Investigating the Effect of Sample Rate Variation on the Accuracy of Sound Source Localisation Using a Neural Network

Hobern S, Archer-Boyd A, Murphy D. Investigating the Effect of Sample Rate Variation on the Accuracy of Sound Source Localisation Using a Neural Network. In: AVARIG 2026: Audio for Virtual and Augmented Reality and Immersive Games. Audio Engineering Society; 2026. Paper 10323. Available from: https://aes.org/publications/elibrary-page/?id=23285

@inproceedings{Hobern2026_23285,
  author = {Hobern, Samuel and Archer-Boyd, Alan and Murphy, Damian},
  title = {{Investigating the Effect of Sample Rate Variation on the Accuracy of Sound Source Localisation Using a Neural Network}},
  booktitle = {AVARIG 2026: Audio for Virtual and Augmented Reality and Immersive Games},
  note = {Paper 10323},
  year = {2026},
  month = jun,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=23285}
}

TY  - CPAPER
TI  - Investigating the Effect of Sample Rate Variation on the Accuracy of Sound Source Localisation Using a Neural Network
AU  - Hobern, Samuel
AU  - Archer-Boyd, Alan
AU  - Murphy, Damian
T2  - AVARIG 2026: Audio for Virtual and Augmented Reality and Immersive Games
M1  - Paper 10323
PY  - 2026
DA  - 2026/06/30
UR  - https://aes.org/publications/elibrary-page/?id=23285
PB  - Audio Engineering Society
LA  - en
AB  - This paper describes an experiment to investigate how the localisation performance of a neural network for Sound Source Localisation named SampleDOA_SR would be affected by reducing the sample rate of the audio training data. Reducing the sample rate has several benefits; most notably a reduction in training time. The goal is to determine an appropriate sample rate which balances both localisation accuracy and training time. This information will be used to inform the future training of a neural network for Sound Source Localisation which will be used in a stereo upmixing pipeline. The results of this experiment indicate reducing the sample rate from 48 kHz down to below 4 kHz results in a significant decrease in localisation accuracy. However, above 4 kHz, the decrease in localisation accuracy is minimal whilst training time is reduced significantly. This suggests providing the particular application for the model does not require the highest level of accuracy, a minimal reduction in localisation performance may be acceptable to obtain a large reduction in training time which would also reduce the environmental impact of the model training. A sample rate of 16 kHz is suggested as a suitable balance between accuracy and training time.
ER  -