Reconsidering memorisation in the context of non-tonal piano music

Performers, pedagogues and researchers have shared interest in the topic of musical memorisation for centuries. A large and diverse body of studies on this subject has contributed to the current understanding of musicians’ views of performing from memory, as well as the mechanisms governing encoding and retrieval of musical information. Nevertheless, with a few exceptions, existing research is still highly based on tonal music and lacks further examination in the musical world of non-tonality. The convention of performing from memory is a well-established practice for particular instruments and musical genres, but an exception is often made for recent styles of repertoire moving away from tonality. No study to date has systematically investigated the reasons for such exception and musicians’ views on this matter. Moreover, the existing principles of memorisation that are thought to apply to musicians in the highest levels of skill are strongly based on the use of conceptual knowledge of tonal musical vernacular. Such knowledge is often obscured or absent in non-tonal repertoire. This thesis aims to extend the findings of previous research into musical memorisation in the context of non-tonal piano repertoire by documenting pianists’ views and practices in committing this music to memory. An interview study with pianists expert in contemporary music (Chapter 3) establishes the background for the thesis. A variety of views on performing contemporary music from memory were reported, with several pianists advocating benefits from performing this repertoire by heart and others from using the score. Memorisation accounts revealed idiosyncrasy and variety, but emphasised the importance of specific strategies, such as the use of mental rehearsal, principles of chunking applicable to this repertoire and the importance of different types of memory and their combination. The second study (Chapter 4) explores the topic in further depth, by thoroughly examining the author’s entire process of learning and memorising a newly commissioned non-tonal piece for prepared piano. This study extends findings from performance cue (PC) theory. This widely recognised account of expert memory in music suggests that musicians develop retrieval schemes hierarchically organised around their understanding of musical structure, using different types of PCs. The use of retrieval schemes in this context is confirmed by this study. The author organised the scheme around her own understanding of musical structure, which was gradually developed while working through the piece, since the music had no aural model available or ready-made structural framework to hold on to early in the process. Extending previous research, new types of PCs were documented and, for the first time, negative serial position effects were found for basic PCs (e.g., fingering, notes, patterns) in long-term recall. Finally, the study provided behavioural evidence for the use of chunking in non-tonal piano music. The third study (Chapters 5 and 6) extends these findings to a serial piece memorised by six pianists. Following a multiple-case study approach, this study observed in great depth memorisation approaches carried out by two of those pianists, who performed the music very accurately from memory, and by one pianist who performed less accurately. The first two pianists developed retrieval schemes based on their understanding of musical structure and different types of PCs, mainly basic and structural. Comparisons between the pianists revealed very different views of musical structure in the piece. Even so, both musicians used such understanding to organise encoding and retrieval. The pianist with the least accurate performance adopted an unsystematic approach, mainly relying on incidental memorisation. The absence of a conceptual retrieval scheme resulted in an inability to fully recover from a major memory lapse in performance. The findings of this research provide novel insights into pianists’ views towards performing non-tonal music from memory and into the cognitive mechanisms governing the encoding and retrieval of this music, which have practical applications for musicians wishing to memorise non-tonal piano music

Impact of the assimilation of conventional data on the quantitative precipitation forecasts in the Eastern Mediterranean

International audienceThis study is devoted to the evaluation of the role of assimilation of conventional data on the quantitative precipitation forecasts at regional scale. The conventional data included surface station reports as well as upper air observations. The analysis was based on the simulation of 15 cases of heavy precipitation that occurred in the Eastern Mediterranean. The verification procedure revealed that the ingestion of conventional data by objective analysis in the initial conditions of BOLAM limited area model do not result in a statistically significant improvement of the quantitative precipitation forecasts

I Love Her Oh! Oh! Oh! / music by James V. Monaco; words by Joe Mc Carthy and E. P. Morgan

Cover: drawing of a dancing man; photo inset of Al Jolson; Publisher: Broadway Music Corporation (New York)https://egrove.olemiss.edu/sharris_c/1056/thumbnail.jp

Deep Embeddings for Robust User-Based Amateur Vocal Percussion Classification

Vocal Percussion Transcription (VPT) is concerned with the automatic detection and classification of vocal percussion sound events, allowing music creators and producers to sketch drum lines on the fly. Classifier algorithms in VPT systems learn best from small user-specific datasets, which usually restrict modelling to small input feature sets to avoid data overfitting. This study explores several deep supervised learning strategies to obtain informative feature sets for amateur vocal percussion classification. We evaluated the performance of these sets on regular vocal percussion classification tasks and compared them with several baseline approaches including feature selection methods and a speech recognition engine. These proposed learning models were supervised with several label sets containing information from four different levels of abstraction: instrument-level, syllable-level, phoneme-level, and boxeme-level. Results suggest that convolutional neural networks supervised with syllable-level annotations produced the most informative embeddings for classification, which can be used as input representations to fit classifiers with. Finally, we used back-propagation-based saliency maps to investigate the importance of different spectrogram regions for feature learning

ACPAS: A Dataset of Aligned Classical Piano Audio and Scores for Audio-to-Score Transcription

