We present a novel music signal processing task of classifying the tuning of a harpsichord from audio recordings of standard musical works. We report the results of a classification experiment involving six different temperaments, using real harpsichord recordings as well as synthesised audio data. We introduce the concept of conservative transcription, and show that existing high-precision pitch estimation techniques are sufficient for our task if combined with conservative transcription. In particular, using the CQIFFT algorithm with conservative transcription and removal of short duration notes, we are able to distinguish between 6 different temperaments of harpsichord recordings with 96% accuracy (100% for synthetic data).

We present an architecture for content-based aggregation in peer-to-peer filesharing networks. It is designed to significantly reduce the amount of nodes that have to be queried, by introducing a distributed index into the system. This index allows content to be located in a peer-to-peer network without using broadcast-style techniques. We also show how this index can be created and maintained in a decentralized self-organizing fashion.