Drum Sample Retrieval from Mixed Audio via a Joint Embedding Space of Mixed and Single Audio Samples

Kim W, Nam J. Drum Sample Retrieval from Mixed Audio via a Joint Embedding Space of Mixed and Single Audio Samples. In: AES Convention 149. Audio Engineering Society; 2020. Paper 10390. Available from: https://aes.org/publications/elibrary-page/?id=20927

@inproceedings{Kim2020_20927,
  author = {Kim, Wonil and Nam, Juhan},
  title = {{Drum Sample Retrieval from Mixed Audio via a Joint Embedding Space of Mixed and Single Audio Samples}},
  booktitle = {AES Convention 149},
  note = {Paper 10390},
  year = {2020},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=20927}
}

TY  - CPAPER
TI  - Drum Sample Retrieval from Mixed Audio via a Joint Embedding Space of Mixed and Single Audio Samples
AU  - Kim, Wonil
AU  - Nam, Juhan
T2  - AES Convention 149
M1  - Paper 10390
PY  - 2020
DA  - 2020/10/06
UR  - https://aes.org/publications/elibrary-page/?id=20927
PB  - Audio Engineering Society
LA  - en
AB  - Sample-based music creation has become a mainstream practice. One of the key tasks in the creative process is searching desired samples in large collections. However, most commercial packages describe the samples using metadata, which is limited to explain subtle nuances in timbre and style. Inspired by music producers who often find instrument samples with a reference song, we propose a query-by-example scheme that takes mixed audio as a query and retrieves single audio samples. Our method is based on deep metric learning where a neural network is trained to locate single audio and their mixtures closely in the embedding space. We show that our model successfully retrieves single audio samples given mixed audio query in various evaluation scenarios.
ER  -