Using Mutual Proximity to Improve Content-Based Audio Similarity

This work introduces Mutual Proximity, an unsupervised method which transforms arbitrary distances to similarities computed from the shared neighborhood of two data points. This reinterpretation aims to correct inconsistencies in the original distance space, like the hub phenomenon. Hubs are objects which appear unwontedly often as nearest neighbors in predominantly high-dimensional spaces. We apply Mutual Proximity to a widely used and standard content-based audio similarity algorithm. The algorithm is known to be negatively affected by the high number of hubs it produces. We show that without a modification of the audio similarity features or inclusion of additional knowledge about the datasets, applying Mutual Proximity leads to a significant increase of retrieval quality: (1) hubs decrease and (2) the k-nearest-neighbor classification rates increase significantly. The results of this paper show that taking the mutual neighborhood of objects into account is an important aspect which should be considered for this class of content-based audio similarity algorithms.