  Recent neural room impulse response (RIR) estimators typically comprise an
encoder for reference audio analysis and a generator for RIR synthesis.
Especially, it is the performance of the generator that directly influences the
overall estimation quality. In this context, we explore an alternate generator
architecture for improved performance. We first train an autoencoder with
residual quantization to learn a discrete latent token space, where each token
represents a small time-frequency patch of the RIR. Then, we cast the RIR
estimation problem as a reference-conditioned autoregressive token generation
task, employing transformer variants that operate across frequency, time, and
quantization depth axes. This way, we address the standard blind estimation
task and additional acoustic matching problem, which aims to find an RIR that
matches the source signal to the target signal's reverberation characteristics.
Experimental results show that our system is preferable to other baselines
across various evaluation metrics.
