  The recent development of Audio-based Distributional Semantic Models (ADSMs)
enables the computation of audio and lexical vector representations in a joint
acoustic-semantic space. In this work, these joint representations are applied
to the problem of automatic tag generation. The predicted tags together with
their corresponding acoustic representation are exploited for the construction
of acoustic-semantic clip embeddings. The proposed algorithms are evaluated on
the task of similarity measurement between music clips. Acoustic-semantic
models are shown to outperform the state-of-the-art for this task and produce
high quality tags for audio/music clips.
