Automatic Optimization of a Music Similarity Metric using Similarity Pairs

Kastner T, Herre J, Allamanche E, Hellmuth O, Ertel C, Schalek M. Automatic Optimization of a Music Similarity Metric using Similarity Pairs. In: AES Conference: 25th International Conference: Metadata for Audio. Audio Engineering Society; 2004. Paper 5-3. Available from: https://aes.org/publications/elibrary-page/?id=12815

@inproceedings{Kastner2004_12815,
  author = {Kastner, Thorsten and Herre, Juergen and Allamanche, Eric and Hellmuth, Oliver and Ertel, Christian and Schalek, Marion},
  title = {{Automatic Optimization of a Music Similarity Metric using Similarity Pairs}},
  booktitle = {AES Conference: 25th International Conference: Metadata for Audio},
  note = {Paper 5-3},
  year = {2004},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=12815}
}

TY  - CPAPER
TI  - Automatic Optimization of a Music Similarity Metric using Similarity Pairs
AU  - Kastner, Thorsten
AU  - Herre, Juergen
AU  - Allamanche, Eric
AU  - Hellmuth, Oliver
AU  - Ertel, Christian
AU  - Schalek, Marion
T2  - AES Conference: 25th International Conference: Metadata for Audio
M1  - Paper 5-3
PY  - 2004
DA  - 2004/06/06
UR  - https://aes.org/publications/elibrary-page/?id=12815
PB  - Audio Engineering Society
LA  - en
AB  - With the growing amount of multimedia data available everywhere and the necessity to provide efficient methods for browsing and indexing this plethora of audio content, automated musical similarity search and retrieval has gained considerable attention in recent years. This paper presents a system which combines a set of perceptual low level features with appropriate classification strategies for the task of retrieving similar sounding songs in a database. A method for analyzing the classification results while avoiding time consuming subjective listening tests for an optimum feature selection and combination is presented. It is based on a calculated ''similarity index'' which reflects the similarity between specifically embedded smilarity pairs. The system's performance as well as the usefulness of the analyzing method is evaluated by a subjective listening test.
ER  -