TJ Mahr May 2, 2017
Over the course of the longitudinal study, we re-recorded the stimuli used in our word recognition experiments. I would like double-check when the stimuli were changed in each experiment. This scripts iterates over the wav files in each year of study x experiment x dialect version and computes the durations of the files.
library(purrr)
library(dplyr, warn.conflicts = FALSE)
# Measure duration of a wav file in ms
get_wave_duration <- function(filepath) {
info <- tuneR::readWave(filepath, header = TRUE)
# milliseconds = (1000 ms per second / samples per second) * num samples
(1000 / info$sample.rate) * info$samples
}
find_waves <- function(dirpath) {
list.files(dirpath, pattern = ".wav|.WAV",
recursive = TRUE, full.names = TRUE)
}List all the wav files in each of the longitudinal study folders and compute the duration.
wav_files <- list(tp1 = "tp1", tp2 = "tp2", tp3 = "tp3") %>%
# find the files in each study, creating a data-frame for each study
map(find_waves) %>%
map(~ data_frame(file = .x)) %>%
# collapse to a single data-frame
bind_rows(.id = "study") %>%
mutate(
token = basename(file),
duration = map_dbl(file, get_wave_duration)) %>%
select(study, token, duration, file)
wav_files
#> # A tibble: 1,668 × 4
#> study token duration
#> <chr> <chr> <dbl>
#> 1 tp1 AAE_cake_811_8.wav 712.4036
#> 2 tp1 AAE_cake_812_20.wav 712.4036
#> 3 tp1 AAE_check1_16.wav 913.8549
#> 4 tp1 AAE_check1_4.wav 913.8549
#> 5 tp1 AAE_check1_8.wav 913.8549
#> 6 tp1 AAE_check2_10.wav 913.8549
#> 7 tp1 AAE_Cim_331_6.wav 712.4036
#> 8 tp1 AAE_Cim_332_32.wav 712.4036
#> 9 tp1 AAE_dog_311_12.wav 712.4036
#> 10 tp1 AAE_dog_311_34.wav 712.4036
#> # ... with 1,658 more rows, and 1 more variables: file <chr>Deduce dialect and experiment name.
wav_files <- wav_files %>%
mutate(
task = file %>% stringr::str_extract("MP|RWL"),
dialect = file %>% stringr::str_extract("AAE|SAE")) %>%
select(study, task, dialect, token, duration, file)
wav_files
#> # A tibble: 1,668 × 6
#> study task dialect token duration
#> <chr> <chr> <chr> <chr> <dbl>
#> 1 tp1 MP AAE AAE_cake_811_8.wav 712.4036
#> 2 tp1 MP AAE AAE_cake_812_20.wav 712.4036
#> 3 tp1 MP AAE AAE_check1_16.wav 913.8549
#> 4 tp1 MP AAE AAE_check1_4.wav 913.8549
#> 5 tp1 MP AAE AAE_check1_8.wav 913.8549
#> 6 tp1 MP AAE AAE_check2_10.wav 913.8549
#> 7 tp1 MP AAE AAE_Cim_331_6.wav 712.4036
#> 8 tp1 MP AAE AAE_Cim_332_32.wav 712.4036
#> 9 tp1 MP AAE AAE_dog_311_12.wav 712.4036
#> 10 tp1 MP AAE AAE_dog_311_34.wav 712.4036
#> # ... with 1,658 more rows, and 1 more variables: file <chr>For each experiment, count the number of token durations. This count will reveal whether the tokens in an experiment were normalized to have the same duration.
wav_files %>%
group_by(study, task, dialect) %>%
summarise(num_durations = n_distinct(duration))
#> Source: local data frame [12 x 4]
#> Groups: study, task [?]
#>
#> study task dialect num_durations
#> <chr> <chr> <chr> <int>
#> 1 tp1 MP AAE 3
#> 2 tp1 MP SAE 3
#> 3 tp1 RWL AAE 3
#> 4 tp1 RWL SAE 3
#> 5 tp2 MP AAE 3
#> 6 tp2 MP SAE 3
#> 7 tp2 RWL AAE 50
#> 8 tp2 RWL SAE 50
#> 9 tp3 MP AAE 3
#> 10 tp3 MP SAE 3
#> 11 tp3 RWL AAE 50
#> 12 tp3 RWL SAE 50It looks like the RWL experiments did not have normalized tokens.
