Humanities and Engineering Perspectives on Music Transcription:

Music transcription is a process of creating a notation of musical sounds. It has been used as a basis for the analysis of music from a wide variety of cultures. Recent decades have seen an increasing amount of engineering research within the field of Music Information Retrieval that aims at automatically obtaining music transcriptions in Western staff notation. However, such approaches are not widely applied in research in ethnomusicology. This article aims to bridge interdisciplinary gaps by identifying aspects of proximity and divergence between the two fields. As part of our study, we collected manual transcriptions of traditional dance tune recordings by eighteen transcribers. Our method employs a combination of expert and computational evaluation of these transcriptions. This enables us to investigate the limitations of automatic music transcription (AMT) methods and computational transcription metrics that have been proposed for their evaluation. Based on these findings, we discuss promising avenues to make AMT more useful for studies in the Humanities. These are, first, assessing the quality of a transcription based on an analytic purpose; secondly, developing AMT approaches that are able to learn conventions concerning the transcription of a specific style; thirdly, a focus on novice transcribers as users of AMT systems; and, finally, considering target notation systems different from Western staff notation

Beyond Diverse Datasets: Responsible MIR, Interdisciplinarity, and the Fractured Worlds of Music

Musical worlds, not unlike our lived realities, are fundamentally fragmented and diverse, a fact often seen as a challenge or even a threat to the validity of research in Music Information Research (MIR). In this article, we propose to treat this characteristic of our musical universe(s) as an opportunity to fundamentally enrich and re-orient MIR. We propose that the time has arrived for MIR to reflect on its ethical and cultural turns (if they have been initiated at all) and take them a step further, with the goal of profoundly diversifying the discipline beyond the diversification of datasets. Such diversification, we argue, is likely to remain superficial if it is not accompanied by a simultaneous auto-critique of the discipline’s raison d’être. Indeed, this move to diversify touches on the philosophical underpinnings of what MIR is and should become as a field of research: What is music (ontology)? What are the nature and limits of knowledge concerning music (epistemology)? How do we obtain such knowledge (methodology)? And what about music and our own research endeavor do we consider “good” and “valuable” (axiology)? This path involves sincere inter- and intra-disciplinary struggles that underlie MIR, and we point to “agonistic interdisciplinarity” — that we have practiced ourselves via the writing of this article — as a future worth reaching for. The two featured case studies, about possible philosophical re-orientations in approaching ethics of music AI and about responsible engineering when AI meets traditional music, indicate a glimpse of what is possible

Music, computing and health: A roadmap for the current and future roles of music technology for health care and well-being.

Health and self-regulatio

The search for transient astrophysical neutrino emission with IceCube-DeepCore

We present the results of a search for astrophysical sources of brief transient neutrino emission using IceCube and DeepCore data acquired between 2012 May 15 and 2013 April 30. While the search methods employed in this analysis are similar to those used in previous IceCube point source searches, the data set being examined consists of a sample of predominantly sub-TeV muon-neutrinos from the Northern Sky (-5 degrees < delta < 90 degrees) obtained through a novel event selection method. This search represents a first attempt by IceCube to identify astrophysical neutrino sources in this relatively unexplored energy range. The reconstructed direction and time of arrival of neutrino events are used to search for any significant self-correlation in the data set. The data revealed no significant source of transient neutrino emission. This result has been used to construct limits at timescales ranging from roughly 1 s to 10 days for generic soft-spectra transients. We also present limits on a specific model of neutrino emission from soft jets in core-collapse supernovae

Genome-wide association analyses of physical activity and sedentary behavior provide insights into underlying mechanisms and roles in disease prevention

Although physical activity and sedentary behavior are moderately heritable, little is known about the mechanisms that influence these traits. Combining data for up to 703,901 individuals from 51 studies in a multi-ancestry meta-analysis of genome-wide association studies yields 99 loci that associate with self-reported moderate-to-vigorous intensity physical activity during leisure time (MVPA), leisure screen time (LST) and/or sedentary behavior at work. Loci associated with LST are enriched for genes whose expression in skeletal muscle is altered by resistance training. A missense variant in ACTN3 makes the alpha-actinin-3 filaments more flexible, resulting in lower maximal force in isolated type IIA muscle fibers, and possibly protection from exercise-induced muscle damage. Finally, Mendelian randomization analyses show that beneficial effects of lower LST and higher MVPA on several risk factors and diseases are mediated or confounded by body mass index (BMI). Our results provide insights into physical activity mechanisms and its role in disease prevention. Multi-ancestry meta-analyses of genome-wide association studies for self-reported physical activity during leisure time, leisure screen time, sedentary commuting and sedentary behavior at work identify 99 loci associated with at least one of these traits

