pitches given as sample periods, such as returned by
AutoCorrelationPitches
.
strengths corresponding to the lags
, such as returned by
AutoCorrelationPitches
.
number of paths / candidates. to this the unvoiced candidate is added
the peak amplitude of the underlying input signal, one sample per pitch frame, used for the unvoiced candidate.
the maximum lag time, corresponding to the minimum pitch
threshold for determining whether window is voiced or unvoiced.
threshold for determining whether window is background or foreground.
weighting factor for low versus high frequency preference.
costs for moving pitches up and down.
to match the parameters in Praat, you should multiply
the "literature" value by 0.01 * sampleRate / stepSize
(typically in the order of 0.25)
cost for transitioning between voiced and unvoiced segments.
to match the parameters in Praat,
the "literature" value by 0.01 * sampleRate / stepSize
(typically in the order of 0.25)
see StrongestLocalMaxima
see Viterbi
pitches given as sample periods, such as returned by
AutoCorrelationPitches
.
Abstract method which must be implemented by creating the actual UGen
s
during expansion.
Abstract method which must be implemented by creating the actual UGen
s
during expansion. This method is at most called once during graph
expansion
the expanded object (depending on the type parameter U
)
the maximum lag time, corresponding to the minimum pitch
number of paths / candidates.
number of paths / candidates. to this the unvoiced candidate is added
weighting factor for low versus high frequency preference.
costs for moving pitches up and down.
costs for moving pitches up and down.
to match the parameters in Praat, you should multiply
the "literature" value by 0.01 * sampleRate / stepSize
(typically in the order of 0.25)
the peak amplitude of the underlying input signal, one sample per pitch frame, used for the unvoiced candidate.
threshold for determining whether window is background or foreground.
strengths corresponding to the lags
, such as returned by
AutoCorrelationPitches
.
cost for transitioning between voiced and unvoiced segments.
cost for transitioning between voiced and unvoiced segments.
to match the parameters in Praat,
the "literature" value by 0.01 * sampleRate / stepSize
(typically in the order of 0.25)
see StrongestLocalMaxima
see Viterbi
threshold for determining whether window is voiced or unvoiced.
A UGen that takes concurrent pitch tracker paths, and conditions them for the Viterbi algorithm. The inputs are typically taken from
AutoCorrelationPitches
, and from this a suitableadd
signal is produced to be used in theViterbi
UGen. The output are matrices of size(numIn + 1).squared
.Warning: This is still not thoroughly tested.
pitches given as sample periods, such as returned by
AutoCorrelationPitches
.strengths corresponding to the
lags
, such as returned byAutoCorrelationPitches
.number of paths / candidates. to this the unvoiced candidate is added
the peak amplitude of the underlying input signal, one sample per pitch frame, used for the unvoiced candidate.
the maximum lag time, corresponding to the minimum pitch
threshold for determining whether window is voiced or unvoiced.
threshold for determining whether window is background or foreground.
weighting factor for low versus high frequency preference.
costs for moving pitches up and down. to match the parameters in Praat, you should multiply the "literature" value by
0.01 * sampleRate / stepSize
(typically in the order of 0.25)cost for transitioning between voiced and unvoiced segments. to match the parameters in Praat, the "literature" value by
0.01 * sampleRate / stepSize
(typically in the order of 0.25) see StrongestLocalMaxima see Viterbi