Package 'voice' reference manual

Title:	Tools for Voice Analysis, Speaker Recognition and Mood Inference
Description:	Tools for voice analysis, speaker recognition and mood inference.
Authors:	Zabala Filipe J. [cre, aut]
Maintainer:	Zabala Filipe J. <[email protected]>
License:	GPL-3
Version:	0.4.25
Built:	2024-11-06 05:12:45 UTC
Source:	https://github.com/filipezabala/voice

Returns the total time of audio files in seconds

Description

Returns the total time of audio files in seconds

Usage

audio_time(x, filesRange = NULL, recursive = FALSE)
audio_time(x, filesRange = NULL, recursive = FALSE)

Arguments

`x`	Either a WAV file or a directory containing WAV files.
`filesRange`	The desired range of directory files (default: `NULL`, i.e., all files).
`recursive`	Logical. Should the listing recursively into directories? (default: `FALSE`) Used by `base::list.files`.

Value

A tibble containing file name <chr> and audio time <dbl> in seconds.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

# Tibble containing file name and audio time
(at <- voice::audio_time(unique(dirname(path2wav))))
str(at)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

# Tibble containing file name and audio time
(at <- voice::audio_time(unique(dirname(path2wav))))
str(at)

Who spoke when?

Description

Diarization of WAV audios.

Usage

diarize(
  fromWav,
  toRttm = NULL,
  autoDir = FALSE,
  pycall = "~/miniconda3/envs/pyvoice/bin/python",
  token = NULL
)
diarize(
  fromWav,
  toRttm = NULL,
  autoDir = FALSE,
  pycall = "~/miniconda3/envs/pyvoice/bin/python",
  token = NULL
)

Arguments

`fromWav`	Either a file or a directory containing WAV files.
`toRttm`	A directory to write RTTM files. If the default `toRttm = NULL` is used, `'./voiceAudios/rttm'` is created and used.
`autoDir`	Logical. Must the directories tree be created? Default: `FALSE`. See 'Details'.
`pycall`	Python call. See https://github.com/filipezabala/voice for details.
`token`	Access token needed to instantiate pretrained speaker diarization pipeline from pyannote.audio. #1 Install pyannote.audio 3.1 with pip install pyannote.audio (already listed @ https://raw.githubusercontent.com/filipezabala/voice/master/requirements.txt). #2. Accept https://hf.co/pyannote/segmentation-3.0 user conditions. #3 Accept https://hf.co/pyannote-speaker-diarization-3.1 user conditions. #4. Create access token at https://hf.co/settings/tokens.

Details

When autoDir = TRUE, the following directories are created: '../mp3','../rttm', '../split' and '../musicxml'. Use getwd() to find the parent directory '../'.

Value

RTTM files in NIST standard. See 'voice::read_rttm'.

Examples

## Not run: 
library(voice)

wavDir <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

voice::diarize(fromWav = unique(dirname(wavDir)),
toRttm = tempdir(),
token = NULL) # Must enter a token! See documentation.

(rttm <- dir(tempdir(), '.[Rr][Tt][Tt][Mm]$', full.names = TRUE))
file.info(rttm)

## End(Not run)
## Not run: 
library(voice)

wavDir <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

voice::diarize(fromWav = unique(dirname(wavDir)),
toRttm = tempdir(),
token = NULL) # Must enter a token! See documentation.

(rttm <- dir(tempdir(), '.[Rr][Tt][Tt][Mm]$', full.names = TRUE))
file.info(rttm)

## End(Not run)

Duration of sequences

Description

Duration of sequences

Usage

duration(x, windowShift = 5)
duration(x, windowShift = 5)

Arguments

`x`	A vector containing symbols and `NA`.
`windowShift`	Window shift to duration in ms (default: 5.0).

Value

A data frame with duration in number of lines/ocurrences (dur_line), milliseconds (dur_ms) and proportional (dur_prop).

Examples

library(voice)
duration(letters)
duration(c('a','a','a',letters,'z'))

nts <- c('NA','NA','A3','A3','A3','A3','A#3','B3','B3','C4','C4','C4','C4',
'C4','C4','C#4','C4','C4','C4','B3','A#3','NA','NA','NA','NA','NA','NA','NA',
'NA','NA','NA','NA','NA','NA','NA','NA','NA','NA','NA','D4','D4','D4','C#4',
'C#4','C#4','C4','C4','B3','B3','A#3','A#3','A3','A3','G3','G#3','G3','F#3')
duration(nts)
library(voice)
duration(letters)
duration(c('a','a','a',letters,'z'))

nts <- c('NA','NA','A3','A3','A3','A3','A#3','B3','B3','C4','C4','C4','C4',
'C4','C4','C#4','C4','C4','C4','B3','A#3','NA','NA','NA','NA','NA','NA','NA',
'NA','NA','NA','NA','NA','NA','NA','NA','NA','NA','NA','D4','D4','D4','C#4',
'C#4','C#4','C4','C4','B3','B3','A#3','A#3','A3','A3','G3','G#3','G3','F#3')
duration(nts)

Enrich RTTM files

Description

Enrich Rich Transcription Time Marked (RTTM) files obtained from 'voice::read_rttm'.

Usage

enrich_rttm(listRttm, silence.gap = 0.5, as.tibble = TRUE)
enrich_rttm(listRttm, silence.gap = 0.5, as.tibble = TRUE)

Arguments

`listRttm`	A list containing RTTM files.
`silence.gap`	The silence gap (in seconds) between adjacent words in a keyword. Rows with `tdur <= silence.gap` are removed. (default: `0.5`)
`as.tibble`	Logical. Should it return a tibble?

Value

A list containing either data frames or tibbles obtained from standard RTTM files. See 'voice::read_rttm'.

References

https://www.nist.gov/system/files/documents/itl/iad/mig/KWS15-evalplan-v05.pdf

Examples


library(voice)

url0 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
destfile0 <- paste0(tempdir(), '/sherlock0.rttm')
download.file(url0, destfile = destfile0)
url1 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock1.rttm'
destfile1 <- paste0(tempdir(), '/sherlock1.rttm')
download.file(url0, destfile = destfile1)

rttm <- voice::read_rttm(dirname(destfile0))
(er <- voice::enrich_rttm(rttm))
class(er)
lapply(er, class)

library(voice)

url0 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
destfile0 <- paste0(tempdir(), '/sherlock0.rttm')
download.file(url0, destfile = destfile0)
url1 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock1.rttm'
destfile1 <- paste0(tempdir(), '/sherlock1.rttm')
download.file(url0, destfile = destfile1)

rttm <- voice::read_rttm(dirname(destfile0))
(er <- voice::enrich_rttm(rttm))
class(er)
lapply(er, class)

Expand model

Description

Expand model given y and x variables.

Usage

expand_model(y, x, k)
expand_model(y, x, k)

Arguments

`y`	The Y variable.
`x`	The X variables.
`k`	Number of additive components.

