This paper studies tagging and retrieval of room impulse responses from a labelled library. A similarity-based method is introduced that relies on perceptually relevant characteristics of reverberation. This method is developed using a publicly available dataset of algorithmic reverberation settings. Semantic word vectors are introduced to exploit semantic correlation among tags and allow for unseen words to be used for retrieval. Average precision is reported on a subset of the dataset as well as tagging of recorded room impulse responses. The developed approach manages to assign downloaded room impulse responses to tags that match their short descriptions. Furthermore, introducing semantic word vectors allows it to perform well even when large portions of the training data have been replaced by synonyms.
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