Piano Pedaller: A Measurement System for Classification and Visualisation of Piano Pedalling Techniques

date-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfdate-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfdate-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfdate-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfdate-added: 2017-12-22 18:53:42 +0000 date-modified: 2017-12-22 19:03:05 +0000 keywords: piano gesture recognition, optical sensor, real-time data acquisition, bela, music informatics local-url: https://pdfs.semanticscholar.org/fd00/fcfba2f41a3f182d2000ca4c05fb2b01c475.pdf publisher-url: http://homes.create.aau.dk/dano/nime17/ bdsk-url-1: http://www.nime.org/proceedings/2017/nime2017_paper0062.pdfThis paper presents the results of a study of piano pedalling techniques on the sustain pedal using a newly designed measurement system named Piano Pedaller. The system is comprised of an optical sensor mounted in the piano pedal bearing block and an embedded platform for recording audio and sensor data. This enables recording the pedalling gesture of real players and the piano sound under normal playing conditions. Using the gesture data collected from the system, the task of classifying these data by pedalling technique was undertaken using a Support Vector Machine (SVM). Results can be visualised in an audio based score following application to show pedalling together with the player’s position in the score

SU(2)-invariant spin-1/2 Hamiltonians with RVB and other valence bond phases

We construct a family of rotationally invariant, local, S=1/2 Klein Hamiltonians on various lattices that exhibit ground state manifolds spanned by nearest-neighbor valence bond states. We show that with selected perturbations such models can be driven into phases modeled by well understood quantum dimer models on the corresponding lattices. Specifically, we show that the perturbation procedure is arbitrarily well controlled by a new parameter which is the extent of decoration of the reference lattice. This strategy leads to Hamiltonians that exhibit i) $Z_2$ RVB phases in two dimensions, ii) U(1) RVB phases with a gapless ``photon'' in three dimensions, and iii) a Cantor deconfined region in two dimensions. We also construct two models on the pyrochlore lattice, one model exhibiting a $Z_2$ RVB phase and the other a U(1) RVB phase.Comment: 16 pages, 15 figures; 1 figure and some references added; some minor typos fixe

A review of differentiable digital signal processing for music and speech synthesis

The term “differentiable digital signal processing” describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilitating their integration into neural networks. This article surveys the literature on differentiable audio signal processing, focusing on its use in music and speech synthesis. We catalogue applications to tasks including music performance rendering, sound matching, and voice transformation, discussing the motivations for and implications of the use of this methodology. This is accompanied by an overview of digital signal processing operations that have been implemented differentiably, which is further supported by a web book containing practical advice on differentiable synthesiser programming (https://intro2ddsp.github.io/). Finally, we highlight open challenges, including optimisation pathologies, robustness to real-world conditions, and design trade-offs, and discuss directions for future research

Zero-shot Singing Technique Conversion

In this paper we propose modifications to the neural network framework, AutoVC for the task of singing technique conversion. This includes utilising a pretrained singing technique encoder which extracts technique information, upon which a decoder is conditioned during training. By swapping out a source singer’s technique information for that of the target’s during conversion, the input spectrogram is reconstructed with the target’s technique. We document the beneficial effects of omitting the latent loss, the importance of sequential training, and our process for fine-tuning the bottleneck. We also conducted a listening study where participants rate the specificity of technique-converted voices as well as their naturalness. From this we are able to conclude how effective the technique conversions are and how different conditions affect them, while assessing the model’s ability to reconstruct its input data

SAFE: A System for Extraction and Retrieval of Semantic Audio Descriptors

date-added: 2014-08-02 10:04:50 +0000 date-modified: 2014-11-26 17:42:49 +0000 keywords: semantic audio, VST plugins, data collection, ISMIR demoIn this paper, we present an overview of the Semantic Audio Feature Extraction (SAFE) Project, a system for the extraction and retrieval of semantic descriptions of musical timbre, deployed within the digital audio workstation. By embedding the data capture system into the music production workflow, we are able to maximise the return of semantically annotated music production data, whilst mit- igating against issues such as musical and environmental bias. Users of the plugins are free to submit semantic de- scriptions of their own music, whilst utilising the continually growing collaborative dataset of musical descriptors. In order to provide more contextually representative timbral transformations, the dataset is partitioned using metadata, captured within the application

Pairing Fluctuations Determine Low Energy Electronic Spectra in Cuprate Superconductors

