The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation

With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progress on image and short sequence generation, symbolic music generation remains a challenging problem since the structure of compositions are usually complicated. In this study, we attempt to solve the melody generation problem constrained by the given chord progression. This music meta-creation problem can also be incorporated into a plan recognition system with user inputs and predictive structural outputs. In particular, we explore the effect of explicit architectural encoding of musical structure via comparing two sequential generative models: LSTM (a type of RNN) and WaveNet (dilated temporal-CNN). As far as we know, this is the first study of applying WaveNet to symbolic music generation, as well as the first systematic comparison between temporal-CNN and RNN for music generation. We conduct a survey for evaluation in our generations and implemented Variable Markov Oracle in music pattern discovery. Experimental results show that to encode structure more explicitly using a stack of dilated convolution layers improved the performance significantly, and a global encoding of underlying chord progression into the generation procedure gains even more.Comment: 8 pages, 13 figure

Artificial Intelligence Music Generators in Real Time Jazz Improvisation: a performer’s view

Μια αμφιλεγόμενη είσοδος γεννητριών μουσικής τεχνητής νοημοσύνης στον κόσμο της μουσικής σύνθεσης και ερμηνείας καλπάζει επί του παρόντος. Γόνιμη έρευνα που πηγάζει απο τομείς όπως η ανάκτηση πληροφοριών μουσικής, τα νευρονικά δίκτυα και η βαθιά μάθηση, μεταξύ άλλων, διαμορφώνει αυτό το μέλλον. Ενσωματωμένα και μη ενσωματωμένα συστήματα τεχνητής νοημοσύνης έχουν εισέλθει στον κόσμο της τζαζ προκειμένου να συνδημιουργήσουν ιδιωματικούς μουσικούς αυτοσχεδιασμούς. Αυτή η διπλωματική εξετάζει τους προκύπτοντες μελωδικούς αυτοσχεδιασμούς που παράγονται από τις γεννήτριες OMax, ImproteK και Djazz (OID) μέσω του φακού των στοιχείων της μουσικής και το κάνει από την άποψη ενός ερμηνευτή. Η ανάλυση βασίζεται κυρίως στην αξιολόγηση των ήδη δημοσιευμένων αποτελεσμάτων, καθώς και σε μια μελέτη περίπτωσης που πραγματοποίηθηκε κατά την ολοκλήρωση αυτής της εργασίας που περιλαμβάνει την απόδοση, την ακρόαση και την αξιολόγηση των παραγόμενων αυτοσχεδιασμών του OMax. Επίσης, η εργασία ασχολείται με φιλοσοφικά ζητήματα, με τα γνωστικά θεμέλια του συναισθήματος και του νοήματος και παρέχει μια ολοκληρωμένη ανάλυση της λειτουργικότητας του OID.A highly controversial entrance of Artificial Intelligence (AI) music generators in the world of music composition and performance is currently advancing. A fruitful research from Music Information Retrieval, Neural Networks and Deep Learning, among other areas, are shaping this future. Embodied and non-embodied AI systems have stepped into the world of jazz in order to co-create idiomatic music improvisations. But how musical these improvisations are? This dissertation looks at the resulted melodic improvisations produced by OMax, ImproteK and Djazz (OID) AI generators through the lens of the elements of music and it does so from a performer’s point of view. The analysis is based mainly on the evaluation of already published results as well as on a case study I carried out during the completion of this essay which includes performance, listening and evaluation of generated improvisations of OMax. The essay also reflects upon philosophical issues, cognitive foundations of emotion and meaning and provides a comprehensive analysis of the functionality of OID

A conceptual system design and managerial complexity competency model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Complex adaptive systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex adaptive systems. The challenges of designing complex adaptive systems in a highly dynamic world drive the need for anticipatory capacity within engineering organizations, with a goal of enabling the design of systems that can cope with an unpredictable environment. This thesis explores this question of enhancing anticipatory capacity through the study of a complex adaptive system design methodology and complexity management competencies. A general introduction to challenges and issues in complex adaptive systems design is given, since a good understanding of the industrial context is considered necessary in order to avoid oversimplification of the problem, neglecting certain important factors and being unaware of important influences and relationships. In addition, a general introduction to complex thinking is given, since designing complex adaptive systems requires a non-classical thought, while practical notions of complexity theory and design are put forward. Building on these, the research proposes a Complex Systems Life-Cycle Understanding and Design (CXLUD) methodology to aid system architects and engineers in the design and control of complex adaptive systems. Starting from a creative anticipation construct - a loosening mechanism to allow for more options to be considered, the methodology proposes a conceptual framework and a series of stages to follow to find proper mechanisms that will promote elements to desired solutions by actively interacting among themselves. To illustrate the methodology, a financial systemic risks infrastructure systems architecture development case study is presented. The final part of this thesis develops a conceptual model to analyse managerial complexity competency model from a qualitative phenomenological study perspective. The model developed in this research is called Understanding-Perception-Action (UPA) managerial complexity competency model. The results of this competency model can be used to help ease project manager’s transition into complex adaptive projects, as well as serve as a foundation to launch qualitative and quantitative research into this area of project complexity management

新しい音楽スタイルの創出のための音楽理論生成アルゴリズム

東京藝術大学平成25年