The paper presents a new compositional hierarchical model for robust music transcription. Its main features are unsupervised learning of a hierarchical representation of input data, transparency, ...which enables insights into the learned representation, as well as robustness and speed which make it suitable for real-world and real-time use. The model consists of multiple layers, each composed of a number of parts. The hierarchical nature of the model corresponds well to hierarchical structures in music. The parts in lower layers correspond to low-level concepts (e.g. tone partials), while the parts in higher layers combine lower-level representations into more complex concepts (tones, chords). The layers are learned in an unsupervised manner from music signals. Parts in each layer are compositions of parts from previous layers based on statistical co-occurrences as the driving force of the learning process. In the paper, we present the model's structure and compare it to other hierarchical approaches in the field of music information retrieval. We evaluate the model's performance for the multiple fundamental frequency estimation. Finally, we elaborate on extensions of the model towards other music information retrieval tasks.
Herein, we report a computational algorithm that follows a spectroscopist-driven elucidation process of the structure of an organic molecule based on IR, 1H and 13C NMR, and MS tabular data. The ...algorithm is independent from database searching and is based on a bottom-up approach, building the molecular structure from small structural fragments visible in spectra. It employs an analytical combinatorial approach with a graph search technique to determine the connectivity of structural fragments that is based on the analysis of the NMR spectra, to connect the identified structural fragments into a molecular structure. After the process is completed, the interface lists the compound candidates, which are visualized by the WolframAlpha computational knowledge engine within the interface. The candidates are ranked according to the predefined rules for analyzing the spectral data. The developed elucidator has a user-friendly web interface and is publicly available (http://schmarnica.si).
This study analyzes the effect of using a virtual reality (VR) game as a complementary tool to improve users’ rhythmic performance and perception in a remote and self-learning environment. In recent ...years, remote learning has gained importance due to various everyday situations; however, the effects of using VR in such situations for individual and self-learning have yet to be evaluated. In music education, learning processes are usually heavily dependent on face-to-face communication with a teacher and are based on a formal or informal curriculum. The aim of this study is to investigate the potential of gamified VR learning and its influence on users’ rhythmic sensory and perceptual abilities. We developed a drum-playing game based on a tower defense scenario designed to improve four aspects of rhythmic perceptual skills in elementary school children with various levels of music learning experience. In this study, 14 elementary school children received Meta Quest 2 headsets for individual use in a 14-day individual training session. The results showed a significant increase in their rhythmical skills through an analysis of their rhythmic performance before and after the training sessions. In addition, the experience of playing the VR game and using the HMD setup was also assessed, highlighting some of the challenges of currently available affordable headsets for gamified learning scenarios.
Melody prediction is an important aspect of music listening. The success of prediction, i.e., whether the next note played in a song is the same as the one predicted by the listener, depends on ...various factors. In the paper, we present two studies, where we assess how music familiarity and music expertise influence melody prediction in human listeners, and, expressed in appropriate data/algorithmic ways, computational models. To gather data on human listeners, we designed a melody prediction user study, where familiarity was controlled by two different music collections, while expertise was assessed by adapting the Music Sophistication Index instrument to Slovenian language. In the second study, we evaluated the melody prediction accuracy of computational melody prediction models. We evaluated two models, the SymCHM and the Implication-Realization model, which differ substantially in how they approach melody prediction. Our results show that both music familiarity and expertise affect the prediction accuracy of human listeners, as well as of computational models.
We present the development and evaluation of a gamified rhythmic dictation application for music theory learning. The application was developed as a web application in the existing Troubadour ...platform for music ear training. The focus of the developed application was on user experience and engagement. With regards to the former, we developed a responsive and intuitive user interface; for the latter, we included several gamification elements and assessed the impact on the students’ engagement. We report on students’ experience analysis done through questionnaires and background data collected through the platform. We evaluated the rhythmic dictation application with the conservatory-level music theory students through A/B testing in order to independently evaluate the application’s impact. The results show a significant impact of the application on the students’ exam scores.