Physical activity attenuates the influence of FTO variants on obesity risk : a meta-analysis of 218,166 adults and 19,268 children

BACKGROUND: The FTO gene harbors the strongest known susceptibility locus for obesity. While many individual studies have suggested that physical activity (PA) may attenuate the effect of FTO on obesity risk, other studies have not been able to confirm this interaction. To confirm or refute unambiguously whether PA attenuates the association of FTO with obesity risk, we meta-analyzed data from 45 studies of adults (n = 218,166) and nine studies of children and adolescents (n = 19,268). METHODS AND FINDINGS: All studies identified to have data on the FTO rs9939609 variant (or any proxy [r(2)>0.8]) and PA were invited to participate, regardless of ethnicity or age of the participants. PA was standardized by categorizing it into a dichotomous variable (physically inactive versus active) in each study. Overall, 25% of adults and 13% of children were categorized as inactive. Interaction analyses were performed within each study by including the FTO×PA interaction term in an additive model, adjusting for age and sex. Subsequently, random effects meta-analysis was used to pool the interaction terms. In adults, the minor (A-) allele of rs9939609 increased the odds of obesity by 1.23-fold/allele (95% CI 1.20-1.26), but PA attenuated this effect (p(interaction)  = 0.001). More specifically, the minor allele of rs9939609 increased the odds of obesity less in the physically active group (odds ratio  = 1.22/allele, 95% CI 1.19-1.25) than in the inactive group (odds ratio  = 1.30/allele, 95% CI 1.24-1.36). No such interaction was found in children and adolescents. CONCLUSIONS: The association of the FTO risk allele with the odds of obesity is attenuated by 27% in physically active adults, highlighting the importance of PA in particular in those genetically predisposed to obesity.Peer reviewe

Μία Προσέγγιση Ταξινόμησης Μουσικής βασισμένη σε Συνιστώσες

This thesis introduces a new feature set based on a Non-negative Matrix Factorization approach for the classification of musical signals into genres, only using synchronous organization of music events (vertical dimension of music). This feature set generates a vector space to describe the spectrogram representation of a music signal. The space is modeled statistically by a mixture of Gaussians (GMM). A new signal is classified by considering the likelihoods over all the estimated feature vectors given these statistical models, without constructing a model for the signal itself. Cross-validation tests on two commonly utilized datasets for this task show the superiority of the proposed features compared to the widely used MFCC type of representation based on classification accuracies (over 9% of improvement), as well as on a stability measure introduced in this thesis for GMM. Furthermore, we compare results of Non-negative Matrix Factorization and Independent Component Analysis when used for the approximation of spectrograms, documenting the superiority of Non-negative Matrix Factorization. Based on our findings we give a concept for a complete musical genre classification system using matrix factorization and Support Vector Machines.Παρουσιάζεται ενα καινούργιο σύνολο χαρακτηριστικών για την περιγραφή μουσικής. Περιγράφει την κάθετη δομή μουσικής στηριζόμενο σε Μη-αρνητική Παραγοντοποίηση Πινάκων φασματογραφημάτων. Τα χαρακτηριστικά σχηματίζουν μία βάση φάσματος για μουσικούς ήχους. Αυτές οι βάσεις μοντελοποιούνται στατιστικά ούτως ώστε να παρθούν περιγραφές των χαρακτηριστικών μίας συλλογής ομοίων μουσικών κομματιών. Δείχνουμε την ανωτερότητα του συγκριτικά με MFCC χαρακτηριστικά χρησιμοποιώντας μετρικές απόστασης και ακρίβεια της ταξινόμησης μουσικών ειδών. Επιπλέον, δείχνουμε ότι η Μη-αρνητική Παραγοντοποίηση Πινάκων έχει καλύτερες επιδόσεις στην προσέγγιση ενός δοθέντος φασματογραφήματος από την Independent Component Analysis. Βασιζόμενοι στα ευρήματά μας προτείνουμε την σύλληψη του συστήματος πλήρης ταξινόμησης μουσικών ειδών χρησιμοποιώντας παραγοντοποίηση πινάκων και Support Vector Machines