ssm: Self-similarity matrix
In tatters/soundgen_beta: Parametric Voice Synthesis

Description Usage Arguments Value References Examples

Calculates the self-similarity matrix and novelty vector of a sound.

ssm(x, samplingRate = NULL, windowLength = 40, overlap = 75,
  step = NULL, ssmWin = 40, maxFreq = NULL, nBands = NULL,
  MFCC = 2:13, input = c("mfcc", "audiogram", "spectrum")[1],
  norm = FALSE, simil = c("cosine", "cor")[1], returnSSM = TRUE,
  kernelLen = 200, kernelSD = 0.2, plot = TRUE, specPars = list(levels =
  seq(0, 1, length = 30), color.palette = seewave::spectro.colors, xlab =
  "Time, s", ylab = "kHz", ylim = c(0, maxFreq/1000)), ssmPars = list(levels =
  seq(0, 1, length = 30), color.palette = seewave::spectro.colors, xlab =
  "Time, s", ylab = "Time, s", main = "Self-similarity matrix"),
  noveltyPars = list(type = "b", pch = 16, col = "black", lwd = 3))

`x`	path to a .wav file or a vector of amplitudes with specified samplingRate
`samplingRate`	sampling rate of `x` (only needed if `x` is a numeric vector, rather than a .wav file)
`windowLength`	length of FFT window, ms
`overlap`	overlap between successive FFT frames, %
`step`	you can override `overlap` by specifying FFT step, ms
`ssmWin`	window for averaging SSM, ms
`maxFreq`	highest band edge of mel filters, Hz. Defaults to `samplingRate / 2`. See `melfcc`
`nBands`	number of warped spectral bands to use. Defaults to `100 * windowLength / 20`. See `melfcc`
`MFCC`	which mel-frequency cepstral coefficients to use; defaults to `2:13`)
`input`	either MFCCs ("cepstrum") or mel-filtered spectrum ("audiogram")
`norm`	if TRUE, each FFT frame is normalized by max power
`simil`	method for comparing frames: "cosine" = cosine similarity, "cor" = Pearson's correlation
`returnSSM`	if TRUE, returns the SSM
`kernelLen`	length of checkerboard kernel for calculating novelty, ms (the larger, the more global vs. local the novelty)
`kernelSD`	SD of checkerboard kernel for calculating novelty
`plot`	if TRUE, plots the SSM
`specPars`	graphical parameters passed to `seewave::filled.contour.modif2` and affecting the spectrogram
`ssmPars`	graphical parameters passed to `seewave::filled.contour.modif2` and affecting the plot of SSM
`noveltyPars`	graphical parameters passed to `lines` and affecting the novelty contour

If returnSSM is TRUE, returns a list of two components: $ssm contains the self-similarity matrix, and $novelty contains the novelty vector. If returnSSM is FALSE, only produces a plot.

El Badawy, D., Marmaroli, P., & Lissek, H. (2013). Audio Novelty-Based Segmentation of Music Concerts. In Acoustics 2013 (No. EPFL-CONF-190844)
Foote, J. (1999, October). Visualizing music and audio using self-similarity. In Proceedings of the seventh ACM international conference on Multimedia (Part 1) (pp. 77-80). ACM.
Foote, J. (2000). Automatic audio segmentation using a measure of audio novelty. In Multimedia and Expo, 2000. ICME 2000. 2000 IEEE International Conference on (Vol. 1, pp. 452-455). IEEE.

## Not run: 
sound = c(soundgen(), soundgen(nSyl = 4, sylLen = 50, pauseLen = 70,
          formants = NA, pitchAnchors = list(time = c(0, 1),
          value = c(500, 330))))
# playme(sound)
m1 = ssm(sound, samplingRate = 16000,
         input = 'audiogram', simil = 'cor', norm = FALSE,
         ssmWin = 10, kernelLen = 150)  # detailed, local features
m2 = ssm(sound, samplingRate = 16000,
         input = 'mfcc', simil = 'cosine', norm = TRUE,
         ssmWin = 50, kernelLen = 600)  # more global
# plot(m2$novelty, type='b')  # use for peak detection, etc

## End(Not run)