Value

A char vector containing the expanded models.

Examples

library(voice)

expand_model('y', LETTERS[1:4], 1)
expand_model('y', LETTERS[1:4], 2)
expand_model('y', LETTERS[1:4], 3)
expand_model('y', LETTERS[1:4], 4)

# multiple models using apply functions
nx <- 10 # number of X variables to be used
models <- lapply(1:nx, expand_model, y = 'y', x = LETTERS[1:nx])
names(models) <- 1:nx
models
sum(sapply(models, length)) # total of models
library(voice)

expand_model('y', LETTERS[1:4], 1)
expand_model('y', LETTERS[1:4], 2)
expand_model('y', LETTERS[1:4], 3)
expand_model('y', LETTERS[1:4], 4)

# multiple models using apply functions
nx <- 10 # number of X variables to be used
models <- lapply(1:nx, expand_model, y = 'y', x = LETTERS[1:nx])
names(models) <- 1:nx
models
sum(sapply(models, length)) # total of models

Extract audio features

Description

Extracts features from WAV audio files.

Usage

extract_features(
  x,
  features = c("f0", "fmt", "rf", "rpf", "rcf", "rfc", "mfcc"),
  filesRange = NULL,
  sex = "u",
  windowShift = 10,
  numFormants = 8,
  numcep = 12,
  dcttype = c("t2", "t1", "t3", "t4"),
  fbtype = c("mel", "htkmel", "fcmel", "bark"),
  resolution = 40,
  usecmp = FALSE,
  mc.cores = 1,
  full.names = TRUE,
  recursive = FALSE,
  check.mono = FALSE,
  stereo2mono = FALSE,
  overwrite = FALSE,
  freq = 44100,
  round.to = NULL,
  verbose = FALSE,
  pycall = "~/miniconda3/envs/pyvoice/bin/python"
)
extract_features(
  x,
  features = c("f0", "fmt", "rf", "rpf", "rcf", "rfc", "mfcc"),
  filesRange = NULL,
  sex = "u",
  windowShift = 10,
  numFormants = 8,
  numcep = 12,
  dcttype = c("t2", "t1", "t3", "t4"),
  fbtype = c("mel", "htkmel", "fcmel", "bark"),
  resolution = 40,
  usecmp = FALSE,
  mc.cores = 1,
  full.names = TRUE,
  recursive = FALSE,
  check.mono = FALSE,
  stereo2mono = FALSE,
  overwrite = FALSE,
  freq = 44100,
  round.to = NULL,
  verbose = FALSE,
  pycall = "~/miniconda3/envs/pyvoice/bin/python"
)

Arguments

`x`	A vector containing either files or directories of audio files in WAV format.
`features`	Vector of features to be extracted. (Default: `'f0','fmt','rf','rcf','rpf','rfc','mfcc'`). Available features: `'f0','f0_mhs','f0_praat','fmt','fmt_praat','zcr','rms','gain','rfc','ac','cep','dft','css','lps','mfcc','df','pf','rf','rcf','rpf'`.
`filesRange`	The desired range of directory files (Default: `NULL`, i.e., all files). Should only be used when all the WAV files are in the same folder.
`sex`	`= <code>` set sex specific parameters where <code> = `'f'`[emale], `'m'`[ale] or `'u'`[nknown] (Default: `'u'`). Used as 'gender' by `wrassp::ksvF0`, `wrassp::forest` and `wrassp::mhsF0`.
`windowShift`	`= <dur>` set analysis window shift to <dur>ation in ms (Default: `5.0`). Used by `wrassp::ksvF0`, `wrassp::forest`, `wrassp::mhsF0`, `wrassp::zcrana`, `wrassp::rfcana`, `wrassp::acfana`, `wrassp::cepstrum`, `wrassp::dftSpectrum`, `wrassp::cssSpectrum` and `wrassp::lpsSpectrum`.
`numFormants`	`= <num>` <num>ber of formants (Default: `8`). Used by `wrassp::forest`.
`numcep`	Number of Mel-frequency cepstral coefficients (cepstra) to return (Default: `12`). Used by `tuneR::melfcc`.
`dcttype`	Type of DCT used. `'t1'` or `'t2'`, `'t3'` for HTK `'t4'` for feacalc (Default: `'t2'`). Used by `tuneR::melfcc`.
`fbtype`	Auditory frequency scale to use: `'mel'`, `'bark'`, `'htkmel'`, `'fcmel'` (Default: `'mel'`). Used by `tuneR::melfcc`.
`resolution`	`= <freq>` set FFT length to the smallest value which results in a frequency resolution of <freq> Hz or better (Default: `40.0`). Used by `wrassp::cssSpectrum`, `wrassp::dftSpectrum` and `wrassp::lpsSpectrum`.
`usecmp`	Logical. Apply equal-loudness weighting and cube-root compression (PLP instead of LPC) (Default: `FALSE`). Used by `tuneR::melfcc`.
`mc.cores`	Number of cores to be used in parallel processing. (Default: `1`)
`full.names`	Logical. If `TRUE`, the directory path is prepended to the file names to give a relative file path. If `FALSE`, the file names (rather than paths) are returned. (Default: `TRUE`) Used by `base::list.files`.
`recursive`	Logical. Should the listing recursively into directories? (Default: `FALSE`) Used by `base::list.files`.
`check.mono`	Logical. Check if the WAV file is mono. (Default: `TRUE`)
`stereo2mono`	(Experimental) Logical. Should files be converted from stereo to mono? (Default: `TRUE`)
`overwrite`	(Experimental) Logical. Should converted files be overwritten? If not, the file gets the suffix `_mono`. (Default: `FALSE`)
`freq`	Frequency in Hz to write the converted files when `stereo2mono=TRUE`. (Default: `44100`)
`round.to`	Number of decimal places to round to. (Default: `NULL`)
`verbose`	Logical. Should the running status be showed? (Default: `FALSE`)
`pycall`	Python call. See https://github.com/filipezabala/voice for details.

Details

The feature 'df' corresponds to 'formant dispersion' (df2:df8) by Fitch (1997), 'pf' to formant position' (pf1:pf8) by Puts, Apicella & Cárdena (2011), 'rf' to 'formant removal' (rf1:rf8) by Zabala (2023), 'rcf' to 'formant cumulated removal' (rcf2:rcf8) by Zabala (2023) and 'rpf' to 'formant position removal' (rpf2:rpf8) by Zabala (2023). The 'fmt_praat' feature may take long time processing. The following features may contain a variable number of columns: 'cep', 'dft', 'css' and 'lps'.

Value

A Media data frame containing the selected features.

