TOWARDS A FUZZY LOGIC APPROACH TO DRUM PATTERN HUMANISATION

2014

In the present work, the Fuzzy Inductive Reasoning methodology (FIR) is used to improve coherence among beat patterns, structured in a musical A-B form. Patterns were generated based on a probability matrix, encoding a particular musical style, designed by experts. Then, all possible patterns were generated and the most probables were selected. A-B musical forms were created and the coherence of the sequence was evaluated by experts by using linguistic quantities. The output pairs (A-B pattern and its qualification) were used as inputs to train a FIR system, and the variables that produce “coherent” outputs and the relations among them where identified as rules. The extracted rules are discussed in the context of the musical form and from the psychological perception.

2019

In order to collaborate and co-create with humans, an AI system must be capable of both reactive and anticipatory behavior. We present a case study of such a system in the domain of musical improvisation. We consider a duo consisting of a human pianist accompained by an off-the-shelf virtual drummer, and we design an AI system to control the perfomance parameters of the drummer (e.g., patterns, intensity, or complexity) as a function of what the human pianist is playing. The AI system utilizes a model elicited from the musicians and encoded through fuzzy logic. This paper outlines the methodology, design, and development process of this system. An evaluation in public concerts is upcoming. This case study is seen as a step in the broader investigation of anticipation and creative processes in mixed human-robot, or "anthrobotic" systems.

2004

The modeling of the behavior of human process operators using fuzzy methods is well established in technical applications. We investigate whether these fuzzy strategies can also be successfully applied for modeling the manner in which a musician interprets a piece of music. For L. v. Beethoven’s composition “Fur Elise” we set up a base of 150 fuzzy rules that relate to features laid down explicitly or implicitly in the score, and produce situation-dependent variations of the volume and time points of the start and end of the notes. For sensitive processing of these rules advanced fuzzy-strategies are applied. The interpretation obtained is encouraging. We are aware that human artistry in interpreting music is much more sophisticated than what we can presently imitate. However, we consider it as a challenge, not an a priori fruitless question, to determine to what extent it is possible to automate the interpretation of music by fuzzy methods. At the same time, we consider the problem...

2005

We describe a real-time musical agent that generates an audio drum-track by concatenating audio segments automatically extracted from pre-existing musical files. The drum-track can be controlled in real-time by specifying high-level properties (or constraints) holding on metadata automatically extracted from the audio segments. A constraint-satisfaction mechanism, based on local search, selects audio segments that best match those constraints at any time. We report on several drum track audio descriptors designed for the system. We also describe a basic mecanism for controlling the tradeoff between the agent's autonomy and reactivity, which we illustrate with experiments made in the context of a virtual duet between the system and a human pianist.

Sonic Ideas / Ideas Sonicas, 2011

The percussion composition Signals Intelligence (2002), in solo and ensemble versions, is a creative musical realization of mathematical self-similar sequences. This paper discusses how the mathematics underlying the composition was motivated by musical concerns, and how composition proceeded as a dialogue between intuitive creativity and the application of algorithmic processes.

Applications of Evolutionary Computing, 2008

SMC Conference Proceedings, 2020

This paper presents a set of real-time modules that digitally enhance the performance of a drummer. The modules extract rhythmic information from the multichannel audio acquired using simple microphones onto the different drum parts. Based on the predicted tempo, the modules generate complex patterns that can be manually controlled through high-level parameters or can be left automatic while the system adapts to the playing condition of the drummer. Such an interactive system is intended mainly for an improvised solo performance confronting a human drummer with a computer; however, it could be effectively employed in improvisations with larger ensembles or installations.

One of the major challenges in computer music is to endow the resulting piece with the expressiveness that characterizes human performances. The main problem is to grasp the performer's "touch"; that is, the knowledge brought about when performing a score. Humans acquire it through a long process of observation and imitation. For this reason, previous approaches, based on following musical rules trying to capture interpretation knowledge, have serious limitations to achieve human-like music synthesis. An alternative approach, much closer to the observation-imitation process observed in humans, is that of directly using the interpretation knowledge implicit in examples from recordings of human performers instead of trying to make explicit such knowledge. We report on a system, SaxEx, based on this alternative approach, capable of synthesizing truly human-like performances of Jazz ballads based on examples of human performers within a case-based reasoning system. The results we have obtained clearly show that it is perfectly possible to achieve human-like music synthesis by means of artificial intelligence techniques.

2021

The development of artificial intelligence (AI) methods like machine learning (ML) and deep learning (DL) has created new opportunities and tools for many industries such as self-driving cars, voice recognition and image classification. But can this new and improved AI be used to create popular electronic music? And how? This thesis explores the possibilities of using AI to make Electronic Dance Music (EDM). Artificial Dance Music (ADM) is music created by AI and in particular, by DL, which trains on EDM music to learn from it and then recreate a symbolic representation. In this study, a Turing test is used to evaluate the generated music, with participants listening to both algorithmically-generated music and the original samples. A statistical evaluation of the Turing test results proves that there is no significant difference that distinguishes machine and human-generated EDM music. The goal is to investigate to what extent a machine can create original music and if it can be use...

Proceedings of the SMC Conferences, 2010

In this paper we present an application using an evolutionary algorithm for real-time generation of polyphonic drum loops in a particular style. The population of rhythms is derived from analysis of MIDI drum loops, which profile each style for subsequent automatic generation of rhythmic patterns that evolve over time through genetic algorithm operators and user input data.