Data generation with device-modeling using Treble’s hybrid cloud-based system

Driscoll E, Cosnefroy M, Hafsteinsson H, Stefánsson J, Pind F. Data generation with device-modeling using Treble’s hybrid cloud-based system. In: AES Convention 155. Audio Engineering Society; 2023. Paper 177. Available from: https://aes.org/publications/elibrary-page/?id=22331

@inproceedings{Driscoll2023_22331,
  author = {Driscoll, Erin and Cosnefroy, Matthias and Hafsteinsson, Haukur and Stefánsson, Jón and Pind, Finnur},
  title = {{Data generation with device-modeling using Treble’s hybrid cloud-based system}},
  booktitle = {AES Convention 155},
  note = {Paper 177},
  year = {2023},
  month = oct,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22331}
}

TY  - CPAPER
TI  - Data generation with device-modeling using Treble’s hybrid cloud-based system
AU  - Driscoll, Erin
AU  - Cosnefroy, Matthias
AU  - Hafsteinsson, Haukur
AU  - Stefánsson, Jón
AU  - Pind, Finnur
T2  - AES Convention 155
M1  - Paper 177
PY  - 2023
DA  - 2023/10/06
UR  - https://aes.org/publications/elibrary-page/?id=22331
PB  - Audio Engineering Society
LA  - en
AB  - The growing usage of machine learning and artificial intelligence within audio signal processing underscores the significance of high-quality audio datasets for advancing audio algorithms such as speech enhancement, echo cancellation, de-reverberation, and blind room estimation. However, the conventional approaches of collecting such data present various limitations. Measurement based approaches are costly and time-consuming, and synthetic data generation using standard acoustics simulation methodology has been shown to generalize poorly to real world scenarios, due to limitations in capturing the intricacies of real world room acoustics. In this paper, we present a framework that offers a solution to the challenges associated with dataset creation, enabling the efficient production of extensive datasets that closely mimic real-world audio scenes, thereby enhancing the efficacy of machine learning models. Through the lens of a specific use-case illustration, we highlight the integration of a hybrid wave-based / geometrical acoustics simulation for dataset generation. Notably, our focus extends to accurate device modeling—a critical aspect for the development of multiple-microphone devices and subsequent refinement of machine learning algorithms. We illustrate how the dataset accuracy surpasses the standard limitations of geometrical acoustics simulations. We present analysis of the computational performance of the system and we demonstrate examples of improved machine learning performance using the data.
ER  -