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Captioning has attracted much attention in image and video understandingwhile a small amount of work examines audio captioning. This paper contributesa Mandarin-annotated dataset for audio captioning within a car scene. Asentence-level loss is proposed to be used in tandem with a GRU encoder-decodermodel to generate captions with higher semantic similarity to humanannotations. We evaluate the model on the newly-proposed Car dataset, apreviously published Mandarin Hospital dataset and the Joint dataset,indicating its generalization capability across different scenes. Animprovement in all metrics can be observed, including classical naturallanguage generation (NLG) metrics, sentence richness and human evaluationratings. However, though detailed audio captions can now be automaticallygenerated, human annotations still outperform model captions on many aspects.

Audio Caption in a Car Setting with a Sentence-Level Loss