#61 Polyphonic Instrument Recognition for Exploring Semantic Similarities in Music
Ferdinand Fuhrmann, Perfecto Herrera
Similarity is a key concept for estimating associations among a set
of objects. Music similarity is usually exploited to retrieve relevant
items from a dataset containing audio tracks. In this work, we approach
the problem of semantic similarity between short pieces of
music by analysing their instrumentations. Our aim is to label audio
excerpts with the most salient instruments (e.g. piano, human
voice, drums) and use this information to estimate a semantic relation
(i.e. similarity) between them. We present 3 different methods
for integrating along an audio excerpt frame-based classifier
decisions to derive its instrumental content. Similarity between
audio files is then determined solely by their attached labels. We
evaluate our algorithm in terms of label assignment and similarity
assessment, observing significant differences when comparing it
to commonly used audio similarity metrics. In doing so we test
on music from various genres of Western music to simulate real
world scenarios.
