compmus_long_distance | R Documentation |
We use a number of distance measures in Computational Musicology.
compmus_long_distance
brings them together into one place, along with
common alternative names. In order to support plotting, the distances are
returned in long format rather than matrix format. It is designed for
convenience, not speed.
compmus_long_distance(xdat, ydat, feature, method = "euclidean") compmus_self_similarity(dat, feature, method = "euclidean")
xdat, ydat, dat |
Data frames with |
feature |
An (unquoted) column name over which to compute distances. |
method |
A character string indicating which distance metric to use (see Details). Default is Euclidean distance. |
The following methods are supported.
manhattan
,citybolock
,taxicab
,L1
,totvar
Manhattan distance.
euclidean
,L2
Euclidean distance.
chebyshev
,maximum
Chebyshev distance.
pearson
,correlation
Pearson's pseudo-distance.
cosine
Cosine pseudo-distance.
angular
Angular distance.
aitchison
Aitchison distance.
A tibble with columns xstart
, xduration
, ystart
,
yduration
, and d
.
compmus_self_similarity
: Self-similarity matrices in long format
library(tidyverse) tallis <- get_tidy_audio_analysis("2J3Mmybwue0jyQ0UVMYurH") %>% select(segments) %>% unnest(segments) %>% mutate(pitches = map(pitches, compmus_normalise, "manhattan")) chapelle <- get_tidy_audio_analysis("4ccw2IcnFt1Jv9LqQCOYDi") %>% select(segments) %>% unnest(segments) %>% mutate(pitches = map(pitches, compmus_normalise, "manhattan")) compmus_long_distance(tallis, chapelle, pitches, method = "euclidean") compmus_self_similarity(tallis, pitches, method = "aitchison")
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