistical analysis of data. A total of 248 participants, dominantly young people and women, took part in the online survey. Results showed that musical lyrics covering themes of health, physical activity, and positive thinking can inspire listeners to lead healthy lifestyles. Key images of space, emotions and natural phenomena in Kyrgyz song lyrics reflect the diverse understanding of health and well-being in the culture of Kyrgyz people. Both great composers of the past and modern musicians motivate Kyrgyz people to take care of their health with their patriotic and lyrical songs. The novelty of the research is in the study of a relatively new area of the influence of music on people's behavior within the scope of a healthy lifestyle.
Se no século XVIII a ritmopeia foi amplamente discutida por teóricos e estetas, o século XX reaviva esse interesse em lançar luz sobre o parâmetro rítmico, frequentemente negligenciado no período intermediário. Tanto as discussões do passado quanto as contemporâneas buscam esclarecer os conceitos e as complexas interações sonoras que compõem o aspecto musical do ritmo. Este artigo visa realizar uma revisão bibliográfica e mapear a linha teórica que conduz a uma compreensão mais objetiva e clara dos parâmetros fundamentais que definem a natureza do ritmo, investigando sua relação com a métrica, a hipermétrica e os fenômenos sonoros. Para tanto, adotaremos o modelo teórico-analítico proposto por Danuta Mirka, que reconstrói a genealogia das teorias sobre ritmopeia do século XVIII e incorpora as contribuições dos musicólogos contemporâneos. A problemática será abordada a partir de um estudo de caso específico de José Maurício Nunes Garcia, estabelecendo uma conexão entre as diferentes teorias para elucidar os princípios fundamentais que regem a conceituação do ritmo. Um ponto central que perpassa todo o percurso teórico é a ideia de hierarquia como organizadora do processo estético e cognitivo. Assim, este artigo demonstra esse processo como um elemento crucial para a compreensão do ritmo e sua relação com a dimensão métrica.
Initiating a quest to unravel the complexities of musical aesthetics through the lens of information dynamics, our study delves into the realm of musical sequence modeling, drawing a parallel between the sequential structured nature of music and natural language. Despite the prevalence of neural network models in MIR, the modeling of symbolic music events as applied to music cognition and music neuroscience has largely relied on statistical models. In this "proof of concept" paper we posit the superiority of neural network models over statistical models for predicting musical events. Specifically, we compare LSTM, Transformer, and GPT models against a widely-used markov model to predict a chord event following a sequence of chords. Utilizing chord sequences from the McGill Billboard dataset, we trained each model to predict the next chord from a given sequence of chords. We found that neural models significantly outperformed statistical ones in our study. Specifically, the LSTM with attention model led with an accuracy of 0.329, followed by Transformer models at 0.321, GPT at 0.301, and standard LSTM at 0.191. Variable Order Markov and Markov trailed behind with accuracies of 0.277 and 0.140, respectively. Encouraged by these results, we extended our investigation to multidimensional modeling, employing a many-to-one LSTM, LSTM with attention, Transformer, and GPT predictors. These models were trained on both chord and melody lines as two-dimensional data using the CoCoPops Billboard dataset, achieving an accuracy of 0.083, 0.312, 0.271, and 0.120, respectively, in predicting the next chord.
With the introduction of ChatGPT, the public’s perception of AI-generated content has begun to reshape. Artificial intelligence has significantly reduced the barrier to entry for non-professionals in creative endeavors, enhancing the efficiency of content creation. Recent advancements have seen significant improvements in the quality of symbolic music generation, which is enabled by the use of modern generative algorithms to extract patterns implicit in a piece of music based on rule constraints or a musical corpus. Nevertheless, existing literature reviews tend to present a conventional and conservative perspective on future development trajectories, with a notable absence of thorough benchmarking of generative models. This paper provides a survey and analysis of recent intelligent music generation techniques, outlining their respective characteristics and discussing existing methods for evaluation. Additionally, the paper compares the different characteristics of music generation techniques in the East and West as well as analysing the field’s development prospects.
It is often claimed that music training improves auditory and linguistic skills. Results of individual studies are mixed, however, and most evidence is correlational, precluding inferences of causation. Here, we evaluated data from 62 longitudinal studies that examined whether music training programs affect behavioral and brain measures of auditory and linguistic processing (N = 3928). For the behavioral data, a multivariate meta-analysis revealed a small positive effect of music training on both auditory and linguistic measures, regardless of the type of assignment (random vs. non-random), training (instrumental vs. non-instrumental), and control group (active vs. passive). The trim-and-fill method provided suggestive evidence of publication bias, but meta-regression methods (PET-PEESE) did not. For the brain data, a narrative synthesis also documented benefits of music training, namely for measures of auditory processing and of speech and prosody processing. Thus, the available literature provides evidence that music training produces small neurobehavioral enhancements in auditory and linguistic processing, although future studies are needed to confirm that such enhancements are not due to publication bias.
Abstract This study examines the digital transformation effects of artificial intelligence (AI)-based facial and music biometrics on customers’ cognitive and emotional states, and how these effects influence their behavioral responses in terms of value creation. Using a real-life, major optical retail store in China, 386 customers participated in a five-day experiment with different types of music (enhanced by music-recognition biometrics). The findings show that for utilitarian-type customers in a high-involvement AI purchase condition, music-recognition biometric-induced emotion mediates cognition and behavioral intentions. Both likability and the tempo of the music affect the impact of music on cognition. This study contributes to a better understanding of the relationship between cognition and emotion induced by AI-based facial and music biometric systems in shaping customer behavior and it adds to the atmospheric literature. This is a significant contribution given the paucity of research in the context of the Chinese retail environment, which is now a significant retail market with global importance.
The intersection between music and Extended Reality (XR) has grown significantly over the past twenty years, amounting to an established area of research today. The use of XR technologies represents a fundamental paradigm shift for various musical contexts as they disrupt traditional notions of musical interaction by enabling performers and audiences to interact musically with virtual objects, agents, and environments. This article both surveys and expands upon the knowledge accumulated in existing research in this area to build a foundation for future works that bring together Music and XR. To this end, we created a freely available dataset of 260 publications in this space and conducted an in-depth analysis covering 199 works in the last decade. We conducted this analysis using a list of conceptual dimensions belonging to technical, artistic, perceptual and methodological domains. This review of the literature is complemented with a set of interviews with domain experts with the goal of establishing a definition for the emergent field of Musical XR, i.e., the field of music in Extended Realities. Based on the results of the conducted review, a research agenda for the field is proposed.