In jaburgoyne/compmus: Support Functions for Computational Musicology

Set-up

library(tidyverse)
library(spotifyr)
library(compmus)

In order for the code below to run, it is also necessary to set up Spotify login credentials for spotifyr.

Tonal Templates

The focus of the readings this week were chord and key estimation. One set of standard templates is below: 1--0 coding for the chord templates and the Krumhansl--Kessler key profiles.

circshift <- function(v, n) {if (n == 0) v else c(tail(v, n), head(v, -n))}

    # C     C#    D     Eb    E     F     F#    G     Ab    A     Bb    B 
major_chord <- 
    c(1,    0,    0,    0,    1,    0,    0,    1,    0,    0,    0,    0)
minor_chord <- 
    c(1,    0,    0,    1,    0,    0,    0,    1,    0,    0,    0,    0)
seventh_chord <- 
    c(1,    0,    0,    0,    1,    0,    0,    1,    0,    0,    1,    0)

major_key <- 
    c(6.35, 2.23, 3.48, 2.33, 4.38, 4.09, 2.52, 5.19, 2.39, 3.66, 2.29, 2.88)
minor_key <-
    c(6.33, 2.68, 3.52, 5.38, 2.60, 3.53, 2.54, 4.75, 3.98, 2.69, 3.34, 3.17)

chord_templates <-
    tribble(
        ~name  , ~template,
        'Gb:7'  , circshift(seventh_chord,  6),
        'Gb:maj', circshift(major_chord,    6),
        'Bb:min', circshift(minor_chord,   10),
        'Db:maj', circshift(major_chord,    1),
        'F:min' , circshift(minor_chord,    5),
        'Ab:7'  , circshift(seventh_chord,  8),
        'Ab:maj', circshift(major_chord,    8),
        'C:min' , circshift(minor_chord,    0),
        'Eb:7'  , circshift(seventh_chord,  3),
        'Eb:maj', circshift(major_chord,    3),
        'G:min' , circshift(minor_chord,    7),
        'Bb:7'  , circshift(seventh_chord, 10),
        'Bb:maj', circshift(major_chord,   10),
        'D:min' , circshift(minor_chord,    2),
        'F:7'   , circshift(seventh_chord,  5),
        'F:maj' , circshift(major_chord,    5),
        'A:min' , circshift(minor_chord,    9),
        'C:7'   , circshift(seventh_chord,  0),
        'C:maj' , circshift(major_chord,    0),
        'E:min' , circshift(minor_chord,    4),
        'G:7'   , circshift(seventh_chord,  7),
        'G:maj' , circshift(major_chord,    7),
        'B:min' , circshift(minor_chord,   11),
        'D:7'   , circshift(seventh_chord,  2),
        'D:maj' , circshift(major_chord,    2),
        'F#:min', circshift(minor_chord,    6),
        'A:7'   , circshift(seventh_chord,  9),
        'A:maj' , circshift(major_chord,    9),
        'C#:min', circshift(minor_chord,    1),
        'E:7'   , circshift(seventh_chord,  4),
        'E:maj' , circshift(major_chord,    4),
        'G#:min', circshift(minor_chord,    8),
        'B:7'   , circshift(seventh_chord, 11),
        'B:maj' , circshift(major_chord,   11),
        'D#:min', circshift(minor_chord,    3),
)

key_templates <-
    tribble(
        ~name    , ~template,
        'Gb:maj', circshift(major_key,  6),
        'Bb:min', circshift(minor_key, 10),
        'Db:maj', circshift(major_key,  1),
        'F:min' , circshift(minor_key,  5),
        'Ab:maj', circshift(major_key,  8),
        'C:min' , circshift(minor_key,  0),
        'Eb:maj', circshift(major_key,  3),
        'G:min' , circshift(minor_key,  7),
        'Bb:maj', circshift(major_key, 10),
        'D:min' , circshift(minor_key,  2),
        'F:maj' , circshift(major_key,  5),
        'A:min' , circshift(minor_key,  9),
        'C:maj' , circshift(major_key,  0),
        'E:min' , circshift(minor_key,  4),
        'G:maj' , circshift(major_key,  7),
        'B:min' , circshift(minor_key, 11),
        'D:maj' , circshift(major_key,  2),
        'F#:min', circshift(minor_key,  6),
        'A:maj' , circshift(major_key,  9),
        'C#:min', circshift(minor_key,  1),
        'E:maj' , circshift(major_key,  4),
        'G#:min', circshift(minor_key,  8),
        'B:maj' , circshift(major_key, 11),
        'D#:min', circshift(minor_key,  3))

Chordograms and Keygrams

Armed with these templates, we can make chordograms and keygrams for individual pieces. Similar to previous weeks, we start by choosing a level of hierarchy and then summarise the chroma features a that level. Higher levels like section are more appropriate for key profiles; lower levels like beat are more appropriate for chord profiles.