References

Levinson N. (1946). The Wiener (root mean square) error criterion in filter design and prediction. Journal of Mathematics and Physics, 25(1-4), 261–278. (doi:10.1002/SAPM1946251261)

Durbin J. (1960). “The fitting of time-series models.” Revue de l’Institut International de Statistique, pp. 233–244. (https://www.jstor.org/stable/1401322)

Cooley J.W., Tukey J.W. (1965). “An algorithm for the machine calculation of complex Fourier series.” Mathematics of computation, 19(90), 297–301. (https://www.ams.org/journals/mcom/1965-19-090/S0025-5718-1965-0178586-1/)

Wasson D., Donaldson R. (1975). “Speech amplitude and zero crossings for automated identification of human speakers.” IEEE Transactions on Acoustics, Speech, and Signal Processing, 23(4), 390–392. (https://ieeexplore.ieee.org/document/1162690)

Allen J. (1977). “Short term spectral analysis, synthesis, and modification by discrete Fourier transform.” IEEE Transactions on Acoustics, Speech, and Signal Processing, 25(3), 235– 238. (https://ieeexplore.ieee.org/document/1162950)

Schäfer-Vincent K. (1982). "Significant points: Pitch period detection as a problem of segmentation." Phonetica, 39(4-5), 241–253. (doi:10.1159/000261665 )

Schäfer-Vincent K. (1983). "Pitch period detection and chaining: Method and evaluation." Phonetica, 40(3), 177–202. (doi:10.1159/000261691)

Ephraim Y., Malah D. (1984). “Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator.” IEEE Transactions on acoustics, speech, and signal processing, 32(6), 1109–1121. (https://ieeexplore.ieee.org/document/1164453)

Delsarte P., Genin Y. (1986). “The split Levinson algorithm.” IEEE transactions on acoustics, speech, and signal processing, 34(3), 470–478. (https://ieeexplore.ieee.org/document/1164830)

Jackson J.C. (1995). "The Harmonic Sieve: A Novel Application of Fourier Analysis to Machine Learning Theory and Practice." Technical report, Carnegie-Mellon University Pittsburgh PA Schoo; of Computer Science. (https://apps.dtic.mil/sti/pdfs/ADA303368.pdf)

Fitch, W.T. (1997) "Vocal tract length and formant frequency dispersion correlate with body size in rhesus macaques." J. Acoust. Soc. Am. 102, 1213 – 1222. (doi:10.1121/1.421048)

Boersma P., van Heuven V. (2001). Praat, a system for doing phonetics by computer. Glot. Int., 5(9/10), 341–347. (https://www.fon.hum.uva.nl/paul/papers/speakUnspeakPraat_glot2001.pdf)

Ellis DPW (2005). “PLP and RASTA (and MFCC, and inversion) in Matlab.” Online web resource. (https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/)

Puts, D.A., Apicella, C.L., Cardenas, R.A. (2012) "Masculine voices signal men's threat potential in forager and industrial societies." Proc. R. Soc. B Biol. Sci. 279, 601–609. (doi:10.1098/rspb.2011.0829)

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# minimal usage
M1 <- extract_features(path2wav)
M2 <- extract_features(dirname(path2wav))
identical(M1,M2)
table(basename(M1$wav_path))

# limiting filesRange
M3 <- extract_features(path2wav, filesRange = 3:6)
table(basename(M3$wav_path))
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# minimal usage
M1 <- extract_features(path2wav)
M2 <- extract_features(dirname(path2wav))
identical(M1,M2)
table(basename(M1$wav_path))

# limiting filesRange
M3 <- extract_features(path2wav, filesRange = 3:6)
table(basename(M3$wav_path))

Features summary

Description

Returns summary measures of 'voice::extract_features'.

Usage

feat_summary(
  x,
  groupBy = "wav_path",
  wavPath = unique(x$wav_path),
  wavPathName = "wav_path",
  features = "f0",
  filesRange = NULL,
  sex = "u",
  windowShift = 10,
  numFormants = 8,
  numcep = 12,
  dcttype = c("t2", "t1", "t3", "t4"),
  fbtype = c("mel", "htkmel", "fcmel", "bark"),
  resolution = 40,
  usecmp = FALSE,
  mc.cores = 1,
  full.names = TRUE,
  recursive = FALSE,
  check.mono = FALSE,
  stereo2mono = FALSE,
  overwrite = FALSE,
  freq = 44100,
  round.to = 4,
  verbose = FALSE
)
feat_summary(
  x,
  groupBy = "wav_path",
  wavPath = unique(x$wav_path),
  wavPathName = "wav_path",
  features = "f0",
  filesRange = NULL,
  sex = "u",
  windowShift = 10,
  numFormants = 8,
  numcep = 12,
  dcttype = c("t2", "t1", "t3", "t4"),
  fbtype = c("mel", "htkmel", "fcmel", "bark"),
  resolution = 40,
  usecmp = FALSE,
  mc.cores = 1,
  full.names = TRUE,
  recursive = FALSE,
  check.mono = FALSE,
  stereo2mono = FALSE,
  overwrite = FALSE,
  freq = 44100,
  round.to = 4,
  verbose = FALSE
)

Arguments

`x`	An Extended data frame to be tagged with media information.
`groupBy`	A variable to group the summary measures. The argument must be a character vector. (Default: `groupBy = 'wav_path'`).
`wavPath`	A vector containing the path(s) to WAV files. May be both as `dirname` or `basename` formats.
`wavPathName`	A string containing the WAV path name. (Default: `wavPathName = 'wav_path'`).
`features`	Vector of features to be extracted. (Default: `'f0'`).
`filesRange`	The desired range of directory files (default: `NULL`, i.e., all files). Should only be used when all the WAV files are in the same folder.
`sex`	`= <code>` set sex specific parameters where <code> = `'f'`[emale], `'m'`[ale] or `'u'`[nknown] (Default: `'u'`). Used as 'gender' by `wrassp::ksvF0`, `wrassp::forest` and `wrassp::mhsF0`.
`windowShift`	`= <dur>` set analysis window shift to <dur>ation in ms (Default: `5.0`). Used by `wrassp::ksvF0`, `wrassp::forest`, `wrassp::mhsF0`, `wrassp::zcrana`, `wrassp::rfcana`, `wrassp::acfana`, `wrassp::cepstrum`, `wrassp::dftSpectrum`, `wrassp::cssSpectrum` and `wrassp::lpsSpectrum`.
`numFormants`	`= <num>` <num>ber of formants (Default: `8`). Used by `wrassp::forest`.
`numcep`	Number of Mel-frequency cepstral coefficients (cepstra) to return (Default: `12`). Used by `tuneR::melfcc`.
`dcttype`	Type of DCT used. `'t1'` or `'t2'`, `'t3'` for HTK `'t4'` for feacalc (Default: `'t2'`). Used by `tuneR::melfcc`.
`fbtype`	Auditory frequency scale to use: `'mel'`, `'bark'`, `'htkmel'`, `'fcmel'` (Default: `'mel'`). Used by `tuneR::melfcc`.
`resolution`	`= <freq>` set FFT length to the smallest value which results in a frequency resolution of <freq> Hz or better (Default: `40.0`). Used by `wrassp::cssSpectrum`, `wrassp::dftSpectrum` and `wrassp::lpsSpectrum`.
`usecmp`	Logical. Apply equal-loudness weighting and cube-root compression (PLP instead of LPC) (Default: `FALSE`). Used by `tuneR::melfcc`.
`mc.cores`	Number of cores to be used in parallel processing. (Default: `1`)
`full.names`	Logical. If `TRUE`, the directory path is prepended to the file names to give a relative file path. If `FALSE`, the file names (rather than paths) are returned. (Default: `TRUE`). Used by `base::list.files`.
`recursive`	Logical. Should the listing recursively into directories? (Default: `FALSE`) Used by `base::list.files`.
`check.mono`	Logical. Check if the WAV file is mono. (Default: `TRUE`)
`stereo2mono`	(Experimental) Logical. Should files be converted from stereo to mono? (Default: `TRUE`)
`overwrite`	(Experimental) Logical. Should converted files be overwritten? If not, the file gets the suffix `_mono`. (Default: `FALSE`)
`freq`	Frequency in Hz to write the converted files when `stereo2mono=TRUE`. (Default: `44100`)
`round.to`	Number of decimal places to round to. (Default: `NULL`)
`verbose`	Logical. Should the running status be showed? (Default: `FALSE`)