This paper presents a model capable of learning the rhythmic characteristics of a music signal through unsupervised learning. The model learns a multi-layer hierarchy of rhythmic patterns ranging ...from simple structures on lower layers to more complex patterns on higher layers. The learned hierarchy is fully transparent, which enables observation and explanation of the structure of the learned patterns. The model employs tempo-invariant encoding of patterns and can thus learn and perform inference on tempo-varying and noisy input data. We demonstrate the model’s capabilities of learning distinctive rhythmic structures of different music genres using unsupervised learning. To test its robustness, we show how the model can efficiently extract rhythmic structures in songs with changing time signatures and live recordings. Additionally, the model’s time-complexity is empirically tested to show its usability for analysis-related applications.
This paper presents a new version and a three-month evaluation of the Troubadour platform—an open-source music theory ear training platform. Through interviews with teachers, we gathered the ...most-needed features which would aid their use of the platform. In the new version of the Troubadour platform, we implemented different types of interaction, including class management, re-occurring homework and challenges. Previous research has shown a significant improvement in the students’ performance while using the platform. However, the short time span of the previous experiments has not shown whether these results can be attributed to the novelty bias. To evaluate the efficacy of the platform beyond its novelty bias, we performed a three-month-long evaluation experiment on the students’ interaction through questionnaires and platform-collected data. We collected data on their engagement with the platform. During the experiment, the students attended the school through online courses during the first part of the evaluation, and in-class in the second part. In this paper, we investigate the students’ engagement during the three-month period, explore the influence of the platform’s use in-class versus online learning process, analyze the students’ self-report on their practice habits and compare them with the collected data. The results showed high student engagement during the lockdown period, while the in-class process showed a decrease in the platform’s use, unveiling the students’ need for such platform as a complementary learning channel in remote learning.
Trubadur je odprtokodna platforma za urjenje glasbenega posluha z avtomatiziranimi vajami ritmičnega in intervalnega nareka. Platformo smo ovrednotili z dijaki Konservatorija za glasbo in balet ...Ljubljana v šolskih letih 2018/19–2020/21. Rezultati evalvacije so pokazali, da lahko uporaba platforme poveča uspešnost pri testih in predstavlja dopolnitev učenja na daljavo.
The importance of self-regulated learning (SRL) has increased during the COVID-19 pandemic and measures for assessing students' self-regulation skills and knowledge are greatly needed. We present the ...results of the first thorough adaptation of the Children's Perceived use of Self-Regulated Learning Inventory (CP-SRLI). The inventory, consisting of 15 scales measuring nine components of SRL, was administered to a sample of 541 Slovenian ninth graders. Confirmatory factor analyses supported internal structure validity of most components, but two components required some structural modifications. Internal consistency coefficients were acceptable for the majority of scale scores and were highly comparable to the original ones. While metric invariance across gender was confirmed, the scalar invariance of some scales needs further examination. Meaningful correlations with relevant externally assessed and self-reported self-regulation and school performance variables indicated good criterion validity of the inventory. The Slovenian version of the CP-SRLI thus proved to be a sufficiently valid and reliable instrument for assessing pupils' learning self-regulation.
The study of inter-annotator agreement in musical pattern annotations has gained increased attention over the past few years. While expert annotations are often taken as the reference for evaluating ...pattern discovery algorithms, relying on just one reference is not usually sufficient to capture the complex musical relations between patterns. In this paper, we address the potential of digital annotation tools to enable large-scale annotations of musical patterns, by comparing datasets gathered with two recently developed digital tools. We investigate the influence of the tools and different annotator backgrounds on the annotation process by performing inter-annotator agreement analysis and feature-based analysis on the annotated patterns. We discuss implications for further adaptation of annotation tools, and the potential for deriving reference data from such rich annotation datasets for the evaluation of automatic pattern discovery algorithms in the future.
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