Automatic Recognition and Parametrization of Frequency Modulation Techniques in Bass Guitar Recordings

Abeßer J, Dittmar C, Schuller G. Automatic Recognition and Parametrization of Frequency Modulation Techniques in Bass Guitar Recordings. In: AES Conference: 42nd International Conference: Semantic Audio. Audio Engineering Society; 2011. Paper 3-1. Available from: https://aes.org/publications/elibrary-page/?id=15961

@inproceedings{Abeser2011_15961,
  author = {Abeßer, Jakob and Dittmar, Christian and Schuller, Gerald},
  title = {{Automatic Recognition and Parametrization of Frequency Modulation Techniques in Bass Guitar Recordings}},
  booktitle = {AES Conference: 42nd International Conference: Semantic Audio},
  note = {Paper 3-1},
  year = {2011},
  month = jul,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=15961}
}

TY  - CPAPER
TI  - Automatic Recognition and Parametrization of Frequency Modulation Techniques in Bass Guitar Recordings
AU  - Abeßer, Jakob
AU  - Dittmar, Christian
AU  - Schuller, Gerald
T2  - AES Conference: 42nd International Conference: Semantic Audio
M1  - Paper 3-1
PY  - 2011
DA  - 2011/07/06
UR  - https://aes.org/publications/elibrary-page/?id=15961
PB  - Audio Engineering Society
LA  - en
AB  - In this paper, we propose a novel method to parametrize and classify different frequency modulation techniques in bass guitar recordings. A parametric spectral estimation technique is applied to refine the fundamental frequency estimates derived from an existing bass transcription algorithm. We apply a two-stage taxonomy of bass playing styles with special focus on the frequency modulation techniques slide, bending, and vibrato. An existing database of isolated note recordings is extended by approx. 900 samples to evaluate the presented algorithm. We achieve comparable classification accuracy values of 85.1% and 81.5% for classification on class-level and subclass-level. Furthermore, two potential application scenarios are outlined.
ER  -