A Musical Audio Search Method Based on Self-Similarity Features

We propose a method for musical audio search based on signal matching. A major problem in the signal matching approach to musical audio search has been key variation; if the key of a query signal is significantly different from the one in the stored database, the search will fail. To cope with this problem, our method newly employs self-similarity as the feature for signal matching. The self-similarity proposed here is similarity of the power spectrum defined between two time points within an audio signal. We show that the method increases the robustness of musical audio search with respect to key variation. In our experimentation, for example, the proposed method yields precision and recall rates of around 0.75 even when the pitches in queries and stored signals differ from each other by seven semitones, whereas a conventional signal matching method does not produce meaningful results in such a case.