  In the field of music information retrieval, the task of simultaneously
identifying the presence or absence of multiple musical instruments in a
polyphonic recording remains a hard problem. Previous works have seen some
success in improving instrument classification by applying temporal attention
in a multi-instance multi-label setting, while another series of work has also
suggested the role of pitch and timbre in improving instrument recognition
performance. In this project, we further explore the use of attention mechanism
in a timbral-temporal sense, \`a la visual attention, to improve the
performance of musical instrument recognition using weakly-labeled data. Two
approaches to this task have been explored. The first approach applies
attention mechanism to the sliding-window paradigm, where a prediction based on
each timbral-temporal `instance' is given an attention weight, before
aggregation to produce the final prediction. The second approach is based on a
recurrent model of visual attention where the network only attends to parts of
the spectrogram and decide where to attend to next, given a limited number of
`glimpses'.
