Structural Decomposition of Recorded Vocal Performances and It's Application to Intelligent Audio Editing

Fazekas G, Sandler M. Structural Decomposition of Recorded Vocal Performances and It's Application to Intelligent Audio Editing. In: AES Convention 123. Audio Engineering Society; 2007. Paper 7249. Available from: https://aes.org/publications/elibrary-page/?id=14307

@inproceedings{Fazekas2007_14307,
  author = {Fazekas, György and Sandler, Mark},
  title = {{Structural Decomposition of Recorded Vocal Performances and It's Application to Intelligent Audio Editing}},
  booktitle = {AES Convention 123},
  note = {Paper 7249},
  year = {2007},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=14307}
}

TY  - CPAPER
TI  - Structural Decomposition of Recorded Vocal Performances and It's Application to Intelligent Audio Editing
AU  - Fazekas, György
AU  - Sandler, Mark
T2  - AES Convention 123
M1  - Paper 7249
PY  - 2007
DA  - 2007/10/06
UR  - https://aes.org/publications/elibrary-page/?id=14307
PB  - Audio Engineering Society
LA  - en
AB  - In an intelligent editing environment, the semantic music structure can be used as beneficial assistance during the post production process. In this paper we propose a new approach to extract both low and high level hierarchical structure from vocal tracks of multi-track master recordings. Contrary to most segmentation methods for polyphonic audio, we utilize extra information available when analyzing a single audio track. A sequence of symbols is derived using a hierarchical decomposition method involving onset detection, pitch tracking and timbre modelling to capture phonetic similarity. Results show that the applied model well captures similarity of short voice segments.
ER  -