# Coherent-to-Diffuse Power Ratio Estimation for Dereverberation

arXiv: https://arxiv.org/abs/1502.03784 · HTML: https://mlsanity.com/paper/1502.03784/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2015.

## Built on by (most cited first, 7 of 7)

- [SAVVY: Spatial Awareness via Audio-Visual LLMs through Seeing and Hearing](https://mlsanity.com/paper/2506.05414/) (2025)
- [Self-Attention Channel Combinator Frontend for End-to-End Multichannel Far-field Speech Recognition](https://mlsanity.com/paper/2109.04783/) (2021)
- [Customizable End-to-end Optimization of Online Neural Network-supported Dereverberation for Hearing Devices](https://mlsanity.com/paper/2204.02694/) (2022)
- [Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments](https://mlsanity.com/paper/2204.02741/) (2022)
- [Evaluation of Deep-Learning-Based Voice Activity Detectors and Room Impulse Response Models in Reverberant Environments](https://mlsanity.com/paper/2106.13511/) (2021)
- [Spatial Processing Front-End For Distant ASR Exploiting Self-Attention Channel Combinator](https://mlsanity.com/paper/2203.13919/) (2022)
- [A neural network-supported two-stage algorithm for lightweight dereverberation on hearing devices](https://mlsanity.com/paper/2204.02978/) (2022)

## Abstract (arXiv)

The estimation of the time- and frequency-dependent coherent-to-diffuse power ratio (CDR) from the measured spatial coherence between two omnidirectional microphones is investigated. Known CDR estimators are formulated in a common framework, illustrated using a geometric interpretation in the complex plane, and investigated with respect to bias and robustness towards model errors. Several novel unbiased CDR estimators are proposed, and it is shown that knowledge of either the direction of arrival (DOA) of the target source or the coherence of the noise field is sufficient for unbiased CDR estimation. The validity of the model for the application of CDR estimates to dereverberation is investigated using measured and simulated impulse responses. A CDR-based dereverberation system is presented and evaluated using signal-based quality measures as well as automatic speech recognition accuracy. The results show that the proposed unbiased estimators have a practical advantage over existing estimators, and that the proposed DOA-independent estimator can be used for effective blind dereverberation.