A binaural room impulse response database for the evaluation of dereverberation algorithms
By Marco Jeub, Magnus Schaefer, Peter Vary. International Conference on Digital Signal Processing, 2009
468 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Computational Mechanics, Signal Processing
View Paper: https://www.semanticscholar.org/paper/875a1edaaefb9172307080d3b07b16044082ea5d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Jean-Marc Valin
- Daniel Povey
- Kehuang Li
- Bo Wu
- Sanjeev Khudanpur
- Vijayaditya Peddinti
- Tom Ko
- Minglei Yang
In Computational Mechanics
- Ritwik Giri
- Umut Isik
- A. Krishnaswamy
- Jean-Marc Valin
- Karim Helwani
- Neerad Phansalkar
- Kehuang Li
- Bo Wu
In Signal Processing
- Daniel Povey
- Sanjeev Khudanpur
- Vimal Manohar
- Shinji Watanabe
- Jinyu Li
- Vijayaditya Peddinti
- Tom Ko
- Dong Yu
Citations in the corpus
- A Reverberation-Time-Aware Approach to Speech Dereverberation Based on Deep Neural Networks (2017, methodology): “Nevertheless, in reverberant conditions, the inter-frame correlation depends on RT60 as shown below.”
- A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation (2017, unknown): “Secondly, the covariance E ( r ij ( ν ) r ∗ ij ( ν (cid:48) )) between its Fourier transform r ij ( ν ) at two different frequencies ν and ν (cid:48) decays quickly with the difference between ν and ν (cid:48) [44], [45].”
- A study on data augmentation of reverberant speech for robust speech recognition (2017, methodology): “The set of real RIRs (R) is composed of three databases: the RWCP sound scene database [8], the REVERB challenge database [9] and the Aachen impulse response database [10].”
- JHU ASpIRE system: Robust LVCSR with TDNNS, iVector adaptation and RNN-LMS (2015, methodology): “Multi-condition training data was created by distorting speech with real world room impulse response (RIR) and noise recordings available from three different databases viz., the RWCP sound scene database 3 [15], the REVERB challenge database [16] and the Aachen impulse response database [17].”
- PoCoNet: Better Speech Enhancement with Frequency-Positional Embeddings, Semi-Supervised Conversational Data, and Biased Loss (2020, methodology): “For real impulse responses, we use the Aachen Impulse Response dataset [22] consisting of of 214 RIR recordings.”