  Even in the absence of any explicit semantic annotation, vast collections of
audio recordings provide valuable information for learning the categorical
structure of sounds. We consider several class-agnostic semantic constraints
that apply to unlabeled nonspeech audio: (i) noise and translations in time do
not change the underlying sound category, (ii) a mixture of two sound events
inherits the categories of the constituents, and (iii) the categories of events
in close temporal proximity are likely to be the same or related. Without
labels to ground them, these constraints are incompatible with classification
loss functions. However, they may still be leveraged to identify geometric
inequalities needed for triplet loss-based training of convolutional neural
networks. The result is low-dimensional embeddings of the input spectrograms
that recover 41% and 84% of the performance of their fully-supervised
counterparts when applied to downstream query-by-example sound retrieval and
sound event classification tasks, respectively. Moreover, in
limited-supervision settings, our unsupervised embeddings double the
state-of-the-art classification performance.
