Abstract
We present a new system for chord transcription
from polyphonic musical audio that uses domain-specific
knowledge about tonal harmony and metrical position to
improve chord transcription performance. Low-level pulse
and spectral features are extracted from an audio source
using the Vamp plugin architecture. Subsequently, for
each beat-synchronised chromagram we compute a list of
chord candidates matching that chromagram, together with
the confidence in each candidate. When one particular
chord candidate matches the chromagram significantly better
than all others, this chord is selected to represent the
segment. However, when multiple chords match the chromagram
similarly well, we use a formal music theoretical
model of tonal harmony to select the chord candidate
that best matches the sequence based on the surrounding
chords. In an experiment we show that exploiting metrical
and harmonic knowledge yields statistically significant
chord transcription improvements on a corpus of 217 Beatles,
Queen, and Zweieck songs.
| Original language | English |
|---|---|
| Title of host publication | 13th International Society for Music Information Retrieval Conference (ISMIR 2012) |
| Place of Publication | Porto |
| Pages | 295-300 |
| Number of pages | 6 |
| Publication status | Published - 2012 |