Details

filesRange should only be used when all the WAV files are in the same folder.

Value

A tibble data frame containing summarized numeric columns using (1) mean, (2) standard deviation, (3) variation coefficient, (4) median, (5) interquartile range and (6) median absolute deviation.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# creating Extended synthetic data
E <- dplyr::tibble(subject_id = c(1,1,1,2,2,2,3,3,3),
wav_path = path2wav)

# minimal usage
feat_summary(E)

# canonical data
feat_summary(E, groupBy = 'subject_id')
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# creating Extended synthetic data
E <- dplyr::tibble(subject_id = c(1,1,1,2,2,2,3,3,3),
wav_path = path2wav)

# minimal usage
feat_summary(E)

# canonical data
feat_summary(E, groupBy = 'subject_id')

Get bit rate

Description

Get bit rate from WAV file.

Usage

get_bit(x)
get_bit(x)

Arguments

`x`	Wave object from 'tuneR::readWave'.

Value

Integer indicating the bit rate from a WAV file.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
voice::get_bit(rw)

rwl <- lapply(path2wav, tuneR::readWave)
sapply(rwl, voice::get_bit)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
voice::get_bit(rw)

rwl <- lapply(path2wav, tuneR::readWave)
sapply(rwl, voice::get_bit)

Time duration

Description

Get time duration from WAV file.

Usage

get_dur(x)
get_dur(x)

Arguments

`x`	Wave object from 'tuneR::readWave'.

Value

Numeric indicating the time duration in seconds from a WAV file.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
voice::get_dur(rw)

rwl <- lapply(path2wav, tuneR::readWave)
sapply(rwl, voice::get_dur)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
voice::get_dur(rw)

rwl <- lapply(path2wav, tuneR::readWave)
sapply(rwl, voice::get_dur)

Get left channel

Description

Get left channel from WAV file.

Usage

get_left(x)
get_left(x)

Arguments

`x`	Wave object from 'tuneR::readWave'.

Value

Numeric vector indicating the left channel from a WAV file.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
l <- voice::get_left(rw)
head(l)
length(l)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
l <- voice::get_left(rw)
head(l)
length(l)

Get right channel

Description

Get right channel from WAV file.

Usage

get_right(x)
get_right(x)

Arguments

`x`	Wave object from 'tuneR::readWave'.

Value

Numeric vector indicating the right channel from a WAV file.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
r <- voice::get_right(rw)
head(r)
length(r)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
r <- voice::get_right(rw)
head(r)
length(r)

Get sample rate

Description

Get sample rate from WAV file.

Usage

get_samp.rate(x)
get_samp.rate(x)

Arguments

`x`	Wave object from 'tuneR::readWave'.

Value

Integer indicating the sample rate from a WAV file.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
voice::get_samp.rate(rw)

rwl <- lapply(path2wav, tuneR::readWave)
sapply(rwl, voice::get_samp.rate)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern <- glob2rx('*.wav'), full.names = TRUE)

rw <- tuneR::readWave(path2wav[1])
voice::get_samp.rate(rw)

rwl <- lapply(path2wav, tuneR::readWave)
sapply(rwl, voice::get_samp.rate)

Time beginning

Description

Get time beginning from a data frame in RTTM standard.

Usage

get_tbeg(x)
get_tbeg(x)

Arguments

`x`	A data frame in RTTM standard. See 'voice::read_rttm'.

Value

Numeric vector containing the time beginning in seconds.

Examples

library(voice)

url0 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
download.file(url0, destfile = paste0(tempdir(), '/sherlock0.rttm'))

rttm <- voice::read_rttm(tempdir())
(gtb <- voice::get_tbeg(rttm$sherlock0.rttm))
class(gtb)
library(voice)

url0 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
download.file(url0, destfile = paste0(tempdir(), '/sherlock0.rttm'))

rttm <- voice::read_rttm(tempdir())
(gtb <- voice::get_tbeg(rttm$sherlock0.rttm))
class(gtb)

Time duration

Description

Get time duration from a data frame in RTTM standard.

Usage

get_tdur(x)
get_tdur(x)

Arguments

`x`	A data frame in RTTM standard. See 'voice::read_rttm'.

Value

Numeric vector containing the time duration in seconds.

Examples

library(voice)

url0 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
download.file(url0, destfile = paste0(tempdir(), '/sherlock0.rttm'))

rttm <- voice::read_rttm(tempdir())
(gtd <- voice::get_tdur(rttm$sherlock0.rttm))
class(gtd)
library(voice)

url0 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
download.file(url0, destfile = paste0(tempdir(), '/sherlock0.rttm'))

rttm <- voice::read_rttm(tempdir())
(gtd <- voice::get_tdur(rttm$sherlock0.rttm))
class(gtd)

Interpolate vectors

Description

Interpolate vactors, compressing to compact.to fraction. May remove zeros.

Usage

interp(
  y,
  compact.to,
  drop.zeros = FALSE,
  to.data.frame = FALSE,
  round.off = NULL,
  weight = NULL
)
interp(
  y,
  compact.to,
  drop.zeros = FALSE,
  to.data.frame = FALSE,
  round.off = NULL,
  weight = NULL
)

Arguments

`y`	A vector or time series.
`compact.to`	Proportion of remaining points after compaction, between (including) 0 and 1. If equals to 1 and keep.zeros = TRUE, the original vector is presented.
`drop.zeros`	Logical. Drop repeated zeros? Default: `FALSE`.
`to.data.frame`	Logical. Convert to data frame? Default: `FALSE`.
`round.off`	Number of decimal places of the interpolated `y` Default: `NULL`.
`weight`	Vector of weights with same length of `y`. Default: `NULL`.

