Andreas Arzt, Stefan Lattner,
"Audio-to-score alignment using transposition-invariant features"
: Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), 2018
Original Titel:
Audio-to-score alignment using transposition-invariant features
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the International Society for Music Information Retrieval Conference (ISMIR)
Original Kurzfassung:
Audio-to-score alignment is an important pre-processing
step for in-depth analysis of classical music. In this paper, we apply novel transposition-invariant audio features
to this task. These low-dimensional features represent local pitch intervals and are learned in an unsupervised fashion by a gated autoencoder. Our results show that the
proposed features are indeed fully transposition-invariant
and enable accurate alignments between transposed scores
and performances. Furthermore, they can even outperform
widely used features for audio-to-score alignment on ?untransposed data?, and thus are a viable and more flexible alternative to well-established features for music alignment
and matching.
Forschungs-