1,720,965 research outputs found
Music in newspapers: Interdisciplinary opportunities and data-related challenges
No full textOver the past millennia, music has actively been performed and listened to by mankind, thus also playing an important role in establishing sociocultural identities that have evolved over time. In parallel, for many centuries, newspapers played an important role in informing society on a regular and frequent basis on topics noteworthy at that time. Therefore, in retrospect, these newspapers offer windows into historic topics of sociocultural significance, including cultural and musical life. Thanks to ongoing digitization efforts, large-scale newspaper corpora now have become broadly available and accessible. Taking the digitized historical newspaper collection of the National Library of The Netherlands as an example, in this paper, we discuss how considering music-related mentionings in newspapers can enable potential new research directions and questions. We discuss open syntactic and semantic data-related technical challenges when analyzing music-related mentionings in digitized historical newspaper collections. Finally, we discuss how successful detection of music-related mentionings can also benefit engagement of non-scholarly end users, concluding with an invitation to the interdisciplinary research community to actively contribute to the given use case.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia Computin
Plugify: Online booking platform for live music
No full textElectrical Engineering, Mathematics and Computer ScienceIntelligent SystemsTechnische Informatic
Mass Media Musical Meaning: Opportunities from the Collaborative Web
No full textIn the digital domain, music is usually studied from a positivist viewpoint, focusing on general ‘objective’ music descriptors. In this work, we strive to put music in a more social and cultural context, looking into ways to unify data analysis methods with thoughts from the humanities on musical meaning and significance. More specifically, we investigate whether information in collaborative web resources on movie plot narratives and folksonomic song tags is capable of revealing common associations between these two. Reported initial findings suggest this is indeed the case, which opens opportunities for further work in this area, cross-disciplinary collaborations, and novel contextually oriented music information retrieval application scenarios.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Multifaceted Approaches to Music Information Retrieval
No full textMusic is a multifaceted phenomenon: beyond addressing our auditory channel, the consumption of music triggers further senses. Also in creating and communicating music, multiple modalities are at play. Next to this, it allows for various ways of interpretation: the same musical piece can be performed in different valid ways, and audiences can in their turn have different reception and interpretation reactions towards music. Music is experienced in many different everyday contexts, which are not confined to direct performance and consumption of musical content alone: instead, music frequently is used to contextualize non-musical settings, ranging from audiovisual productions to special situations and events in social communities. Finally, music is a topic under study in many different research fields, ranging from the humanities and social sciences to natural sciences, and—with the advent of the digital age—in engineering as well. In this thesis, we argue that the full potential of digital music data can only be unlocked when considering the multifaceted aspects as mentioned above. Adopting this view, we provide multiple novel studies and methods for problems in the Music Information Retrieval field: the dedicated research field established to deal with the creation of analysis, indexing and access mechanisms to digital music data. A major part of the thesis is formed by novel methods to perform data-driven analyses of multiple recorded music performances. Proposing a top-down approaches investigating similarities and dissimilarities across a corpus of multiple performances of the same piece, we discuss how this information can be used to reveal varying amounts of artistic freedom over the timeline of a musical piece, initially focusing on the analysis of alignment patterns in piano performance. After this, we move to the underexplored field of comparative analysis of orchestral recordings, proposing how differences between orchestral renditions can further be visualized, explained and related to one another by adopting techniques borrowed from visual human face recognition techniques. The other major part of the thesis considers the challenge of auto-suggesting suitable soundtracks for user-generated video. Building on thoughts in Musicology, Media Studies and Music Psychology, we propose a novel prototypical system which explicitly solicits the intended narrative for the video, and employs information from collaborative web resources to establish connotative connections to musical descriptors, followed by audiovisual reranking. To assess what features can relevantly be employed in search engine querying scenarios, we also further investigate what elements in free-form narrative descriptions invoked by production music are stable, revealing connections to linguistic event structure. Further contributions of the thesis consist of extensive positioning of the newly proposed directions in relation to existing work, and known practical end-user stakeholder demands. As we will show, the paradigms and technical work proposed in this thesis managed to push significant steps forward in employing multimodality, allowing for various ways of interpretation and opening doors to viable and realistic multidisciplinary approaches which are not solely driven by a technology push. Furthermore, ways to create concrete impact at the consumer experience side were paved, which can be more deeply acted upon in the near future.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Extracting information from collections of musical performances using Dynamic Time Warping
No full textWhen performing a piece of music, there are certain ways in which performers turn the score into their own musical interpretation of it. The difference between what is written and what is played can be substantial, and can vary between performers and even individual performances. In this thesis we examine what information can be gained from analyzing the differences that can be found when inspecting a collection of performances of a piece by various performers. We develop several techniques for analyzing the alignment yielded by Dynamic Time Warping and also explore the possibility of employing techniques from computer graphics to solve the task of finding an alignment. We test the techniques on synthetic data first, where applicable. We also test the techniques on a real dataset, a collection of performances of Chopin's mazurkas. Finally, we give some indication of how the techniques may most effectively be put to use based on the results of the tests.Electrical Engineering, Mathematics and Computer ScienceIntelligent SystemsMedia and Knowledge Engineerin
Capturing the Meaning of Complex Texts about Music
No full textFollowing four annual C@merata evaluations at MediaEval, in which natural language phrases about music must be mapped to passages in MusicXML scores, we have developed a representation which captures the meaning of a text about music as a JSON feature structure. We have written a system to convert any text to this repres entation, comprising 23 stages of pre-processing, followed by application of the SpaCy statistical dependency parser. We have applied the system to the C@merata test queries with accurate results. Our approach can capture extremely detailed information, as well as general musical ideas.Multimedia Computin