We describe here a minimal theory of tight binding electrons moving on the square planar Cu lattice of the hole-doped cuprates and mixed quantum mechanically with pairs of them (Cooper pairs). Superconductivity occurring at the transition temperature T_c is the long-range, d-wave symmetry phase coherence of these Cooper pairs. Fluctuations necessarily associated with incipient long-range superconducting order have a generic large distance behaviour near T_c. We calculate the spectral density of electrons coupled to such Cooper pair fluctuations and show that features observed in Angle Resolved Photo Emission Spectroscopy (ARPES) experiments on different cuprates above T_c as a function of doping and temperature emerge naturally in this description. These include `Fermi arcs' with temperature-dependent length and an antinodal pseudogap which fills up linearly as the temperature increases towards the pseudogap temperature. Our results agree quantitatively with experiment. Below T_c, the effects of nonzero superfluid density and thermal fluctuations are calculated and compared successfully with some recent ARPES experiments, especially the observed `bending' or deviation of the superconducting gap from the canonical d-wave form.Comment: 14 pages, 8 figures (to appear in Phys. Rev. B

Learners restrict their linguistic generalizations using preemption but not entrenchment: evidence from artificial language learning studies with adults and children

A central goal of research into language acquisition is explaining how, when learners generalize to new cases, they appropriately RESTRICT their generalizations (e.g., to avoid producing ungrammatical utterance such as *The clown laughed the man). The past 30 years have seen an unresolved debate between STATISTICAL PREEMPTION and ENTRENCHMENT as explanations. Under preemption, the use of a verb in a particular construction (e.g., *The clown laughed the man) is probabilistically blocked by hearing that verb other constructions WITH SIMILAR MEANINGS ONLY (e.g., The clown made the man laugh). Under entrenchment, such errors (e.g., *The clown laughed the man) are probabilistically blocked by hearing ANY utterance that includes the relevant verb (e.g., by The clown made the man laugh AND The man laughed). Across five artificial-language-learning studies, we designed a training regime such that learners received evidence for the (by the relevant hypothesis) ungrammaticality of a particular unattested verb/noun+particle combination (e.g., *chila+kem; *squeako+kem) via either preemption only or entrenchment only. Across all five studies, participants in the preemption condition (as per our preregistered prediction) rated unattested verb/noun+particle combinations as less acceptable for restricted verbs/nouns, which appeared during training, than for unrestricted, novel-at-test verbs/nouns, which did not appear during training; i.e., strong evidence for preemption. Participants in the entrenchment condition showed no evidence for such an effect (and in 3/5 experiments, positive evidence for the null). We conclude that a successful model of learning linguistic restrictions must instantiate competition between different forms only where they express the same (or similar) meanings

SU(N) quantum spin models: A variational wavefunction study

The study of SU(N) quantum spin models is relevant to a variety of physical systems including ultracold atoms in optical lattices, and also leads to insights into novel quantum phases and phase transitions of SU(2) spin models. We use Gutzwiller projected fermionic variational wavefunctions to explore the phase diagram and correlation functions of SU(N) spin models in the self-conjugate representation, with Heisenberg bilinear and biquadratic interactions. In 1D, the variational phase diagram of the SU(4) spin chain is constructed by examining instabilities of the Gutzwiller projected free fermion ground state to various broken symmetries, and it agrees well with exact results.The spin and dimer correlations of the Gutzwiller projected free fermion state with N flavors of fermions are also in good agreement with exact and 1/N calculations for the critical points of SU(N) spin chains. In 2D, the variational phase diagram on the square lattice is obtained by studying instabilities of the Gutzwiller projected pi-flux state. The variational ground state of the pure Heisenberg model is found to exhibit long range Neel order for N=2,4 and spin Peierls order for N > 4. For N=4 and 6, biquadratic interactions lead to a complex phase diagram which includes an extended valence bond crystal in both cases, as well as a stable pi-flux phase for N=6. The spin correlations of the projected pi-flux state at N=4 are in good agreement with 1/N calculations. We find that this state also shows strongly enhanced dimer correlations, in qualitative accord with the large-N results. We compare our results with a recent QMC study of the SU(4) Heisenberg model.Comment: 22 pages, 7 figs, added references to arxiv versio

An Exploratory Study on Perceptual Spaces of the Singing Voice

Sixty participants provided dissimilarity ratings between various singing techniques. Multidimensional scaling, class averaging and clustering techniques were used to analyse timbral spaces and how they change between different singers, genders and registers. Clustering analysis showed that ground-truth similarity and silhouette scores that were not significantly different between gender or register conditions, while similarity scores were positively correlated with participants’ instrumental abilities and task comprehension. Participant feedback showed how a revised study design might mitigate noise in our data, leading to more detailed statistical results. Timbre maps and class distance analysis showed us which singing techniques remained similar to one another across gender and register conditions. This research provides insight into how the timbre space of singing changes under different conditions, highlights the subjectivity of perception between participants, and provides generalised timbre maps for regularisation in machine learnin