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Signal polarity detection (RESKEW algorithm)

lst_polarity.Rd

Detects microphone/recording polarity from LP residual skewness. The RESKEW algorithm compares residual skewness characteristics with and without high-pass filtering to determine signal polarity.

Usage

lst_polarity(listOfFiles, beginTime = 0, endTime = 0, toFile = FALSE, return_jstf = FALSE, verbose = TRUE)

Arguments

listOfFiles

Vector of file paths (WAV, MP3, MP4, etc.) to analyze

beginTime

Start time in seconds (0 for beginning of file)

endTime

End time in seconds (0 for end of file)

toFile

Write output to file (default: FALSE, not supported for scalar output)

verbose

Show progress messages (default: TRUE)

return_jstf

Logical. Return JsonTrackObj instead of data.frame? Default FALSE. When both toFile and return_jstf are TRUE, the file is written AND the object returned.

Value

Data frame with columns:

  • file: Input file basename

  • polarity: Polarity sign (+1 or -1)

Details

Algorithm (RESKEW, Drugman et al.):

  1. High-pass filter signal at 490 Hz cutoff (removes low-frequency noise/drift)

  2. Compute LP residual with order = fs/1000 + 2 samples, 25ms frames, 5ms shift

  3. Compare residual skewness: unfiltered vs. high-pass filtered

  4. Polarity = sign(skew_filtered - skew_unfiltered)

Interpretation:

  • +1: Normal polarity (positive peaks are vocal pulses)

  • -1: Inverted polarity (flip signal before further processing)

Note: If skewness values are very close, result may be unstable. Recommend confidence threshold: |result| > 0.1 for reliable polarity detection.

Examples

if (FALSE) { # \dontrun{
# Single file
pol <- lst_polarity("speech.wav")
cat("Polarity:", pol$polarity, "\n")

# Batch process
files <- c("file1.wav", "file2.wav")
polarities <- lst_polarity(files)
} # }