Automatic Extraction of Music Descriptors from Acoustic Signals Using EDS

Zils A, Pachet F. Automatic Extraction of Music Descriptors from Acoustic Signals Using EDS. In: AES Convention 116. Audio Engineering Society; 2004. Paper 6127. Available from: https://aes.org/publications/elibrary-page/?id=12768

@inproceedings{Zils2004_12768,
  author = {Zils, Aymeric and Pachet, Francois},
  title = {{Automatic Extraction of Music Descriptors from Acoustic Signals Using EDS}},
  booktitle = {AES Convention 116},
  note = {Paper 6127},
  year = {2004},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=12768}
}

TY  - CPAPER
TI  - Automatic Extraction of Music Descriptors from Acoustic Signals Using EDS
AU  - Zils, Aymeric
AU  - Pachet, Francois
T2  - AES Convention 116
M1  - Paper 6127
PY  - 2004
DA  - 2004/05/06
UR  - https://aes.org/publications/elibrary-page/?id=12768
PB  - Audio Engineering Society
LA  - en
AB  - High-Level music descriptors are key ingredients for music information retrieval systems. Although there is a long tradition in extracting information from acoustic signals, the field of music information extraction is largely heuristic in nature. We present here a heuristic-based generic approach for extracting automatically high-level music descriptors from acoustic signals. This approach is based on Genetic Programming, used to build relevant features as functions of mathematical and signal processing operators. The search of relevant features is guided by specialized heuristics that embody knowledge about the signal processing functions built by the system. Signal processing patterns are used in order to control the general processing methods. In addition, rewriting rules are introduced to simplify overly complex expressions, and a caching system further reduces the computing cost of each cycle.
ER  -