Slow Feature Oscillation Enhances Reservoir Computing for Learning Long-Period Patterns

Yuji Kawai, Shinya Fujii, Minoru Asada

Abstract: Reservoir computing (RC) is a powerful framework for learning and generating time-series data, including musical rhythmic patterns. However, its ability to capture and reproduce long-period rhythmic patterns remains limited. In this study, we address this limitation by leveraging slow feature analysis (SFA) to extract a slowly varying oscillatory feature that captures the characteristics of long-period patterns, specifically the periods of the target rhythms. We propose a novel approach that integrates the slow feature oscillation as an input to the RC model, thereby enhancing the learning and generation of long-period rhythmic patterns. The effectiveness of the proposed method is demonstrated through tasks involving synthetic time-series data containing simple long-period rhythmic patterns. The proposed method outperforms conventional RC in terms of rhythm reproduction and its fidelity. Furthermore, an experiment involving a human hi-hat drumming performance shows that the proposed method significantly improves the long-period rhythmic characteristics in the generated time-series data. These findings highlight the potential of combining SFA with RC to advance the modeling and generation of complex temporal patterns.