Means of Integrating Audio Content Analysis Algorithms

Two generic mechanisms are proposed that facilitate the efficient integration of audio content analysis algorithms. The first mechanism, priorityrule based interleaving of algorithms, allows the simultaneous interoperation of several bottom-up analysis modules by interleaving their atomic steps. It aims at increased accuracy through controlled manipulation of common data. The second mechanism, top-down routing of requests for data, allows high-level predictions to direct the bottom-up analysis towards verifying the predicted hypotheses by observations. Examples from automatic music transcription are presented to clarify the use of the proposed methods.