Music Information Retrieval in Live Coding: A Theoretical Framework

The work presented in this article has been partly conducted while the first author was at Georgia Tech from 2015–2017 with the support of the School of Music, the Center for Music Technology and Women in Music Tech at Georgia Tech. Another part of this research has been conducted while the first author was at Queen Mary University of London from 2017–2019 with the support of the AudioCommons project, funded by the European Commission through the Horizon 2020 programme, research and innovation grant 688382. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Music information retrieval (MIR) has a great potential in musical live coding because it can help the musician–programmer to make musical decisions based on audio content analysis and explore new sonorities by means of MIR techniques. The use of real-time MIR techniques can be computationally demanding and thus they have been rarely used in live coding; when they have been used, it has been with a focus on low-level feature extraction. This article surveys and discusses the potential of MIR applied to live coding at a higher musical level. We propose a conceptual framework of three categories: (1) audio repurposing, (2) audio rewiring, and (3) audio remixing. We explored the three categories in live performance through an application programming interface library written in SuperCollider, MIRLC. We found that it is still a technical challenge to use high-level features in real time, yet using rhythmic and tonal properties (midlevel features) in combination with text-based information (e.g., tags) helps to achieve a closer perceptual level centered on pitch and rhythm when using MIR in live coding. We discuss challenges and future directions of utilizing MIR approaches in the computer music field

Learning and generalization in radial basis function networks

The aim of supervised learning is to approximate an unknown target function by adjusting the parameters of a learning model in response to possibly noisy examples generated by the target function. The performance of the learning model at this task can be quantified by examining its generalization ability. Initially the concept of generalization is reviewed, and various methods of measuring it, such as generalization error, prediction error, PAC learning and the evidence, are discussed and the relations between them examined. Some of these relations are dependent on the architecture of the learning model.Two architectures are prevalent in practical supervised learning: the multi -layer perceptron (MLP) and the radial basis function network (RBF). While the RBF has previously been examined from a worst -case perspective, this gives little insight into the performance and phenomena that can be expected in the typical case. This thesis focusses on the properties of learning and generalization that can be expected on average in the RBF.There are two methods in use for training the RBF. The basis functions can be fixed in advance, utilising an unsupervised learning algorithm, or can adapt during the training process. For the case in which the basis functions are fixed, the typical generalization error given a data set of particular size is calculated by employing the Bayesian framework. The effects of noisy data and regularization are examined, the optimal settings of the parameters that control the learning process are calculated, and the consequences of a mismatch between the learning model and the data -generating mechanism are demonstrated.The second case, in which the basis functions are adapted, is studied utilising the on -line learning paradigm. The average evolution of generalization error is calculated in a manner which allows the phenomena of the learning process, such as the specialization of the basis functions, to be eludicated. The three most important stages of training: the symmetric phase, the symmetry- breaking phase and the convergence phase, are analyzed in detail; the convergence phase analysis allows the derivation of maximal and optimal learning rates. Noise on both the inputs and outputs of the data -generating mechanism is introduced, and the consequences examined. Regularization via weight decay is also studied, as are the effects of the learning model being poorly matched to the data generator

Dynamics of on-line learning in radial basis function networks

On-line learning is examined for the radial basis function network, an important and practical type of neural network. The evolution of generalization error is calculated within a framework which allows the phenomena of the learning process, such as the specialization of the hidden units, to be analyzed. The distinct stages of training are elucidated, and the role of the learning rate described. The three most important stages of training, the symmetric phase, the symmetry-breaking phase, and the convergence phase, are analyzed in detail; the convergence phase analysis allows derivation of maximal and optimal learning rates. As well as finding the evolution of the mean system parameters, the variances of these parameters are derived and shown to be typically small. Finally, the analytic results are strongly confirmed by simulations

Transitioning Between Audience and Performer: Co-Designing Interactive Music Performances with Children

Live interactions have the potential to meaningfully engage audiences during musical performances, and modern technologies promise unique ways to facilitate these interactions. This work presents findings from three co-design sessions with children that investigated how audiences might want to interact with live music performances, including design considerations and opportunities. Findings from these sessions also formed a Spectrum of Audience Interactivity in live musical performances, outlining ways to encourage interactivity in music performances from the child perspective

