Using Mutual Proximity to Improve Content-Based Audio Similarity .
Sprache des Titels:
Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), Miami, Florida.
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
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.