Interactive Music Generation with Positional Constraints using Anticipation-RNNs

Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for interactive and creative usages such as interactive music generation. This paper introduces a novel architecture called Anticipation-RNN which possesses the assets of the RNN-based generative models while allowing to enforce user-defined positional constraints. We demonstrate its efficiency on the task of generating melodies satisfying positional constraints in the style of the soprano parts of the J.S. Bach chorale harmonizations. Sampling using the Anticipation-RNN is of the same order of complexity than sampling from the traditional RNN model. This fast and interactive generation of musical sequences opens ways to devise real-time systems that could be used for creative purposes.Comment: 9 pages, 7 figure

Music Recombination Using a Genetic Algorithm

This paper presents a new system, based on genetic algorithms, to compose music pieces automatically based on analysis of the exemplar MIDI files. The aim of this project is to create a new music piece which is based on the information in the source pieces. This system extracts musical features from two MIDI files and automatically generates a new music piece using a genetic algorithm. The user specifies the length of the piece to create, and the weighting of musical features from each of the MIDI files to guide the generation. This system will provide the composer a new music piece based on two selected music pieces

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Advances in Multiple Viewpoint Systems and Applications in Modelling Higher Order Musical Structure

PhDStatistical approaches are capable of underpinning strong models of musical structure, perception, and cognition. Multiple viewpoint systems are probabilistic models of sequential prediction that aim to capture the multidimensional aspects of a symbolic domain with predictions from multiple finite-context models combined in an information theoretically informed way. Information theory provides an important grounding for such models. In computational terms, information content is an empirical measure of compressibility for model evaluation, and entropy a powerful weighting system for combining predictions from multiple models. In perceptual terms, clear parallels can be drawn between information content and surprise, and entropy and certainty. In cognitive terms information theory underpins explanatory models of both musical representation and expectation. The thesis makes two broad contributions to the field of statistical modelling of music cognition: firstly, advancing the general understanding of multiple viewpoint systems, and, secondly, developing bottom-up, statistical learning methods capable of capturing higher order structure. In the first category, novel methods for predicting multiple basic attributes are empirically tested, significantly outperforming established methods, and refuting the assumption found in the literature that basic attributes are statistically independent from one another. Additionally, novel techniques for improving the prediction of derived viewpoints (viewpoints that abstract information away from whatever musical surface is under consideration) are introduced and analysed, and their relation with cognitive representations explored. Finally, the performance and suitability of an established algorithm that automatically constructs locally optimal multiple viewpoint systems is tested. In the second category, the current research brings together a number of existing statistical methods for segmentation and modelling musical surfaces with the aim of representing higher-order structure. A comprehensive review and empirical evaluation of these information theoretic segmentation methods is presented. Methods for labelling higher order segments, akin to layers of abstraction in a representation, are empirically evaluated and the cognitive implications explored. The architecture and performance of the models are assessed from cognitive and musicological perspectives.Media and Arts Technology programme, EPSRC Doctoral Training Centre EP/G03723X/1

