  In real-world acoustic scenarios, there often are multiple sound sources
present in a room. These sources are situated in various locations and produce
sounds that reach the listener from multiple directions. The presence of
multiple sources in a room creates new challenges in estimating the room
impulse response (RIR) as each source has a unique RIR, dependent on its
location and orientation. Therefore, issues of determining which RIR should be
predicted and how to predict it arise, when the input signal is a mixture of
multiple reverberated sources. To address these, we propose a new task of
predicting a "representative" RIR for a room in a multiple source environment
and present a training method to achieve this goal. In contrast to the model
trained in a single source environment, our method shows robust performance,
regardless of the number of sources in the environment.
