@article{
 title = {Drum Stroke Computing : Multimodal Signal Processing for Drum Stroke Identification and Performance Metrics},
 type = {article},
 year = {2012},
 keywords = {drum stroke identification,machine learning,multimodality,music information,performance metrics,retrieval,surrogate data training,surrogate sensors},
 pages = {365-369},
 id = {0efef1b9-3b4b-3e91-be26-753d0793ce4d},
 created = {2016-11-17T19:13:45.000Z},
 file_attached = {true},
 profile_id = {bca0fddf-79ea-3c29-93ed-6177ce521efd},
 group_id = {e79131d5-b618-3b3c-ae97-e4263040fd28},
 last_modified = {2017-03-14T16:56:30.626Z},
 read = {true},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {In this paper we present a multimodal system for analyzing drum performance. In the first example we perform automatic drum hand recognition utilizing a technique for automatic labeling of training data using direct sensors, and only indirect sensors (e.g. a microphone) for testing. Left/Right drum hand recognition is achieved with an average accuracy of 84.95% for two performers. Secondly we provide a study investigating multimodality dependent performance metrics analysis.},
 bibtype = {article},
 author = {Hochenbaum, Jordan and Kapur, Ajay},
 journal = {NIME 2012 Proceedings of the International Conference on New Interfaces for Musical Expression}
}

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