acoustic echo
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Author(s):  
Diego Di Carlo ◽  
Pinchas Tandeitnik ◽  
Cedrić Foy ◽  
Nancy Bertin ◽  
Antoine Deleforge ◽  
...  

AbstractThis paper presents a new dataset of measured multichannel room impulse responses (RIRs) named dEchorate. It includes annotations of early echo timings and 3D positions of microphones, real sources, and image sources under different wall configurations in a cuboid room. These data provide a tool for benchmarking recent methods in echo-aware speech enhancement, room geometry estimation, RIR estimation, acoustic echo retrieval, microphone calibration, echo labeling, and reflector position estimation. The dataset is provided with software utilities to easily access, manipulate, and visualize the data as well as baseline methods for echo-related tasks.



2021 ◽  
pp. 108037
Author(s):  
Juan-Gerardo Avalos ◽  
Giovanny Sanchez ◽  
Carlos Trejo ◽  
Luis Garcia ◽  
Eduardo Pichardo ◽  
...  


2021 ◽  
Vol 182 ◽  
pp. 108215
Author(s):  
Srikanth Burra ◽  
Asutosh Kar ◽  
Jan Østergaard


Author(s):  
Jonah Casebeer ◽  
Nicholas J. Bryan ◽  
Paris Smaragdis
Keyword(s):  


Author(s):  
Hongsheng Chen ◽  
Guoliang Chen ◽  
Kai Chen ◽  
Jing Lu

AbstractThe acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and the far-end signal. Usually, a post-processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of dual-path recurrent neural network (DPRNN) to improve the quality of speech communication. Both the residual signal and the auxiliary signal, the far-end signal or the output of the adaptive filter, obtained from the linear acoustic echo cancelation are adopted to form a dual-stream for the DPRNN. We validate the efficacy of the proposed method in the notoriously difficult double-talk situations and discuss the impact of different auxiliary signals on performance. We also compare the performance of the time domain and the time-frequency domain processing. Furthermore, we propose an efficient and applicable way to deploy our method to off-the-shelf loudspeakers by fine-tuning the pre-trained model with little recorded-echo data.



2021 ◽  
Author(s):  
Ernst Seidel ◽  
Jan Franzen ◽  
Maximilian Strake ◽  
Tim Fingscheidt


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