Blind estimation of ensemble width in binaural music recordings using ‘spatiograms’ under simulated anechoic conditions

Antoniuk P, Zielinski SK. Blind estimation of ensemble width in binaural music recordings using ‘spatiograms’ under simulated anechoic conditions. In: AES Conference: AES 2023 International Conference on Spatial and Immersive Audio. Audio Engineering Society; 2023. Paper 15. Available from: https://aes.org/publications/elibrary-page/?id=22203

@inproceedings{Antoniuk2023_22203,
  author = {Antoniuk, Pawel and Zielinski, Slawomir Krzysztof},
  title = {{Blind estimation of ensemble width in binaural music recordings using ‘spatiograms’ under simulated anechoic conditions}},
  booktitle = {AES Conference: AES 2023 International Conference on Spatial and Immersive Audio},
  note = {Paper 15},
  year = {2023},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22203}
}

TY  - CPAPER
TI  - Blind estimation of ensemble width in binaural music recordings using ‘spatiograms’ under simulated anechoic conditions
AU  - Antoniuk, Pawel
AU  - Zielinski, Slawomir Krzysztof
T2  - AES Conference: AES 2023 International Conference on Spatial and Immersive Audio
M1  - Paper 15
PY  - 2023
DA  - 2023/08/06
UR  - https://aes.org/publications/elibrary-page/?id=22203
PB  - Audio Engineering Society
LA  - en
AB  - Modern music information retrieval algorithms could be used to comprehensively characterize monaural recordings. However, there are currently no methods capable of analysing real-life binaural recordings in terms of their spatial audio content. The development of such analysers is important as they could be used for the search and retrieval of binaural recordings in terms of their spatial content. This paper presents a method for estimating one of the most important attributes pertinent to spatial audio scene description in music recordings, namely ‘ensemble width.’ The study builds on the concept of spatial analysis of binaural signals using ‘spatiograms,’ recently introduced by Arthi and Sreenivas [1]. The method was developed using simulated anechoic conditions, whereby audio recordings were generated by the convolution of monaural recordings with head-related transfer functions (HRTF). According to the results obtained under the HRTF-dependent tests, the mean absolute error of ensemble width estimation was equal to 13.62°. Under the HRTF-independent test conditions, the error increased by only 1.99°, indicating that the generalization property of the method could be considered satisfactory.
ER  -