Track short-term autocorrelation function

Computes the short-term autocorrelation function (ACF) of audio signals using the libassp C library (Scheffers 2012) . Useful as a front-end feature for voicing detection and LP-based analysis. Prefer trk_acf over manual lag computation when frame-synchronous output in SSFF format is needed.

Usage

trk_acf(listOfFiles, beginTime = 0, centerTime = FALSE, endTime = 0, windowShift = 5, windowSize = 20, effectiveLength = TRUE, window = "BLACKMAN", analysisOrder = 0, energyNormalization = FALSE, lengthNormalization = FALSE, toFile = TRUE, explicitExt = "acf", outputDirectory = NULL, assertLossless = NULL, logToFile = FALSE, keepConverted = FALSE, convertOverwrites = FALSE, verbose = TRUE)

Arguments

listOfFiles: Character vector of audio file paths. Any format supported by av is accepted; non-native inputs are transcoded automatically.
beginTime: Numeric. Start of analysis window in seconds. Default 0 (file start).
centerTime: Numeric or logical. Single-frame analysis time point in seconds; overrides beginTime, endTime, and windowShift. Default FALSE.
endTime: Numeric. End of analysis window in seconds. Default 0 (file end).
windowShift: Numeric. Frame shift in milliseconds; sets output frame rate (1000 / windowShift Hz). Default 5 ms.
windowSize: Numeric. Analysis window size in milliseconds. Default 20 ms.
effectiveLength: Logical. Make window size effective rather than exact. Default TRUE.
window: Character. Analysis window function type. Default "BLACKMAN". See AsspWindowTypes for supported types.
analysisOrder: Integer. Number of lag coefficients per frame. 0 sets order to sample rate in kHz + 3 (e.g. 19 for 16 kHz audio). Default 0.
energyNormalization: Logical. Compute energy-normalised ACF. Default FALSE.
lengthNormalization: Logical. Compute length-normalised ACF. Default FALSE.
toFile: Logical. If TRUE, write SSFF output files and return the count written (invisibly). If FALSE, return an AsspDataObj. Default TRUE.
explicitExt: Character. Output file extension. Default "acf".
outputDirectory: Character. Directory for output files. NULL (default) writes alongside the input file.
verbose: Logical. Print per-file progress. Default TRUE.
assertLossless: Character vector of additional file extensions to treat as losslessly encoded.
logToFile: Logical. Write processing log to a file in outputDirectory rather than the console. Default FALSE.
keepConverted: Logical. Retain intermediate transcoded files. Default FALSE.
convertOverwrites: Logical. Allow transcoding to overwrite existing files. Default FALSE.

Value

If toFile = FALSE: an AsspDataObj with track:

ACF: REAL32, dimensionless, n_frames x analysisOrder columns. Autocorrelation coefficients at lags 0 … analysisOrder-1.

Frame rate: 1000 / windowShift Hz (default 200 Hz). If toFile = TRUE: integer count of files written, returned invisibly.

Details

analysisOrder = 0 selects an order equal to the sample rate in kHz + 3. Energy normalisation divides each frame's ACF by its lag-0 value. Length normalisation divides by frame length. Both can be combined.

References

Scheffers M (2012). “Advanced Speech Signal Processor.” https://sourceforge.net/projects/libassp/files/libassp/.

Examples

# get path to audio file
path2wav <- list.files(system.file("samples","sustained", package = "superassp"), pattern = glob2rx("a1.wav"), full.names = TRUE)

# calculate short-term autocorrelation
res <- trk_acf(path2wav, toFile=FALSE)
#> Applying `method(trk_acf, class_character)()` to 1 recording

# plot short-term autocorrelation values
matplot(seq(0, n_records(res) - 1) / sample_rate(res) +
        attr(res, 'startTime'),
        res$ACF,
        type='l',
        xlab='time (s)',
        ylab='Short-term autocorrelation values')