@conference {3834,
	title = {More Cowbell: a Physically-Informed, Circuit-Bendable, Digital Model of the TR-808 Cowbell},
	booktitle = {137th International Audio Engineering Society (AES) Conference},
	year = {2014},
	note = {\&nbsp;},
	month = {10/2014},
	address = {Los Angeles, CA},
	abstract = {\&nbsp;},
	keywords = {cowbell, drum machine, physical modeling, TR-808, virtual analog},
	url = {http://www.aes.org/e-lib/browse.cfm?elib=17530},
	author = {Kurt James Werner and Abel, Jonathan S and Julius O. Smith}
}
@conference {3832,
	title = {A Physically-Informed, Circuit-Bendable, Digital Model of the Roland TR-808 Bass Drum Circuit},
	booktitle = {17th International Conference on Digital Audio Effects (DAFx-14)},
	year = {2014},
	note = {\&nbsp;},
	month = {09/2014},
	address = {Erlangen, Germany},
	abstract = {\&nbsp;},
	keywords = {bass drum, drum machine, physical modeling, TR-808, virtual analog},
	url = {http://www.dafx14.fau.de/papers/dafx14_kurt_james_werner_a_physically_informed,_ci.pdf},
	author = {Kurt James Werner and Abel, Jonathan S and Julius O. Smith}
}
@conference {3833,
	title = {The TR-808 Cymbal: a Physically-Informed, Circuit-Bendable, Digital Model},
	booktitle = {40th International Computer Music Conference (ICMC) / 11th Sound and Music Computing Conference (SMC)},
	year = {2014},
	note = {\&nbsp;},
	month = {09/2014},
	address = {Athens, Greece},
	abstract = {\&nbsp;},
	keywords = {cymbal, drum machine, physical modeling, TR-808, virtual analog},
	url = {http://icmc14-smc14.net/images/proceedings/OS24-B10-TheTR-808Cymbal.pdf},
	author = {Kurt James Werner and Abel, Jonathan S and Julius O. Smith}
}
@conference {3753,
	title = {ISSE: An Interactive Source Separation Editor},
	booktitle = {CHI Conference on Human Factors in Computing Systems},
	year = {2014},
	note = {\&nbsp;},
	month = {04/2014},
	publisher = {ACM},
	organization = {ACM},
	address = {Toronto, Canada},
	abstract = {\&nbsp;},
	author = {Nicholas Bryan and Mysore, Gautham J. and Ge Wang}
}
@mastersthesis {3754,
	title = {Interactive Sound Source Separation},
	volume = {PhD},
	year = {2014},
	note = {\&nbsp;},
	month = {03/2014},
	pages = {165},
	school = {Stanford University},
	address = {Stanford},
	abstract = {\&nbsp;},
	author = {Nicholas Bryan}
}
@conference {3656,
	title = {Source Separation of Polyphonic Music  with Interactive User-Feedback on a Piano-Roll Display},
	booktitle = {International Society for Music Information Retrieval Conference},
	year = {2013},
	note = {\&nbsp;},
	month = {11/2013},
	address = {Curitiba},
	abstract = {\&nbsp;},
	keywords = {Interaction, piano-roll, source separation, user-feedack},
	author = {Nicholas Bryan and Mysore, Gautham J. and Ge Wang}
}
@conference {3614,
	title = {An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source Separation},
	booktitle = {International Conference on Machine Learning},
	year = {2013},
	note = {\&nbsp;},
	month = {06/2013},
	address = {Atlanta, GA},
	abstract = {\&nbsp;},
	author = {Nicholas Bryan and Gautham Mysore}
}
@conference {3613,
	title = {Interactive Refinement of Supervised and Semi-Supervised Sound Source Separation Estimates},
	booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing},
	year = {2013},
	note = {\&nbsp;},
	month = {05/2013},
	address = {Vancouver, Canada},
	abstract = {\&nbsp;},
	keywords = {source separation},
	author = {Nicholas Bryan and Gautham Mysore}
}
@conference {3612,
	title = {Interactive User-Feedback for Sound Source Separation},
	booktitle = {International Conference on Intelligent User-Interfaces (IUI), Workshop on Interactive Machine Learning},
	year = {2013},
	note = {\&nbsp;},
	month = {03/2013},
	address = {Santa Monica},
	abstract = {\&nbsp;},
	author = {Nicholas Bryan and Gautham Mysore}
}
@conference {3480,
	title = {Learnng Sparse Feature Representations For Music Annotation And Retrieval},
	booktitle = {13th International Society for Music Information Retrieval Conference},
	year = {2012},
	note = {\&nbsp;},
	month = {10/2012},
	address = {Porto, Portugal},
	abstract = {We present a data-processing pipeline based on sparse feature learning and describe its applications to music annotation and retrieval. Content-based music annotation and retrieval systems process audio starting with features. While commonly used features, such as MFCC, are handcrafted to extract characteristics of the audio in a succinct way, there is increasing interest in learning features automatically from data using unsupervised algorithms. We describe a systemic approach applying feature-learning algorithms to music data, in particular, focusing on a highdimensional sparse-feature representation. Our experiments show that, using only a linear classifier, the newly learned features produce results on the CAL500 dataset comparable to state-of-the-art music annotation and retrieval systems.},
	url = {http://ccrma.stanford.edu/~juhan/pubs/jnam-ismir2012.pdf},
	author = {Juhan Nam and Jorge Herrera and Slaney, Malcolm and Smith, Julius}
}
