Goldsmiths Ph.D. student Michaela Magas has been busy with user interaction design and publicity for AudioDB. First she made a web site showing the prototype interfaces she has developed: mHashup site.
Then there are a couple of nice news articles (including video) of audioDB’s mhashup front end:
BBC News article comparing Pandora, Melodis, Shazam and mHashup. And [...]
The Problem with Music: Modeling Distance Distributions of Large Music Collection.
Talk at Dartmouth Computer Science Colloquium
Wednesday 21st Januray 2009, Silsby Room 028 (*note location change*), Dartmouth College
Talk Slides
Abstract: Recently, a number of piano recordings by different artists were found in a classical music catalog that exhibited a striking resemblance to each other. Could this [...]
This talk describes new approximate nearest-neighbor methods employed in a scalable audio-feature database system called “AudioDB.” This open-source system is designed to scale to storing and searching hundreds of millions of feature vectors on standard UNIX workstation platforms. A radius-bounded nearest-neighbor vector-sequence search algorithm, based on locality sensitive hashing LSH , achieves sublinear retrieval times at this scale. The performance of the LSH-based algorithm depends critically on the choice of radius bound supplied—the wrong value impacts retrieval accuracy or retrieval time. An optimal radius estimator is derived by modeling the minimum value distribution of a random sample of a data set’s pairwise distance distribution