Towards better understanding of cover song retrieval: A modular evaluation of system choices
No full textA study is presented of the cover song retrieval problem, in which multiple performances of the same musical work are sought. The problem is considered in the context of content-based audio retrieval. In order to gain more insight into the current state-of-the art in cover song retrieval research, several existing approaches to cover song retrieval are studied in a modular way. Modules from different approaches are systematically recombined and each resulting combination is tested on several designated datasets. The modules have been chosen such that they reflect general and independent system decisions, while the datasets were constructed to each pose a specific and known subset of the broad range of cover song types and similarity challenges. For the experiments that are carried out, we depart from cover song system combinations that use the conventional approach of representing songs as absolute chroma vectors over time. Additionally, we transform these representations into a statistical meta-representation and also study the influences of using relative first-order time-differential information. In the results obtained, several critical choices in system modules influencing the overall performance can be identified. Our work is presented by first discussing the cover song retrieval problem in a top-down way, starting from the general musical and technical issues posed that form the inspiration for our choice of considered system modules. Subsequently, we continue by discussing the existing approaches that were studied in more detail. After explaining our experimental setup and evaluation methodology, the results of our work are presented. Finally, considering the results of our work, several suggestions for future directions in cover song retrieval research are made that especially focus at gaining more understanding in the relation between high-level aspects and technical solutions.MediamaticsElectrical Engineering, Mathematics and Computer Scienc
Comparative analysis of orchestral performance recordings: An image-based approach
No full textTraditionally, the computer-assisted comparison of multiple performances of the same piece focused on performances on single instruments. Due to data availability, there also has been a strong bias towards analyzing piano performances, in which local timing, dynamics and articulation are important expressive performance features. In this paper, we consider the problem of analyzing multiple performances of the same symphonic piece, performed by different orchestras and different conductors. While differences between interpretations in this genre may include commonly studied features on timing, dynamics and articulation, the timbre of the orchestra and choices of balance within the ensemble are other important aspects distinguishing different orchestral interpretations from one another. While it is hard to model these higher-level aspects as explicit audio features, they can usually be noted visually in spectrogram plots. We therefore propose a method to compare orchestra performances by examining visual spectrogram characteristics. Inspired by eigenfaces in human face recognition, we apply Principal Components Analysis on synchronized performance fragments to localize areas of cross-performance variation in time and frequency. We discuss how this information can be used to examine performer differences, and how beyond pairwise comparison, relative differences can be studied between multiple performances in a corpus at once.Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Understanding Data Scientists’ use of Explainability and Interpretability tools
No full textMachine learning techniques are being used increasingly in high-risk domains such as healthcare, law, and finance. Misclassifications or mispredictions in areas like these can have serious consequences. Therefore, it is crucial to have high performing models that can make as minimal errors as possible. However, in some cases, it is not enough to know what the predictions and classifications are, it is also important to understand why a given model is making certain decisions. Explainability and interpretability tools help data scientists in understanding how an output is obtained by a machine learning model from a given input. Model explainability and interpretability tools like SHAP (Shapley Additive Explanations) and GAMs (Generalised additive models) are becoming widely used. However, user studies that evaluate the extent to which these tools help data scientists interpret and explain their models are still uncommon. Our study attempts to find out how data scientists use these tools – their general behaviour, factors affecting their trust in the tools, and their expectations from the tools. We conduct a qualitative study consisting of Pilot interviews and Contextual inquiries. By analyzing the results, the study shows that the tools sometimes are not used at their full potential. Moreover, some participants expressed the need to also align with other stakeholders to get the full picture on the explanations and trust them. The most important factors affecting their trust were the lack of clarity in understanding the visualisations that further lead to the participants reasoning intuitively and having suspicions about the explanations,Computer Scienc
“It’s the most fair thing to do, but it doesn’t make any sense”: Perceptions of mathematical fairness notions by hiring professionals
No full textMathematical fairness notions introduced in literature aim to make algorithmic decisions fair. However, their usage has been criticized in domains such as recidivism and lending for producing unfair decisions. Questions regarding fairness, which also have an important role in hiring are giving way to concerns about the increasing adoption of algorithmic decision-making systems in the field. However, there are no concrete studies linking mathematical notions to actual perceptions of fairness by people active in hiring and applicant selection.We aim to explore the understanding and alignment of existing fairness notions by organizational representatives in the context of early candidate selection in hiring. Towards that, we interviewed 17 professionals from executive functions, talent acquisition, HR, I/O psychology, and diversity and inclusion operations in The Netherlands. By designing user-friendly illustrations and explanations in the context of early candidate selection in hiring, we explore their ratings and responses to six fairness notions on understandability, perception of fairness, perception of diversity, and applicability. Our qualitative investigation suggests that these fairness notions raise three concerns. One, they lack additional contexts such as a company's size or diversity goals. Two, they give rise to several ethical and practical concerns such as lack of trust in the data, disadvantaging minorities, or the selection of unqualified applicants. Lastly, they act only as a small step towards fairness in the large hiring pipeline. We conclude that a qualitative approach in collaboration between designers, practitioners, and policymakers is the key to refinement and contextualization of future technologically enabled fair hiring policies. Our participants' intrinsic motivation to engage with the topic of fairness strengthens our case.Computer Scienc
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