AI DevOps for Large-Scale HRTF Predition and Evaluation: An End to End Pipeline

Shahid F, Javeri N, Jain K, Badhwar S. AI DevOps for Large-Scale HRTF Predition and Evaluation: An End to End Pipeline. In: AES Conference: 2018 AES International Conference on Audio for Virtual and Augmented Reality. Audio Engineering Society; 2018. Paper P9-4. Available from: https://aes.org/publications/elibrary-page/?id=19700

@inproceedings{Shahid2018_19700,
  author = {Shahid, Faiyadh and Javeri, Nikhil and Jain, Kapil and Badhwar, Shruti},
  title = {{AI DevOps for Large-Scale HRTF Predition and Evaluation: An End to End Pipeline}},
  booktitle = {AES Conference: 2018 AES International Conference on Audio for Virtual and Augmented Reality},
  note = {Paper P9-4},
  year = {2018},
  month = aug,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=19700}
}

TY  - CPAPER
TI  - AI DevOps for Large-Scale HRTF Predition and Evaluation: An End to End Pipeline
AU  - Shahid, Faiyadh
AU  - Javeri, Nikhil
AU  - Jain, Kapil
AU  - Badhwar, Shruti
T2  - AES Conference: 2018 AES International Conference on Audio for Virtual and Augmented Reality
M1  - Paper P9-4
PY  - 2018
DA  - 2018/08/06
UR  - https://aes.org/publications/elibrary-page/?id=19700
PB  - Audio Engineering Society
LA  - en
AB  - Bringing truly immersive 3D audio experiences to the end user requires a fast and a user friendly method of predicting HRTFs. While machine learning based approaches for HRTF prediction hold potential, it can be challenging to determine the best work?ow for deployment given the iterative nature of data preprocessing, feature extraction, prediction, and performance evaluation. Here, we describe an automated, end to end pipeline for HRTF prediction and evaluation that simultaneously tracks data, code and model, allowing for a comparison of existing and new techniques against a single benchmark.
ER  -