Value

A list of interpolated x and y values with length near to compact.to*length(y).

Examples

library(voice)

v1 <- 1:100
(c1 <- interp(v1, compact.to = 0.2))
length(c1$y)
plot(1:100, type = 'l')
points(c1$x, c1$y, col='red')

# with weight
(c2 <- interp(v1, compact.to = 0.2, weight = rev(v1)))
plot(c1$y)
points(c2$y, col = 'red')

(v2 <- c(1:5, rep(0,10), 1:10, rep(0,5), 10:20, rep(0,10)))
length(v2)
interp(v2, 0.1, drop.zeros = TRUE, to.data.frame = FALSE)
interp(v2, 0.1, drop.zeros = TRUE, to.data.frame = TRUE)
interp(v2, 0.2, drop.zeros = TRUE)
interp(v2, 0.2, drop.zeros = FALSE)

(v3 <- c(rep(0,10), 1:20, rep(0,3)))
(c3 <- interp(v3, 1/3, drop.zeros = FALSE, to.data.frame = FALSE))
lapply(c3, length)
plot(v3, type = 'l')
points(c3$x, c3$y, col = 'red')

(v4 <- c(rnorm(1:100)))
(c4 <- interp(v4, 1/4, round.off = 3))
library(voice)

v1 <- 1:100
(c1 <- interp(v1, compact.to = 0.2))
length(c1$y)
plot(1:100, type = 'l')
points(c1$x, c1$y, col='red')

# with weight
(c2 <- interp(v1, compact.to = 0.2, weight = rev(v1)))
plot(c1$y)
points(c2$y, col = 'red')

(v2 <- c(1:5, rep(0,10), 1:10, rep(0,5), 10:20, rep(0,10)))
length(v2)
interp(v2, 0.1, drop.zeros = TRUE, to.data.frame = FALSE)
interp(v2, 0.1, drop.zeros = TRUE, to.data.frame = TRUE)
interp(v2, 0.2, drop.zeros = TRUE)
interp(v2, 0.2, drop.zeros = FALSE)

(v3 <- c(rep(0,10), 1:20, rep(0,3)))
(c3 <- interp(v3, 1/3, drop.zeros = FALSE, to.data.frame = FALSE))
lapply(c3, length)
plot(v3, type = 'l')
points(c3$x, c3$y, col = 'red')

(v4 <- c(rnorm(1:100)))
(c4 <- interp(v4, 1/4, round.off = 3))

Inperpolate data frames

Description

Interpolate data frames using multicore, compressing to compact.to fraction. May remove zeros.

Usage

interp_df(
  x,
  compact.to,
  id = colnames(x)[1],
  colnum = NULL,
  drop.x = TRUE,
  drop.zeros = FALSE,
  to.data.frame = TRUE,
  round.off = NULL,
  weight = NULL,
  mc.cores = 1
)
interp_df(
  x,
  compact.to,
  id = colnames(x)[1],
  colnum = NULL,
  drop.x = TRUE,
  drop.zeros = FALSE,
  to.data.frame = TRUE,
  round.off = NULL,
  weight = NULL,
  mc.cores = 1
)

Arguments

`x`	A data frame.
`compact.to`	Proportion of remaining points after interpolation. If equals to 1 and keep.zeros = TRUE, the original vector is presented.
`id`	The identification column. Default: `colname` of the first column of `x`.
`colnum`	A `char` vector indicating the numeric colnames. If `NULL`, uses the columns of the `numeric` class.
`drop.x`	Logical. Drop columns containing .x? Default: `TRUE`.
`drop.zeros`	Logical. Drop repeated zeros or keep 1 zero per null set? Default: `FALSE`.
`to.data.frame`	Logical. Should return a data frame? If `FALSE` returns a list. Default: `TRUE`.
`round.off`	Number of decimal places of the interpolated `y`. Default: `NULL`.
`weight`	Vector of weights with same length of `y`. Default: `NULL`.
`mc.cores`	The number of cores to mclapply. Default: `1`.

Value

A data frame of interpolated values with nrow near to compact.to*length(x).

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# getting Media data frame via lean call
M <- extract_features(dirname(path2wav), features = c('f0','fmt'),
mc.cores = 1, verbose = FALSE)


(cM.df <- interp_df(M[,-(1:2)], 0.1, mc.cores = 1))
(cM.df2 <- interp_df(M[,-(1:2)], 0.1, drop.x = FALSE, mc.cores = 1))

dim(M)
dim(cM.df)
dim(cM.df2)
(cM.list <- interp_df(M[,-(1:2)], 0.1, to.data.frame = FALSE, mc.cores = 1))

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# getting Media data frame via lean call
M <- extract_features(dirname(path2wav), features = c('f0','fmt'),
mc.cores = 1, verbose = FALSE)


(cM.df <- interp_df(M[,-(1:2)], 0.1, mc.cores = 1))
(cM.df2 <- interp_df(M[,-(1:2)], 0.1, drop.x = FALSE, mc.cores = 1))

dim(M)
dim(cM.df)
dim(cM.df2)
(cM.list <- interp_df(M[,-(1:2)], 0.1, to.data.frame = FALSE, mc.cores = 1))

Interpolate vectors using multicore

Description

Interpolate vectors using multicore

Usage

interp_mc(
  y,
  compact.to,
  drop.zeros = FALSE,
  to.data.frame = FALSE,
  round.off = NULL,
  weight = NULL,
  mc.cores = 1
)
interp_mc(
  y,
  compact.to,
  drop.zeros = FALSE,
  to.data.frame = FALSE,
  round.off = NULL,
  weight = NULL,
  mc.cores = 1
)

Arguments

`y`	A numeric vector, matrix or data frame.
`compact.to`	Proportion of remaining points after compression. If equals to 1 and keep.zeros = TRUE, the original vector is presented.
`drop.zeros`	Logical. Drop repeated zeros? Default: `FALSE`.
`to.data.frame`	Logical. Convert to data frame? Default: `FALSE`.
`round.off`	Number of decimal places of the interpolated `y`. Default: `NULL`.
`weight`	Vector of weights with same length of `y`. Default: `NULL`.
`mc.cores`	The number of cores to mclapply. Default: `1`.

Value

A list of x and y convoluted values with length near to compact.to*length(y).

