A study on more realistic room simulation for far-field keyword spotting
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Abstract (arXiv)
We investigate the impact of more realistic room simulation for training far-field keyword spotting systems without fine-tuning on in-domain data. To this end, we study the impact of incorporating the following factors in the room impulse response (RIR) generation: air absorption, surface- and frequency-dependent coefficients of real materials, and stochastic ray tracing. Through an ablation study, a wake word task is used to measure the impact of these factors in comparison with a ground-truth set of measured RIRs. On a hold-out set of re-recordings under clean and noisy far-field conditions, we demonstrate up to 35.8% relative improvement over the commonly-used (single absorption coefficient) image source method. Source code is made available in the Pyroomacoustics package, allowing others to incorporate these techniques in their work.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Jun 2020.
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Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025MB-RIRs: a Synthetic Room Impulse Response Dataset with Frequency-Dependent Absorption Coefficientscited by 3
- 2025Treble10: A high-quality dataset for far-field speech recognition, dereverberation, and enhancementcited by 2
- 2026Improving multichannel speech enhancement through accurate room-acoustic simulationscited by 1
- 2026Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancementcited by 1
- 2026Training DeepFilterNet with Accurate Room Acoustic Simulations Improves Single-Channel Speech Enhancement21 Aug 2026
- 2026Improving multichannel speech enhancement through accurate room-acoustic simulations30 Jun 2026
- 2025Treble10: A high-quality dataset for far-field speech recognition, dereverberation, and enhancement27 Oct 2025
- 2025MB-RIRs: a Synthetic Room Impulse Response Dataset with Frequency-Dependent Absorption Coefficients13 Jul 2025