Multimodal Musician Recognition
Jordan Hochenbaum, Ajay Kapur, and Matthew Wright
Proceedings of the International Conference on New Interfaces for Musical Expression
- Year: 2010
- Location: Sydney, Australia
- Pages: 233–237
- Keywords: Performer Recognition, Multimodal, HCI, Machine Learning, Hyperinstrument, eSitar
- DOI: 10.5281/zenodo.1177805
- PDF link
Abstract:
This research is an initial effort in showing how a multimodal approach can improve systems for gaining insight into a musician's practice and technique. Embedding a variety of sensors inside musical instruments and synchronously recording the sensors' data along with audio, we gather a database of gestural information from multiple performers, then use machine-learning techniques to recognize which musician is performing. Our multimodal approach (using both audio and sensor data) yields promising performer classification results, which we see as a first step in a larger effort to gain insight into musicians' practice and technique.
Citation:
Jordan Hochenbaum, Ajay Kapur, and Matthew Wright. 2010. Multimodal Musician Recognition. Proceedings of the International Conference on New Interfaces for Musical Expression. DOI: 10.5281/zenodo.1177805BibTeX Entry:
@inproceedings{Hochenbaum2010, abstract = {This research is an initial effort in showing how a multimodal approach can improve systems for gaining insight into a musician's practice and technique. Embedding a variety of sensors inside musical instruments and synchronously recording the sensors' data along with audio, we gather a database of gestural information from multiple performers, then use machine-learning techniques to recognize which musician is performing. Our multimodal approach (using both audio and sensor data) yields promising performer classification results, which we see as a first step in a larger effort to gain insight into musicians' practice and technique. }, address = {Sydney, Australia}, author = {Hochenbaum, Jordan and Kapur, Ajay and Wright, Matthew}, booktitle = {Proceedings of the International Conference on New Interfaces for Musical Expression}, doi = {10.5281/zenodo.1177805}, issn = {2220-4806}, keywords = {Performer Recognition, Multimodal, HCI, Machine Learning, Hyperinstrument, eSitar}, pages = {233--237}, title = {Multimodal Musician Recognition}, url = {http://www.nime.org/proceedings/2010/nime2010_233.pdf}, year = {2010} }