Examples

library(voice)
# Same result of interp() function if x is a vector
interp(1:100, compact.to = 0.1, drop.zeros = TRUE, to.data.frame = FALSE)
interp_mc(1:100, compact.to = 0.1, drop.zeros = TRUE, to.data.frame = FALSE)

interp(1:100, compact.to = 0.1, drop.zeros = TRUE, to.data.frame = TRUE)
interp_mc(1:100, compact.to = 0.1, drop.zeros = TRUE, to.data.frame = TRUE)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)


# getting Media data frame
M <- voice::extract_features(dirname(path2wav), mc.cores = 1, verbose = FALSE)

M.num <- M[,-(1:3)]
nrow(M.num)
cm1 <- interp_mc(M.num, compact.to = 0.1, drop.zeros = TRUE,
to.data.frame = FALSE, mc.cores = 1)
names(cm1)
lapply(cm1$f0, length)

library(voice)
# Same result of interp() function if x is a vector
interp(1:100, compact.to = 0.1, drop.zeros = TRUE, to.data.frame = FALSE)
interp_mc(1:100, compact.to = 0.1, drop.zeros = TRUE, to.data.frame = FALSE)

interp(1:100, compact.to = 0.1, drop.zeros = TRUE, to.data.frame = TRUE)
interp_mc(1:100, compact.to = 0.1, drop.zeros = TRUE, to.data.frame = TRUE)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)


# getting Media data frame
M <- voice::extract_features(dirname(path2wav), mc.cores = 1, verbose = FALSE)

M.num <- M[,-(1:3)]
nrow(M.num)
cm1 <- interp_mc(M.num, compact.to = 0.1, drop.zeros = TRUE,
to.data.frame = FALSE, mc.cores = 1)
names(cm1)
lapply(cm1$f0, length)

Verify if an audio is mono

Description

Verify if an audio is mono

Usage

is_mono(x)
is_mono(x)

Arguments

`x`	Path to WAV audio file.

Value

Logical. 'TRUE' indicates a mono (one-channel) file. 'FALSE' indicates a non-mono (two-channel) file.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

is_mono(path2wav[1])
sapply(path2wav, is_mono)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

is_mono(path2wav[1])
sapply(path2wav, is_mono)

Sample IDs and paths

Description

A dataset containing sample IDs and paths from Ardila et al (2019) 'Common voice: A massively-multilingual speech corpus', used in Zabala (2023) 'voice: new approaches to audio analysis'. The considered sample contains 34,425 rows associated with 838 IDs (p_s = 2.4%).

Usage

mozilla_id_path
mozilla_id_path

References

Ardila R, Branson M, Davis K, Henretty M, Kohler M, Meyer J, Morais R, Saunders L, Tyers FM, Weber G (2019). "Common voice: A massively-multilingual speech corpus." arXiv preprint arXiv:1912.06670. URL https://arxiv.org/abs/1912.06670.

Examples

library(voice)
mozilla_id_path
library(voice)
mozilla_id_path

Assign notes to frequencies

Description

Returns a vector of notes for equal-tempered scale, A4 = 440 Hz.

Usage

notes(x, method = "spn", moving.average = FALSE, k = 11)
notes(x, method = "spn", moving.average = FALSE, k = 11)

Arguments

`x`	Numeric vector of frequencies in Hz.
`method`	Method of specifying musical pitch. (Default: `spn`, i.e., Scientific Pitch Notation).
`moving.average`	Logical. Must apply moving average? (Default: `FALSE`).
`k`	Integer width of the rolling window used if moving.average is TRUE. (Default: `11`).

Details

The symbol '#' is being used to represent a sharp note, the higher in pitch by one semitone on Scientific Pitch Notation (SPN).

Value

A vector containing the notes for equal-tempered scale, A4 = 440 Hz. When ‘method = ’spn'‘ the vector is of class ’ordered factor'. When ‘method = ’octave'‘ the vector is of class ’factor'. When ‘method = ’midi'‘ the vector is of class ’integer'.

References

https://pages.mtu.edu/~suits/notefreqs.html

Examples

library(voice)
notes(c(220,440,880))
notes(c(220,440,880), method = 'octave')
notes(c(220,440,880), method = 'midi')
library(voice)
notes(c(220,440,880))
notes(c(220,440,880), method = 'octave')
notes(c(220,440,880), method = 'midi')

Frequencies on Scientific Pitch Notation (SPN)

Description

Returns a tibble of frequencies on Scientific Pitch Notation (SPN) for equal-tempered scale, A4 = 440 Hz.

Usage

notes_freq()
notes_freq()

Details

The symbol '#' is being used to represent a sharp note, the higher in pitch by one semitone. The SPN is also known as American Standard Pitch Notation (ASPN) or International Pitch Notation (IPN).

Value

A tibble with frequencies for equal-tempered scale, A4 = 440 Hz.

References

https://pages.mtu.edu/~suits/notefreqs.html

Examples

library(voice)
notes_freq()
library(voice)
notes_freq()

Read RTTM files

Description

Read Rich Transcription Time Marked (RTTM) files in fromRttm directory.

Usage

read_rttm(fromRttm)
read_rttm(fromRttm)

Arguments

fromRttm

A directory/folder containing RTTM files.

Details

The Rich Transcription Time Marked (RTTM) files are space-delimited text files containing one turn per line defined by NIST - National Institute of Standards and Technology. Each line containing ten fields:

type Type: segment type; should always by SPEAKER.

file File ID: file name; basename of the recording minus extension (e.g., rec1_a).

chnl Channel ID: channel (1-indexed) that turn is on; should always be 1.

tbeg Turn Onset – onset of turn in seconds from beginning of recording.

tdur Turn Duration – duration of turn in seconds.

ortho Orthography Field – should always by <NA>.

stype Speaker Type – should always be <NA>.

name Speaker Name – name of speaker of turn; should be unique within scope of each file.

conf Confidence Score – system confidence (probability) that information is correct; should always be <NA>.

slat Signal Lookahead Time – should always be <NA>.

Value

A list containing data frames obtained from standard RTTM files. See 'Details'.

References

https://www.nist.gov/system/files/documents/itl/iad/mig/KWS15-evalplan-v05.pdf

Examples

library(voice)

url0 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
download.file(url0, destfile = paste0(tempdir(), '/sherlock0.rttm'))
url1 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock1.rttm'
download.file(url0, destfile = paste0(tempdir(), '/sherlock1.rttm'))

(rttm <- voice::read_rttm(tempdir()))
class(rttm)
lapply(rttm, class)
library(voice)

url0 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
download.file(url0, destfile = paste0(tempdir(), '/sherlock0.rttm'))
url1 <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock1.rttm'
download.file(url0, destfile = paste0(tempdir(), '/sherlock1.rttm'))

(rttm <- voice::read_rttm(tempdir()))
class(rttm)
lapply(rttm, class)

Compress zeros.

Description

Transforms n sets of m>n zeros (alternated with sets of non zeros) into n sets of n zeros.

Usage

rm0(y)
rm0(y)

Arguments

`y`	A vector or time series.

Value

Vector with n zeros.

