arXiv subjects

Çağrı Erdem

Publications and source records attributed to Çağrı Erdem.

3 recordsLinked to original sources

arXiv2026

Quality-Diversity Search in Sound Generation: Investigating Innovation Engines for Audio Exploration

This study addresses the challenges composers and sound designers face in creating and refining tools to achieve their musical goals. Using evolutionary processes to promote diversity and foster serendipitous discoveries, we automate the search through uncharted sonic spaces for sound discovery, arguing that diversity-promoting algorithms can bridge the gap between the theoretical realisation and practical accessibility of sounds. We describe a system for generative sound synthesis combining Quality Diversity (QD) algorithms with a supervised discriminative model, inspired by the Innovation Engine algorithm, and explore different configurations and the interplay between the chosen synthesis approach and the discriminative model. We examine the interaction between Compositional Pattern Producing Networks (CPPNs) and Digital Signal Processing (DSP) graphs, introducing a novel approach that uses multiple specialised CPPNs for different frequency ranges; this yields simpler networks while maintaining performance comparable to single-CPPN setups. We also investigate evolutionary stepping stones by analysing goal switches between musical and non-musical contexts, revealing how lineages traverse unlikely paths to current elites. Expanding the behaviour space of a previous study to include various sound durations, we uncover specialisation within temporal niches. Results indicate that CPPN and DSP graphs coupled with a Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) and a deep learning classifier can generate a substantial variety of synthetic sounds, diverse and innovative across temporal and contextual dimensions. We present the generated sound objects through an online explorer and as rendered sound files, and, in the context of music composition, an experimental application that showcases their creative potential across various durations and contexts.

cs.SD↗

arXiv2026

Human-Like Trajectories Generation via Receding Horizon Tracking Applied to the TickTacking Interface

TickTacking is a rhythm-based interface that allows users to control a pointer in a two-dimensional space through dual-button tapping. This paper investigates the generation of human-like trajectories using a receding horizon approach applied to the TickTacking interface in a target-tracking task. By analyzing user-generated trajectories, we identify key human behavioral features and incorporate them in a controller that mimics these behaviors. The performance of this human-inspired controller is evaluated against a baseline optimal-control-based agent, demonstrating the importance of specific control features for achieving human-like interaction. These findings contribute to the broader goal of developing rhythm-based human-machine interfaces by offering design insights that enhance user performance, improve intuitiveness, and reduce interaction frustration

cs.HC↗

arXiv2025

Exploring Definitions of Quality and Diversity in Sonic Measurement Spaces

Digital sound synthesis presents the opportunity to explore vast parameter spaces containing millions of configurations. Quality diversity (QD) evolutionary algorithms offer a promising approach to harness this potential, yet their success hinges on appropriate sonic feature representations. Existing QD methods predominantly employ handcrafted descriptors or supervised classifiers, potentially introducing unintended exploration biases and constraining discovery to familiar sonic regions. This work investigates unsupervised dimensionality reduction methods for automatically defining and dynamically reconfiguring sonic behaviour spaces during QD search. We apply Principal Component Analysis (PCA) and autoencoders to project high-dimensional audio features onto structured grids for MAP-Elites, implementing dynamic reconfiguration through model retraining at regular intervals. Comparison across two experimental scenarios shows that automatic approaches achieve significantly greater diversity than handcrafted behaviour spaces while avoiding expert-imposed biases. Dynamic behaviour-space reconfiguration maintains evolutionary pressure and prevents stagnation, with PCA proving most effective among the dimensionality reduction techniques. These results contribute to automated sonic discovery systems capable of exploring vast parameter spaces without manual intervention or supervised training constraints.

cs.SD↗