  Modern neural-network-based speech processing systems are typically required
to be robust against reverberation, and the training of such systems thus needs
a large amount of reverberant data. During the training of the systems,
on-the-fly simulation pipeline is nowadays preferred as it allows the model to
train on infinite number of data samples without pre-generating and saving them
on harddisk. An RIR simulation method thus needs to not only generate more
realistic artificial room impulse response (RIR) filters, but also generate
them in a fast way to accelerate the training process. Existing RIR simulation
tools have proven effective in a wide range of speech processing tasks and
neural network architectures, but their usage in on-the-fly simulation pipeline
remains questionable due to their computational complexity or the quality of
the generated RIR filters. In this paper, we propose FRAM-RIR, a fast random
approximation method of the widely-used image-source method (ISM), to
efficiently generate realistic multi-channel RIR filters. FRAM-RIR bypasses the
explicit calculation of sound propagation paths in ISM-based algorithms by
randomly sampling the location and number of reflections of each virtual sound
source based on several heuristic assumptions, while still maintains accurate
direction-of-arrival (DOA) information of all sound sources. Visualization of
oracle beampatterns and directional features shows that FRAM-RIR can generate
more realistic RIR filters than existing widely-used ISM-based tools, and
experiment results on multi-channel noisy speech separation and dereverberation
tasks with a wide range of neural network architectures show that models
trained with FRAM-RIR can also achieve on par or better performance on real
RIRs compared to other RIR simulation tools with a significantly accelerated
training procedure. A Python implementation of FRAM-RIR is released.
