  Increasing amount of research has shed light on machine perception of audio
events, most of which concerns detection and classification tasks. However,
human-like perception of audio scenes involves not only detecting and
classifying audio sounds, but also summarizing the relationship between
different audio events. Comparable research such as image caption has been
conducted, yet the audio field is still quite barren. This paper introduces a
manually-annotated dataset for audio caption. The purpose is to automatically
generate natural sentences for audio scene description and to bridge the gap
between machine perception of audio and image. The whole dataset is labelled in
Mandarin and we also include translated English annotations. A baseline
encoder-decoder model is provided for both English and Mandarin. Similar BLEU
scores are derived for both languages: our model can generate understandable
and data-related captions based on the dataset.
