Sound Similarity as a Tool for Understanding Player Experience: Applying Similarity Matrix to Gameplay Performance Segmentation

Similarity matrices offer a robust, non-invasive framework for analyzing player experience by converting gameplay audio streams into chroma representations. By mapping these sequences, researchers can visualize player behavior, identify recurring strategies, and assess progression patterns without needing access to underlying game source code. This methodology serves as a post-processing tool that empirically measures how individual gameplay sessions align with or deviate from a game’s intended linear structure, providing a scalable way to segment performance across diverse titles.

The application of auto-similarity matrices proves particularly effective at distinguishing between varying skill levels and identifying specific behavioral markers. Diagonals within these matrices reveal clear patterns, such as repetitive death sequences or distinct strategic approaches, allowing for the differentiation between novice and expert performance. Case studies involving titles such as Max Payne 3, Battlefield 3, and Super Hexagon demonstrate the versatility of this approach across different genres, confirming its utility in mapping the nuances of player engagement and progression.

This analytical framework provides a valuable alternative to traditional telemetry-based methods, offering a neutral lens through which to evaluate player interaction. By focusing on audio-derived data, the approach maintains a high degree of accessibility while remaining compatible with future enhancements, such as the integration of biometric data or automated image processing. Ultimately, this technique establishes a foundation for more sophisticated, data-driven insights into how players navigate complex virtual environments, facilitating a deeper understanding of the relationship between game design and user behavior.