The effects of shade on primocane fruiting blackberries in the field

Primocane fruiting blackberry production in Arkansas is limited by heat during the flowering and early fruiting season. Shade could be used to delay flowering and fruiting to more favorable growth period. This study was designed to test three levels of shade (0% [control], 30% and 50% shading) applied at three times during the growing season that examined the growth, development, physiology of flowering, and fruiting of ‘Prime-Ark® 45’ blackberries. The seven treatments were as follows: 1) an untreated control (CK), 2) early shade 30% (ES30), mid shade 30% (MS30), 4) late shade 30% (LS30), 5) early shade 50% (ES50), 6) mid shade 50% (MS50), and 7) late shade 50% (LS50). The 30% and 50% treatments were implemented 16 June (ES) and left on for 95 days, 1 July (MS) and left on for 80 days, and 15 July (LS) and left on for 66 days. All shade was removed 19 Sept. 2014. Foliar gas exchange using CIRAS®-3 portable gas exchange monitor and estimated chlorophyll content (Minolta SPAD®) were measured weekly. Beginning at maturity, fruit was harvested biweekly to determine fruit yields per plot. Plant growth was measured destructively at the end of the study period. The cumulative berry weight was greatest for LS50 and LS30 which was not different from the CK or MS50, while ES30, MS30, and ES50 berry weights were significantly less. The cumulative marketable weights were greatest for LS30 and CK, while ES30 and MS30 were less than the CK. Shade altered flower and fruit production, but was not found to result in higher fruit quantities compared to the control. Some ES treatments reduced cropping compared to LS treatments

Length Matters: Message Metrics that Result in Higher Levels of Perceived Partner Responsiveness and Changes in Intimacy as Friends Communicate through Social Network Sites

This study focuses on how young adults enact their relationships in public through self-disclosing interactions on Facebook.  A Facebook self-disclosure status update, along with as many as three corresponding response comments, was copied by each of 271 participants from their own Facebook Wall, and pasted to an online survey.  Status update and response comments contain characters such as letters, numbers, and symbols to express meaning.  Seven textual measures were used to quantify the content of these messages; one such measure was a count of the number of characters contained in each response.  Results show message length is associated with perceived partner responsiveness and feelings of increased intimacy with those who reply to one’s status update with a response comment.  Women, and close friends and family post longer messages.  The outward appearance of a message matters for the perception of responsive communication on Facebook

On-line learning in radial basis functions networks

An analytic investigation of the average case learning and generalization properties of Radial Basis Function Networks (RBFs) is presented, utilising on-line gradient descent as the learning rule. The analytic method employed allows both the calculation of generalization error and the examination of the internal dynamics of the network. The generalization error and internal dynamics are then used to examine the role of the learning rate and the specialization of the hidden units, which gives insight into decreasing the time required for training. The realizable and over-realizable cases are studied in detail; the phase of learning in which the hidden units are unspecialized (symmetric phase) and the phase in which asymptotic convergence occurs are analyzed, and their typical properties found. Finally, simulations are performed which strongly confirm the analytic results

An interactive, graphical coding environment for EarSketch online using Blockly and Web Audio API

Presented at the 2nd Web Audio Conference (WAC), April 4-6, 2016, Atlanta, Georgia.This paper presents an interactive graphical programming environment for EarSketch, using Blockly and Web Audio API. This visual programming element sidesteps syntac- tical challenges common to learning text-based languages, thereby targeting a wider range of users in both informal and academic settings. The implementation allows seamless integration with the existing EarSketch web environment, saving block-based code to the cloud as well as exporting it to Python and JavaScript

Loop-aware Audio Recording for the Web

Music loops are audio recordings used as basic building blocks in many types of music. The use of pre-recorded loops facilitates engagement into music creation to users regardless of their background in music theory. Using online loop databases also affords simple collaboration and exchange. Hence, music loops are particularly attractive for web audio applications. However, traditional musical audio recording typically relies on complex DAW software. Recording loops usually requires consideration of musical meter and tempo, and withstanding metronome sounds. In this paper, we propose loop-aware audio recording as a use case for web audio technologies. Our approach supports hands-free, low-stress recording of music loops in web- enabled devices. The system is able to detect repetitions in an incoming audio stream. Based on this information, it segments and ranks the repeated fragments, presenting the list to the user. We provide an example implementation, and evaluate the use of the different MIR libraries available in the web audio platform for the proposed task

Three Studies Exploring Genetic and Social Influences on the Association Between Religiosity and Substance Use Behaviors

Research exploring the relationship between religiosity and substance use behaviors typically find a significant inverse association between those two phenomena. One implicit assumption of these studies is that the relationship between religiosity and substance use behaviors functions in a similar way for all individuals within a population. However, some research has shown that the association between religiosity and substance use behaviors may differ between certain sub-groups within a population. In this dissertation I explore three factors that may lead to variation in the association of religiosity and substance use. I explore variation across family status (i.e. marital and parental status); variation across the life course (between adolescence and early adulthood) and variation due to genetic factors. I find that 1) the association between religiosity and smoking is stronger among married parents compared to other groups; 2) the association between religiosity and alcohol use increases between adolescence and early adulthood; and 3) there is genetic component of the covariance of religiosity and substance use behaviors that increases between adolescence and early adulthood. My findings reveal the need for researchers exploring the association between religiosity and substance use to take into account potential variation in that relationship.Doctor of Philosoph