The following code fetches the analysis for Zager and Evans's 'In the Year 2525' (1969).

twenty_five <- 
    get_tidy_audio_analysis('5UVsbUV0Kh033cqsZ5sLQi') %>% 
    compmus_align(sections, segments) %>% 
    select(sections) %>% unnest(sections) %>% 
    mutate(
        pitches = 
            map(segments, 
                compmus_summarise, pitches, 
                method = 'mean', norm = 'manhattan'))

The new helper function compmus_match_pitch_template compares the averaged chroma vectors against templates to yield a chordo- or keygram. The two truck-driver modulations from G-sharp minor through A minor to B-flat minor are clear.

twenty_five %>% 
    compmus_match_pitch_template(key_templates, 'euclidean', 'manhattan') %>% 
    ggplot(
        aes(x = start + duration / 2, width = duration, y = name, fill = d)) +
    geom_tile() +
    scale_fill_viridis_c(option = 'E', guide = 'none') +
    theme_minimal() +
    labs(x = 'Time (s)', y = '')

Once you have the code running, try the following adaptations.

Try summarising the track at different levels of hierarchy (beats, bars, sections), as well as different combinations of summarisation methods and norms, just as previous weeks. The table below repeats the combinations we have considered.
Try making a chordogram instead of the keygram above.
Replace the key profiles above with Temperley's proposed improvements from the reading this week. (Don't forget to re-run the chunk after you are finished.) Do the revised profiles work any better? Can you think of a way to improve the chord profiles, too?

| Domain | Normalisation | Distance | Summary Statistic | | ----------------------------|---------------|-----------|-------------------| | Non-negative (e.g., chroma) | Manhattan | Manhattan | mean | | | | Aitchison | Aitchison centre | | | Euclidean | cosine | root mean square | | | | angular | root mean square | | | Chebyshev | [none] | max | | Full-range (e.g., timbre) | [none] | Euclidean | mean | | | Euclidean | cosine | root mean square | | | | angular | root mean square |

Track-Level Summaries

Several students have asked how to incorporate the low-level audio analysis features at the playlist level. Here is one strategy for doing so, which we will extend next week. As an example, let's consider the difference between Spotify's 'Sound of' playlists for bebop and big band.

After loading the playlists, we can use the helper function add_audio_analysis to fetch the low-level features for every track. Adding audio analysis for every track is a slow operation, and so for the purposes of this exercise, we will limit ourselves to 30 tracks from each playlist. The results makes heavy use of list-columns, which are discussed in more detail in the optional purrr exercise on DataCamp.

bebop <-
    get_playlist_audio_features(
        'thesoundsofspotify', 
        '55s8gstHcaCyfU47mQgLrB') %>% 
    slice(1:30) %>% 
    add_audio_analysis()
bigband <-
    get_playlist_audio_features(
        'thesoundsofspotify', 
        '2cjIvuw4VVOQSeUAZfNiqY') %>% 
    slice(1:30) %>% 
    add_audio_analysis()
jazz <-
    bebop %>% mutate(genre = "Bebop") %>%
    bind_rows(bigband %>% mutate(genre = "Big Band"))

For non-vector features, we can use the summarise_at command to collect summary statistics like mean and standard deviation.

jazz %>% 
    mutate(
        sections = 
            map(
                sections, 
                summarise_at, 
                vars(tempo, loudness, duration), 
                list(section_mean = mean, section_sd = sd))) %>% 
    unnest(sections) %>%
    ggplot(
        aes(
            x = tempo, 
            y = tempo_section_sd, 
            colour = genre, 
            alpha = loudness)) +
    geom_point(aes(size = duration / 60)) + 
    geom_rug() + 
    theme_minimal() +
    ylim(0, 5) + 
    labs(
        x = 'Mean Tempo (bpm)', 
        y = 'SD Tempo', 
        colour = 'Genre', 
        size = 'Duration (min)', 
        alpha = 'Volume (dBFS)')

When working with vector-valued features like chroma or timbre, we need to use functions from the previous weeks. Here is an example of comparing average timbre coefficients in bebop and big band. Coefficient 6 looks like the most promising marker distinguishing these genres, but we should verify that with cepstrograms and listening tests of specific pieces, supported by the Spotify documentation for its timbre features.

jazz %>% 
    mutate(
        timbre =
            map(
                segments,
                compmus_summarise,
                timbre,
                method = 'mean')) %>%
    select(genre, timbre) %>% 
    compmus_gather_timbre %>% 
    ggplot(aes(x = basis, y = value, fill = genre)) +
    geom_violin() +
    scale_fill_viridis_d() +
    labs(x = 'Spotify Timbre Coefficients', y = '', fill = 'Genre')