Now, I would like to exclude the files that were not the main nouns used in the experiments. Our trials involved three parts: a carrier phrase, a noun, and an attention-getter phrase. For example, find the [carrier phrase] dog [noun] ... check it out [attention getter]. I'm going to clean up the token filenames and figure out a pattern that will filter out nouns versus non-nouns.
# Strip off junk from file name to see the main content of it
words <- wav_files %>%
getElement("token") %>%
stringr::str_replace_all("AAE|SAE", "") %>%
stringr::str_replace_all("\\d", "") %>%
stringr::str_replace_all("_[A-Z]_", "") %>%
stringr::str_replace_all("_", "") %>%
stringr::str_replace_all(".wav", "") %>%
unique() %>%
sort()
words
#> [1] "bear" "bee" "bell" "bep" "bread" "cake" "check"
#> [8] "Check" "cheese" "Cim" "dog" "dress" "dRl" "drum"
#> [15] "duck" "Fin" "flag" "fly" "Fun" "gek" "gift"
#> [22] "girl" "giv" "guk" "heart" "horse" "kite" "look"
#> [29] "Look" "neJ" "pan" "pear" "pen" "pum" "rice"
#> [36] "ring" "San" "See" "shirt" "shoes" "soup" "spoon"
#> [43] "Sup" "suz" "swan" "swing" "sword" "tag" "this"
#> [50] "van" "vase" "vef" "wais"
non_noun <- "check|Check|Fin|Fun|look|Look|See|this"
noun_files <- wav_files %>%
filter(!stringr::str_detect(token, non_noun))Now, there are fewer file durations per experiment.
noun_files %>%
group_by(study, task, dialect) %>%
summarise(num_durations = n_distinct(duration))
#> Source: local data frame [12 x 4]
#> Groups: study, task [?]
#>
#> study task dialect num_durations
#> <chr> <chr> <chr> <int>
#> 1 tp1 MP AAE 1
#> 2 tp1 MP SAE 1
#> 3 tp1 RWL AAE 1
#> 4 tp1 RWL SAE 1
#> 5 tp2 MP AAE 1
#> 6 tp2 MP SAE 1
#> 7 tp2 RWL AAE 48
#> 8 tp2 RWL SAE 48
#> 9 tp3 MP AAE 1
#> 10 tp3 MP SAE 1
#> 11 tp3 RWL AAE 48
#> 12 tp3 RWL SAE 48And I can get what I would like, which is the duration of the noun tokens in each experiment.
noun_files %>%
group_by(study, task, dialect) %>%
summarise(
n_durations = n_distinct(duration),
min_dur = min(duration) %>% round(1),
med_dur = median(duration) %>% round(1),
max_dur = max(duration) %>% round(1)) %>%
ungroup() %>%
knitr::kable()| study | task | dialect | n_durations | min_dur | med_dur | max_dur |
|---|---|---|---|---|---|---|
| tp1 | MP | AAE | 1 | 712.4 | 712.4 | 712.4 |
| tp1 | MP | SAE | 1 | 766.7 | 766.7 | 766.7 |
| tp1 | RWL | AAE | 1 | 566.7 | 566.7 | 566.7 |
| tp1 | RWL | SAE | 1 | 815.2 | 815.2 | 815.2 |
| tp2 | MP | AAE | 1 | 737.0 | 737.0 | 737.0 |
| tp2 | MP | SAE | 1 | 779.1 | 779.1 | 779.1 |
| tp2 | RWL | AAE | 48 | 344.5 | 572.1 | 819.5 |
| tp2 | RWL | SAE | 48 | 457.5 | 545.7 | 725.9 |
| tp3 | MP | AAE | 1 | 737.0 | 737.0 | 737.0 |
| tp3 | MP | SAE | 1 | 779.1 | 779.1 | 779.1 |
| tp3 | RWL | AAE | 48 | 344.5 | 572.1 | 819.5 |
| tp3 | RWL | SAE | 48 | 457.5 | 545.7 | 725.9 |
Okay, that confirms what I thought. The stimuli were re-recorded after TP1, so the same files are used in TP2 and TP3.