We create the ACPAS dataset with aligned audio and scores on classical piano music for automatic music audio-to-score transcription research. The dataset contains 497 distinct music scores aligned with 2189 audio performances, 179.8 hours in total. To our knowledge, it is currently the largest dataset for audio-to-score transcription research. We provide aligned performance audio, performance MIDI and MIDI scores, together with beat, key signature, and time signature annotations. The dataset is partly collected from a list of existing automatic music transcription (AMT) datasets, and partly synthesized. Both real recordings and synthetic recordings are included. We provide a train/validation/test split with no piece overlap and in line with splits in other AMT datasets

Ultra-stiff metallic glasses through bond energy density design

The elastic properties of crystalline metals scale with their valence electron density. Similar observations have been made for metallic glasses. However, for metallic glasses where covalent bonding predominates, such as metalloid metallic glasses, this relationship appears to break down. At present, the reasons for this are not understood. Using high energy x-ray diffraction analysis of melt spun and thin film metallic glasses combined with density functional theory based molecular dynamics simulations, we show that the physical origin of the ultrahigh stiffness in both metalloid and non-metalloid metallic glasses is best understood in terms of the bond energy density. Using the bond energy density as novel materials design criterion for ultra-stiff metallic glasses, we are able to predict a Co$_{33.0}$Ta$_{3.5}$B$_{63.5}$ short range ordered material by density functional theory based molecular dynamics simulations with a high bond energy density of 0.94 eV Å$^{-3}$ and a bulk modulus of 263 GPa, which is 17% greater than the stiffest Co-B based metallic glasses reported in literature.The authors acknowledge support by the German National Science Foundation (DFG) within the SPP-1594. Simulations were performed with computing resources granted by JARA-HPC from RWTH Aachen University under project JARA0131. Parts of this research were carried out at the light source PETRA III (beamline P02.1) at DESY, a member of the Helmholtz Association (HGF). WJC also acknowledges the support of the EPSRC/Rolls-Royce Strategic Partnership (EP/M005607/1)

Adaptive Time–Frequency Scattering for Periodic Modulation Recognition in Music Signals

Vibratos, tremolos, trills, and flutter-tongue are techniques frequently found in vocal and instrumental music. A common feature of these techniques is the periodic modulation in the time--frequency domain. We propose a representation based on time--frequency scattering to model the inter-class variability for fine discrimination of these periodic modulations. Time--frequency scattering is an instance of the scattering transform, an approach for building invariant, stable, and informative signal representations. The proposed representation is calculated around the wavelet subband of maximal acoustic energy, rather than over all the wavelet bands. To demonstrate the feasibility of this approach, we build a system that computes the representation as input to a machine learning classifier. Whereas previously published datasets for playing technique analysis focus primarily on techniques recorded in isolation, for ecological validity, we create a new dataset to evaluate the system. The dataset, named CBF-periDB, contains full-length expert performances on the Chinese bamboo flute that have been thoroughly annotated by the players themselves. We report F-measures of 99% for flutter-tongue, 82% for trill, 69% for vibrato, and 51% for tremolo detection, and provide explanatory visualisations of scattering coefficients for each of these techniques

DMRN+18: Digital Music Research Network One-day Workshop 2023

DMRN+18: Digital Music Research Network One-day Workshop 2023 Queen Mary University of London Tuesday 19th December 2023 • Keynote speaker: Stefan Bilbao The Digital Music Research Network (DMRN) aims to promote research in the area of digital music, by bringing together researchers from UK and overseas universities, as well as industry, for its annual workshop. The workshop will include invited and contributed talks and posters. The workshop will be an ideal opportunity for networking with other people working in the area. Keynote speakers: Stefan Bilbao Tittle: Physics-based Audio: Sound Synthesis and Virtual Acoustics. Abstract: Any acoustically-produced sound produced must be the result of physical laws that describe the dynamics of a given system---always at least partly mechanical, and sometimes with an electronic element as well. One approach to the synthesis of natural acoustic timbres, thus, is through simulation, often referred to in this context as physical modelling, or physics-based audio. In this talk, the principles of physics-based audio, and the various different approaches to simulation are described, followed by a set of examples covering: various musical instrument types; the important related problem of the emulation of room acoustics or “virtual acoustics”; the embedding of instruments in a 3D virtual space; electromechanical effects; and also new modular instrument designs based on physical laws, but without a counterpart in the real world. Some more technical details follow, including the strengths, weaknesses and limitations of such methods, and pointers to some links to data-centred black-box approaches to sound generation and effects processing. The talk concludes with some musical examples and recent work on moving such algorithms to a real-time setting.. Bio: Stefan is a Professor (full) at Reid School of Music, University of Edinburgh, he is the Personal Chair of Acoustics and Audio Signal Processing, Music. He currently works on computational acoustics, for applications in sound synthesis and virtual acoustics. Special topics of interest include: Finite difference time domain methods, distributed nonlinear systems such as strings and plates, architectural acoustics, spatial audio in simulation, multichannel sound synthesis, and hardware and software realizations. More information on: https://www.acoustics.ed.ac.uk/group-members/dr-stefan-bilbao/ DMRN+18 is sponsored by The UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM); a leading PhD research programme aimed at the Music/Audio Technology and Creative Industries, based at Queen Mary University of London