Thursday, July 30th Paris Smaragdis presents a talk entitled: Sound Mixtures: A musician’s delight, an engineer’s nightmare!
2PM, Dartmouth College, Room: Wilson 219
Abstract:
Musicians have long embraced polyphony all the way back to the middle ages. Engineers, even today, are still struggling to come to terms with it. Traditional audio signal processing has primarily focused on [...]
Tutorial AM 1 (10:00-13:00): MIR at the Scale of the Web
by Malcolm Slaney (Yahoo! Research), and Michael Casey (Dartmouth College)
Abstract
In the last couple of years we have received access to music databases with millions of songs. This massive change in the amount of data available to researchers is changing the face of Music Information Retrieval. [...]
Talk by Prof. Michael Casey
To be presented Friday December 5th, 2008
New York University, NYC
Abstract
Soundspotting is a new approach to creating musical streams by
selecting and concatenating source segments from a large audio
database using methods from music information retrieval. Sometimes
called plundermatics, audio mosaics or concatenative synthesis,
soundspotting computes a similarity score between a target audio
segment and all the [...]
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
