Sound can complement vision in ball sports by providing subtle cues about contact dynamics. In table tennis, the brief, high-frequency sounds produced during racket-ball impacts carry information about the racket type, the surface contacted, and whether spin was applied. We address three key problems in this domain: (1) precise bounce detection with millisecond-level temporal accuracy, (2) classification of bounce surface (e.g., racket, table, floor), and (3) spin detection from audio alone. To this end, we propose a real-time-capable pipeline that combines energy-based peak detection with convolutional neural networks trained on a novel dataset of 3,396 bounce samples recorded across 10 racket configurations. The system achieves accurate and low-latency detection of bounces, and reliably classifies both the surface of contact and whether spin was applied. This audio-based approach opens up new possibilities for spin estimation in robotic systems and for real-time feedback in coaching tools. We publicly release both the dataset and code to support further research.