Génération automatique de mélodie par la programmation par contraintes

La programmation par contraintes est un type de programmation déclarative, un paradigme naturellement adapté au traitement de problèmes musicaux. En effet, la composition musicale s’apparente à un processus déclaratif pendant lequel le compositeur travaille pour créer de la musique qui respecte les règles générales de l’art et les critères plus spécifiques du style adopté tout en y incorporant ses propres contraintes. Le parallèle entre cet exercice et la résolution d’un problème de satisfaction de contraintes se fait donc instinctivement. La principale difficulté se trouve au niveau de la modélisation du problème. Une pièce musicale est composée de plusieurs dimensions entre lesquelles existent beaucoup d’interactions. Il est pratiquement impossible pour un système informatique de représenter précisément toutes ces dépendances. Les systèmes de contraintes conçus pour traiter de problèmes musicaux se concentrent alors sur des dimensions en particulier. Parmi ces problèmes, on retrouve la génération de mélodie qui concerne donc les hauteurs et les durées des notes d’une ligne mélodique accompagnée par une suite d’accords. La modélisation d’un tel problème se concentre sur une séquence de notes et ne présente donc aucun élément de polyphonie ou d’instrumentation par exemple, ce qui simplifie la situation. L’objectif de ce projet est de concevoir un système de génération automatique de mélodie selon une suite d’accords donnée qui utilise les informations d’un corpus pour guider la composition. Deux des principaux défis de ce type de problème sont l’organisation des variables et le contrôle de la structure globale de la mélodie générée. Pour relever le premier, nous avons émis l’hypothèse qu’un système structuré hiérarchiquement offrait le plus de flexibilité et permettrait donc d’exprimer les contraintes plus facilement. En ce qui concerne la structure du résultat, nous avons mis au point un algorithme de détection de patrons répétitifs basé sur des arbres des suffixes qui permet au système de répliquer les éléments de la structure d’une mélodie existante.----------ABSTRACT: Constraint programming belongs to the declarative programming paradigm which is naturally suited to tackle musical problems. Musical composition can be seen as a declarative process during which the composer works to create music respecting the general and specific rules of the chosen style and also adds his own touch. The connection between this process and resolving a constraint satisfaction problem is made instinctively. The main challenge of this field is modeling the problem because of all the different dimensions which interact together in a music piece. It is virtually impossible for a computer-based system to provide a view of the same quality a human composer would have. Thus, constraint systems designed to tackle musical problems usually focus on specific dimensions. One of these problems consists of generating a melody given a chord sequence, which only involves note durations and pitches, there is no concept of polyphony or instrumentation, for example. The goal of this project is to design and implement a system able to generate a melody given a chord sequence, using information from a corpus to guide composition. Two of the main challenges of this kind of problems are the variables arrangement and the control of the global structure of the melody. Regarding variables, we made the assumption that a hierarchical organization would improve the system’s flexibility which would make it easier to express constraints. For the structure, we designed an algorithm which uses suffix trees to detect repeating patterns in existing melodies and made the system able to replicate them in the result. Our system is made of hierarchically organized blocs. The melody is made of bars which contain chords under which are located the notes. Each block has a variable number of notes which needs to be fixed first in order to instantiate the corresponding variables. This means that the system has to work in two phases. The first one assigns a rhythm pattern to every bar, which decides both the number of notes and their durations. The second phase fixes the pitch of every note of the melody

Reconocimiento de patrones rítmicos en señales de audio

En este trabajo se presenta una metodología para el reconocimiento automático de patrones rítmicos en señales de audio, usando cadenas ocultas de Markov como herramienta de clasificación. Los experimentos reportados se concentran en el ritmo del candombe, en particular en los patrones rítmicos de los tambores repique y piano. En el caso del repique, se busca identificar en el audio algunos patrones rítmicos, propuestos por Luis Jure en su trabajo “Principios generativos del toque de repique del candombe”. La implementación de la metodología utiliza audio sintético para el entrenamiento de las cadenas ocultas, y los resultados obtenidos en el reconocimiento son muy buenos si el audio que se quiere clasificar es también sintético, obteniendo más del 90 % de acierto en la clasificación. Si se usa audio sintético para entrenar y grabaciones reales para clasificar, el desempeño cae drásticamente, siendo menor a 10 % en las pruebas realizadas. Se discuten algunas alternativas para mejorar la clasificación en ese caso, una de las cuales es implementada. Aún así, la clasificación de audios reales no mejora demasiado, resultando apenas superior al 10 %. Para el tambor piano, el problema es identificar en el audio qué compases se corresponden con su patrón más típico (referido usualmente como base de piano) y cuáles no (lo que se conoce como piano repicado). En este caso, tanto el entrenamiento como la evaluación de desempeño se realizan con grabaciones reales, y en ese caso se logra un buen porcentaje en la clasificación (superior al 80 % en todas las pruebas realizadas)

The Music Sound

A guide for music: compositions, events, forms, genres, groups, history, industry, instruments, language, live music, musicians, songs, musicology, techniques, terminology , theory, music video. Music is a human activity which involves structured and audible sounds, which is used for artistic or aesthetic, entertainment, or ceremonial purposes. The traditional or classical European aspects of music often listed are those elements given primacy in European-influenced classical music: melody, harmony, rhythm, tone color/timbre, and form. A more comprehensive list is given by stating the aspects of sound: pitch, timbre, loudness, and duration. Common terms used to discuss particular pieces include melody, which is a succession of notes heard as some sort of unit; chord, which is a simultaneity of notes heard as some sort of unit; chord progression, which is a succession of chords (simultaneity succession); harmony, which is the relationship between two or more pitches; counterpoint, which is the simultaneity and organization of different melodies; and rhythm, which is the organization of the durational aspects of music