Multiple Loudspeaker Localization with Simultaneous Deconvolution

Bharitkar S, Celestinos A. Multiple Loudspeaker Localization with Simultaneous Deconvolution. In: 2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio. Audio Engineering Society; 2025. Paper 10. Available from: https://aes.org/publications/elibrary-page/?id=22999

@inproceedings{Bharitkar2025_22999,
  author = {Bharitkar, Sunil and Celestinos, Adrian},
  title = {{Multiple Loudspeaker Localization with Simultaneous Deconvolution}},
  booktitle = {2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio},
  note = {Paper 10},
  year = {2025},
  month = sep,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=22999}
}

TY  - CPAPER
TI  - Multiple Loudspeaker Localization with Simultaneous Deconvolution
AU  - Bharitkar, Sunil
AU  - Celestinos, Adrian
T2  - 2025 AES International Conference on Artificial Intelligence and Machine Learning for Audio
M1  - Paper 10
PY  - 2025
DA  - 2025/09/02
UR  - https://aes.org/publications/elibrary-page/?id=22999
PB  - Audio Engineering Society
LA  - en
AB  - Loudspeaker localization in a reverberant room is essential for ensuring high-quality immersive rendering with spatially distributed loudspeakers. Traditional approaches rely on beamforming or deep-learning-based feature extraction for a single source localization at a time. This paper applies simultaneous deconvolution (SD) towards multi-source localization where loudspeaker distances are first estimated from impulse responses deconvolved {\em simultaneously}, and the distances are then used towards estimating loudspeaker signal Direction-of-Arrival (DOA). We compare geometric angle estimators, supervised and unsupervised machine learning (ML) models, and traditional signal processing approaches involving cross-correlation and pseudo-spectrum. The Trapezoid estimator and supervised ML models are the top two techniques for use with SD for loudspeaker localization, factoring both synthetic and listening room test sets.
ER  -