Invertibility of a room impulse response

By S. Neely, J. Allen. 1979

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Deliang Wang
2. Yuxuan Wang
3. Kun Han
4. Bo Wu
5. Chin-Hui Lee
6. Lei Xie
7. Kehuang Li
8. Ke Wang

Citations in the corpus

• Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising (2017, neutral): “Inverse filters, however, cannot be fully realized, since the RIR is unstable due to its nonminimum phase nature [29].”
• Learning Spectral Mapping for Speech Dereverberation and Denoising (2015, background): “Further, this approach assumes that the RIR function is minimum-phase that is often not satisfied in practice [27].”
• Investigating Generative Adversarial Networks based Speech Dereverberation for Robust Speech Recognition (2018, background): “In theory, reverberant speech can be regarded as a room impulse response (RIR) convolving with the clean speech in the time domain [5].”
• Speech Dereverberation (2010, background): “It is important to note that magnitude compensation only results in audible distortions in the processed speech signal [19][22].”
• Equalization in an acoustic reverberant environment: robustness results (2000, background): “Actually, perfect reverberation removal in single-channel systems is not possible because the room transfer functions tend to have nonminimum phase components, and thus the exact inverse filter would consequently be either unstable or noncausal [13] and therefore unrealizable in practice.”
• A two-stage algorithm for one-microphone reverberant speech enhancement (2006, background): “A perfect reconstruction of the original signal exists, however, only if the room impulse response function is a minimum-phase filter.”
• A Reverberation-Time-Aware Approach to Speech Dereverberation Based on Deep Neural Networks (2017, background): “Thus, an effective dereverberation solution will benefit many speech applications.”