Examples

library(voice)

(v0 <- c(1:20,rep(0,10)))
(r0 <- rm0(v0))
length(v0)
length(r0)
sum(v0 == 0)

(v1 <- c(rep(0,10),1:20))
(r1 <- rm0(v1))
length(r1)

(v2 <- rep(0,10))
(r2 <- rm0(v2))
length(r2)

(v3 <- c(0:10))
(r3 <- rm0(v3))
length(r3)

(v4 <- c(rep(0,10), 1:10, rep(0,5), 10:20, rep(0,10)))
(r4 <- rm0(v4))
length(r4)
sum(v4 == 0)
library(voice)

(v0 <- c(1:20,rep(0,10)))
(r0 <- rm0(v0))
length(v0)
length(r0)
sum(v0 == 0)

(v1 <- c(rep(0,10),1:20))
(r1 <- rm0(v1))
length(r1)

(v2 <- rep(0,10))
(r2 <- rm0(v2))
length(r2)

(v3 <- c(0:10))
(r3 <- rm0(v3))
length(r3)

(v4 <- c(rep(0,10), 1:10, rep(0,5), 10:20, rep(0,10)))
(r4 <- rm0(v4))
length(r4)
sum(v4 == 0)

Smooth numeric variables in a data frame

Description

Smooth numeric variables in a data frame

Usage

smooth_df(x, k = 11, id = colnames(x)[1], colnum = NULL, mc.cores = 1)
smooth_df(x, k = 11, id = colnames(x)[1], colnum = NULL, mc.cores = 1)

Arguments

`x`	A data frame.
`k`	Integer width of the rolling window. Default: `11`.
`id`	The identification column. Default: `colname` of the first column of `x`.
`colnum`	A `char` vector indicating the numeric colnames. If `NULL`, uses the columns of the `numeric` class.
`mc.cores`	The number of cores to mclapply. By default uses `1`.

Value

Vector of interpolated values with length near to compact.to*length(x).

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# minimal usage
M <- extract_features(path2wav, features = c('f0', 'fmt'))
(Ms <- smooth_df(M[-(1:2)]))
dim(M)
dim(Ms)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# minimal usage
M <- extract_features(path2wav, features = c('f0', 'fmt'))
(Ms <- smooth_df(M[-(1:2)]))
dim(M)
dim(Ms)

Split Wave

Description

Split WAV files either in fromWav directory or using (same names) RTTM files/subdirectories as guidance.

Usage

splitw(
  fromWav,
  fromRttm = NULL,
  toSplit = NULL,
  autoDir = FALSE,
  subDir = FALSE,
  output = "wave",
  filesRange = NULL,
  full.names = TRUE,
  recursive = FALSE,
  silence.gap = 0.5
)
splitw(
  fromWav,
  fromRttm = NULL,
  toSplit = NULL,
  autoDir = FALSE,
  subDir = FALSE,
  output = "wave",
  filesRange = NULL,
  full.names = TRUE,
  recursive = FALSE,
  silence.gap = 0.5
)

Arguments

`fromWav`	Either WAV file or directory containing WAV files.
`fromRttm`	Either RTTM file or directory containing RTTM files. Default: `NULL`.
`toSplit`	A directory to write generated files. Default: `NULL`.
`autoDir`	Logical. Must the directories tree be created? Default: `FALSE`. See 'Details'.
`subDir`	Logical. Must the splitted files be placed in subdirectories? Default: `FALSE`.
`output`	Character string, the class of the object to return, either 'wave' or 'list'.
`filesRange`	The desired range of directory files (default: `NULL`, i.e., all files). Must be TRUE only if `fromWav` is a directory.
`full.names`	Logical. If `TRUE`, the directory path is prepended to the file names to give a relative file path. If `FALSE`, the file names (rather than paths) are returned. (default: `TRUE`) Used by `base::list.files`.
`recursive`	Logical. Should the listing recursively into directories? (default: `FALSE`) Used by `base::list.files`. Inactive if `fromWav` is a file.
`silence.gap`	The silence gap (in seconds) between adjacent words in a keyword. Rows with `tdur <= silence.gap` are removed. (default: `0.5`)

Details

When autoDir = TRUE, the following directories are created: '../mp3','../rttm', '../split' and '../musicxml'. Use getwd() to find the parent directory '../'.

Value

Splited audio files according to the correspondent RTTM file(s). See 'voice::diarize'.

Examples

## Not run: 
library(voice)

urlWav <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/wav/sherlock0.wav'
destWav <- paste0(tempdir(), '/sherlock0.wav')
download.file(urlWav, destfile = destWav)

urlRttm <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
destRttm <- paste0(tempdir(), '/sherlock0.rttm')
download.file(urlRttm, destfile = destRttm)

splitDir <- paste0(tempdir(), '/split')
dir.create(splitDir)
splitw(destWav, fromRttm = destRttm, toSplit = splitDir)

dir(splitDir)

## End(Not run)
## Not run: 
library(voice)

urlWav <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/wav/sherlock0.wav'
destWav <- paste0(tempdir(), '/sherlock0.wav')
download.file(urlWav, destfile = destWav)

urlRttm <- 'https://raw.githubusercontent.com/filipezabala/voiceAudios/main/rttm/sherlock0.rttm'
destRttm <- paste0(tempdir(), '/sherlock0.rttm')
download.file(urlRttm, destfile = destRttm)

splitDir <- paste0(tempdir(), '/split')
dir.create(splitDir)
splitw(destWav, fromRttm = destRttm, toSplit = splitDir)

dir(splitDir)

## End(Not run)

Tag a data frame with media information

Description

Tag a data frame with media information

Usage

tag(
  x,
  groupBy = "wav_path",
  wavPath = unique(x$wav_path),
  wavPathName = "wav_path",
  tags = c("feat_summary"),
  sortByGroupBy = TRUE,
  filesRange = NULL,
  features = "f0",
  sex = "u",
  windowShift = 5,
  numFormants = 8,
  numcep = 12,
  dcttype = c("t2", "t1", "t3", "t4"),
  fbtype = c("mel", "htkmel", "fcmel", "bark"),
  resolution = 40,
  usecmp = FALSE,
  mc.cores = 1,
  full.names = TRUE,
  recursive = FALSE,
  check.mono = FALSE,
  stereo2mono = FALSE,
  overwrite = FALSE,
  freq = 44100,
  round.to = 4,
  verbose = FALSE
)
tag(
  x,
  groupBy = "wav_path",
  wavPath = unique(x$wav_path),
  wavPathName = "wav_path",
  tags = c("feat_summary"),
  sortByGroupBy = TRUE,
  filesRange = NULL,
  features = "f0",
  sex = "u",
  windowShift = 5,
  numFormants = 8,
  numcep = 12,
  dcttype = c("t2", "t1", "t3", "t4"),
  fbtype = c("mel", "htkmel", "fcmel", "bark"),
  resolution = 40,
  usecmp = FALSE,
  mc.cores = 1,
  full.names = TRUE,
  recursive = FALSE,
  check.mono = FALSE,
  stereo2mono = FALSE,
  overwrite = FALSE,
  freq = 44100,
  round.to = 4,
  verbose = FALSE
)

