Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation

Researchers decomposed room impulse responses to understand which acoustic components enable single-channel speaker distance estimation, finding that without time calibration, models rely on early reflections and achieve 1.29m error, while time-calibrated models achieve 0.14m accuracy using propagation delay alone.

This acoustic signal processing research advances fundamental understanding of how machine learning models extract spatial information from audio recordings. The study systematically isolates which reverberation components contribute to distance estimation, revealing a critical distinction between calibrated and uncalibrated scenarios. When temporal information is unavailable, neural networks shift strategy to exploit early reflections and reverberation characteristics, degrading accuracy to 1.29 meters mean absolute error. Conversely, synchronized timing enables extraction of propagation delay, achieving centimeter-level precision regardless of room acoustics.

The research addresses a practical challenge in audio processing and speech recognition applications where microphone synchronization and source calibration vary significantly. Understanding these dependencies has implications for real-world deployment of voice-activated systems, hearing aids, and acoustic monitoring in uncontrolled environments. The correlation analysis with acoustic metrics—direct-to-reverberant ratio (DRR), clarity index (C50), and reverberation time (T60)—provides quantifiable relationships between room characteristics and estimation feasibility.

For developers implementing distance-based audio systems, the findings suggest that investing in temporal calibration mechanisms yields dramatic accuracy improvements. Applications requiring robust performance in reverberant spaces must either ensure precise timing synchronization or accept substantial error margins when relying solely on acoustic cues. The work establishes empirical baselines for model performance across controlled conditions, enabling more realistic expectations for production deployments. Future research should examine transfer learning across different acoustic environments and the feasibility of implicit time calibration through neural network design, potentially bridging the performance gap between idealized laboratory conditions and field applications.

• →Early reflections provide the most informative acoustic cues for distance estimation when time calibration is unavailable
• →Time-calibrated models achieve 0.14m accuracy by extracting propagation delay independent of room impulse response composition
• →Without temporal synchronization, estimation error increases to 1.29m as models exploit reverberation-based acoustic features
• →Distance estimation accuracy degrades significantly in highly reverberant environments with weak direct sound energy
• →The 9x improvement from time calibration demonstrates that precise onset detection is more valuable than sophisticated acoustic analysis