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This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
This python code performs an efficient speech reverberation starting from a dataset of close-talking speech signals and a collection of acoustic impulse responses.
This is the official implementation of our mesh-based neural network (MESH2IR) to generate acoustic impulse responses (IRs) for indoor 3D scenes represented using a mesh.
The IoSR listening room multichannel BRIR dataset contains binaural room impulse responses measured at head angles of 0 to 360 degrees in 2.5 degree steps, for 24 loudspeakers in the standard positions for 22.2 reproduction.
Blackman-Harris Window functions (3-, 5-, 7-term etc.) from 1K to 64M points based only on LUTs and DSP48s FPGA resources. Main core - CORDIC like as DDS (sine / cosine generator)