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We propose a novel approach for blind room impulse response (RIR) estimation systems in the context of a downstream application scenario, far-field automatic speech recognition (ASR).
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“Estimation of modal decay parameters from noisy response measurements,”
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“Bayesian regularization and nonnegative deconvolution for room impulse response estimation,”
Yuanqing Lin and Daniel D Lee, · 2006
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“Room impulse response estimation using sparse online prediction and absolute loss,”
Koby Crammer and Daniel D Lee, · 2006
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“Speaker adaptation of context dependent deep neural networks,”
Hank Liao, · 2013
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“High-order diffraction and diffuse reflections for interactive sound propagation in large environments,”
Carl Schissler, Ravish Mehra, and Dinesh Manocha, · 2014
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“On the use of early-to-late reverberation ratio for ASR in reverberant environments,”
Alessio Brutti and Marco Matassoni, · 2014
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“Room impulse response estimation by iterative weighted L 1 L_{1} -norm,”
Marco Crocco and Alessio Del Bue, · 2015
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“Improving speech recognition in reverberation using a room-aware deep neural network and multi-task learning,”
Ritwik Giri, Michael L. Seltzer, Jasha Droppo, and Dong Yu, · 2015
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“Librispeech: An ASR corpus based on public domain audio books,”
Vassil Panayotov, Guoguo Chen, Daniel Povey, and Sanjeev Khudanpur, · 2015
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“Recent progresses in deep learning based acoustic models,”
Dong Yu and Jinyu Li, · 2017
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“A reverberation-time-aware approach to speech dereverberation based on deep neural networks,”
Bo Wu, Kehuang Li, Minglei Yang, and Chin-Hui Lee, · 2017
Cited alongside, same era.
“Building and evaluation of a real room impulse response dataset,”
Igor Szöke, Miroslav Skácel, Ladislav Mosner, Jakub Paliesek, and Jan Honza Cernocký, · 2019
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“Monaural speech dereverberation using temporal convolutional networks with self attention,”
Yan Zhao, DeLiang Wang, Buye Xu, and Tao Zhang, · 2020
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“The cone of silence: Speech separation by localization,”
“TeCANet: Temporal-contextual attention network for environment-aware speech dereverberation,”
Helin Wang, Bo Wu, Lianwu Chen, Meng Yu, Jianwei Yu, Yong Xu, Shi-Xiong Zhang, Chao Weng, Dan Su, and Dong Yu, · 2021
Later among the works it cites.
“ESPnet-SE: End-to-end speech enhancement and separation toolkit designed for asr integration,”
Chenda et al., · 2021
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“FRA-RIR: Fast random approximation of the image-source method,”
Yi Luo and Jianwei Yu, · 2022
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“MESH2IR: Neural acoustic impulse response generator for complex 3d scenes,”
Anton Ratnarajah et al., · 2022
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“Soundspaces 2.0: A simulation platform for visual-acoustic learning,”
Changan Chen, Carl Schissler, Sanchit Garg, Philip Kobernik, Alexander Clegg, Paul Calamia, Dhruv Batra, Philip W Robinson, and Kristen Grauman, · 2022
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Teerapat Jenrungrot, Vivek Jayaram, Steven M. Seitz, and Ira Kemelmacher-Shlizerman, · 2020
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“IR-GAN: room impulse response generator for far-field speech recognition,”
Anton Ratnarajah, Zhenyu Tang, and Dinesh Manocha, · 2021
Cited alongside, same era.
“Improving reverberant speech separation with synthetic room impulse responses,”
Rohith Aralikatti et al., · 2021
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“Filtered noise shaping for time domain room impulse response estimation from reverberant speech,”
Christian J. Steinmetz, Vamsi Krishna Ithapu, and Paul Calamia, · 2021
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“GWA: A large high-quality acoustic dataset for audio processing,”
Zhenyu Tang et al., · 2022
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“Sparse modeling of the early part of noisy room impulse responses with sparse bayesian learning,”
Maozhong Fu, Jesper Rindom Jensen, Yuhan Li, and Mads Græsbøll Christensen, · 2022
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“FAST-RIR: Fast neural diffuse room impulse response generator,”
Anton Ratnarajah, Shi-Xiong Zhang, Meng Yu, Zhenyu Tang, Dinesh Manocha, and Dong Yu, · 2022
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“Few-shot audio-visual learning of environment acoustics,”
Sagnik Majumder, Changan Chen, Ziad Al-Halah, and Kristen Grauman, · 2022
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