Inverse filtering of room acoustics
By M. Miyoshi, Yutaka Kaneda. 1988
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Donald S. Williamson
- Nasser Mohammadiha
- Deliang Wang
- Takuya Yoshioka
- Keisuke Kinoshita
- Simon Doclo
- Tomohiro Nakatani
- Patrick A. Naylor
Citations in the corpus
- Speech dereverberation and denoising using complex ratio masks (2017, methodology): “Other spectral-magnitude based approaches employ inverse filtering [7] or non-negative matrix factorization (NMF) [8].”
- Equalization in an acoustic reverberant environment: robustness results (2000, methodology): “Working in the time domain and using results of the multiple input/output inverse filter theory (MINT) presented in [20], we found the exact inverse filter coefficients from the following system of equations:”
- Geometric source separation: merging convolutive source separation with geometric beamforming (2001, background): “Conditions on A(!) for separability and invertibilit y are discussed in [10] and [5] respectively.”
- Speech Dereverberation (2010, methodology): “In the multi-channel case, an exact inverse can be achieved with the MINT method [23] and subband version [24].”
- Speech Dereverberation Based on Variance-Normalized Delayed Linear Prediction (2010, methodology): “The third approach, which is the main focus of this paper, involves the use of inverse filtering [6], [7].”
- Speech Dereverberation Using Non-Negative Convolutive Transfer Function and Spectro-Temporal Modeling (2016, methodology): “Finally, in acoustic multichannel equalization methods, the room impulse responses (RIR) between the source and the microphones are blindly estimated and used to design an equalization system [12]–[14], where in theory perfect dereverberation can be achieved.”
- Speech intelligibility in reverberation with ideal binary masking: effects of early reflections and signal-to-noise ratio threshold. (2013, background): “Alternatively, dereverberation and speech separation systems often treat the direct sound (anechoic) target speech as the desired signal under the assumption that both noise and reverberation degrade signal quality (e.g., Miyoshi and Kaneda, 1988; Buchner et al. , 2004; Mandel et al. , 2010).”