Arguments

`x`	An Extended data frame to be tagged with media information. See references.
`groupBy`	A variable to group the summary measures. The argument must be a character vector. (Default: `groupBy = 'wav_path'`).
`wavPath`	A vector containing the path(s) to WAV files. May be both as `dirname` or `basename` formats.
`wavPathName`	A string containing the WAV path name. (Default: `wavPathName = 'wav_path'`).
`tags`	Tags to be added to `x`. See Details. (Default: `'feat_summary'`).
`sortByGroupBy`	Logical. Should the function sort the Extended data frame `x` by `gropuBy`? (Default: `sortByGroupBy = TRUE`).
`filesRange`	The desired range of directory files. Should only be used when all the WAV files are in the same folder. (Default: `NULL`, i.e., all files).
`features`	Vector of features to be extracted. (Default: `'f0'`).
`sex`	`= <code>` set sex specific parameters where <code> = `'f'`[emale], `'m'`[ale] or `'u'`[nknown] (default: `'u'`). Used as 'gender' by `wrassp::ksvF0`, `wrassp::forest` and `wrassp::mhsF0`.
`windowShift`	`= <dur>` set analysis window shift to <dur>ation in ms (default: 5.0). Used by `wrassp::ksvF0`, `wrassp::forest`, `wrassp::mhsF0`, `wrassp::zcrana`, `wrassp::rfcana`, `wrassp::acfana`, `wrassp::cepstrum`, `wrassp::dftSpectrum`, `wrassp::cssSpectrum` and `wrassp::lpsSpectrum`.
`numFormants`	`= <num>` <num>ber of formants (Default: `8`). Used by `wrassp::forest`.
`numcep`	Number of Mel-frequency cepstral coefficients (cepstra) to return (Default: `12`). Used by `tuneR::melfcc`.
`dcttype`	Type of DCT used. `'t1'` or `'t2'`, `'t3'` for HTK `'t4'` for feacalc (Default: `'t2'`). Used by `tuneR::melfcc`.
`fbtype`	Auditory frequency scale to use: `'mel'`, `'bark'`, `'htkmel'`, `'fcmel'` (Default: `'mel'`). Used by `tuneR::melfcc`.
`resolution`	`= <freq>` set FFT length to the smallest value which results in a frequency resolution of <freq> Hz or better (Default: `40.0`). Used by `wrassp::cssSpectrum`, `wrassp::dftSpectrum` and `wrassp::lpsSpectrum`.
`usecmp`	Logical. Apply equal-loudness weighting and cube-root compression (PLP instead of LPC) (Default: `FALSE`). Used by `tuneR::melfcc`.
`mc.cores`	Number of cores to be used in parallel processing. (Default: `1`)
`full.names`	Logical. If `TRUE`, the directory path is prepended to the file names to give a relative file path. If `FALSE`, the file names (rather than paths) are returned. (Default: `TRUE`) Used by `base::list.files`.
`recursive`	Logical. Should the listing recursively into directories? (Default: `FALSE`) Used by `base::list.files`.
`check.mono`	Logical. Check if the WAV file is mono. (Default: `TRUE`)
`stereo2mono`	(Experimental) Logical. Should files be converted from stereo to mono? (Default: `TRUE`)
`overwrite`	(Experimental) Logical. Should converted files be overwritten? If not, the file gets the suffix `_mono`. (Default: `FALSE`)
`freq`	Frequency in Hz to write the converted files when `stereo2mono=TRUE`. (Default: `44100`)
`round.to`	Number of decimal places to round to. (Default: `NULL`)
`verbose`	Logical. Should the running status be showed? (Default: `FALSE`)

Details

filesRange should only be used when all the WAV files are in the same folder.

Value

A tibble data frame containing summarized numeric columns using (1) mean, (2) standard deviation, (3) variation coefficient, (4) median, (5) interquartile range and (6) median absolute deviation.

Examples

library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# creating Extended synthetic data
E <- dplyr::tibble(subject_id = c(1,1,1,2,2,2,3,3,3),
wav_path = path2wav)
E

# minimal usage
tag(E)

# canonical data
tag(E, groupBy = 'subject_id')

# limiting filesRange
tag(E, filesRange = 3:6)

# more features
Et <- tag(E, features = c('f0', 'fmt', 'rf', 'rcf', 'rpf', 'rfc', 'mfcc'),
groupBy = 'subject_id')
Et
str(Et)
library(voice)

# get path to audio file
path2wav <- list.files(system.file('extdata', package = 'wrassp'),
pattern = glob2rx('*.wav'), full.names = TRUE)

# creating Extended synthetic data
E <- dplyr::tibble(subject_id = c(1,1,1,2,2,2,3,3,3),
wav_path = path2wav)
E

# minimal usage
tag(E)

# canonical data
tag(E, groupBy = 'subject_id')

# limiting filesRange
tag(E, filesRange = 3:6)

# more features
Et <- tag(E, features = c('f0', 'fmt', 'rf', 'rcf', 'rpf', 'rfc', 'mfcc'),
groupBy = 'subject_id')
Et
str(Et)

Writes a list to a path

Description

Writes a list to a path

Usage

write_list(x, path)
write_list(x, path)

Arguments

`x`	A list.
`path`	A full path to file.

Value

A file named 'list.txt' in 'path'.

Examples

## Not run: 
library(voice)

pts <- list(x = cars[,1], y = cars[,2])
listFile <- paste0(tempdir(), '/list.txt')
voice::write_list(pts, listFile)
file.info(listFile)
system(paste0('head ', listFile))

## End(Not run)
## Not run: 
library(voice)

pts <- list(x = cars[,1], y = cars[,2])
listFile <- paste0(tempdir(), '/list.txt')
voice::write_list(pts, listFile)
file.info(listFile)
system(paste0('head ', listFile))

## End(Not run)

Package 'voice'

Help Index

Returns the total time of audio files in seconds

Description

Usage

Arguments

Value

Examples

Who spoke when?

Description

Usage

Arguments

Details

Value

Examples

Duration of sequences

Description

Usage

Arguments

Value

Examples

Enrich RTTM files

Description

Usage

Arguments

Value

References

See Also

Examples

Expand model

Description

Usage

Arguments

Value

Examples

Extract audio features

Description

Usage

Arguments

Details

Value

References

Examples

Features summary

Description

Usage

Arguments

Details

Value

Examples

Get bit rate

Description

Usage

Arguments

Value

Examples

Time duration

Description

Usage

Arguments

Value

Examples

Get left channel

Description

Usage

Arguments

Value

Examples

Get right channel

Description

Usage

Arguments

Value

Examples

Get sample rate

Description

Usage

Arguments

Value

Examples