This invention belongs to the field of music
data processing and relates to a symbolic music classification method and
system based on Tonnetz spatiotemporal graphs. The method generates a two-dimensional grid of symbolic music data, consisting of time frames and pitches, and defines graph nodes and initial features for each grid point. Discrete note events are organized into structured nodes. Based on this two-dimensional grid, graph nodes corresponding to pitches that satisfy Tonnetz interval relationships are connected within the same
time frame to form spatial edges. These spatial edges explicitly introduce
consonant interval relationships between pitches, allowing the model to directly obtain structural information with musicological significance without relying on large amounts of data for self-learning, thus solving the problem of insufficient utilization of
harmonic structure. Furthermore, graph nodes with the same
pitch or
pitch differences within a preset range are connected between adjacent time frames to form temporal edges. These temporal edges directly
encode the continuous and stepwise motion of
unison parts during voice progression, enabling the model to capture the dynamic evolution of music in the time dimension and solving the problem of missing voice progression characterization.