NSGConstantQStreaming¶
streaming mode | Standard category
Inputs¶
frame
(vector_real) - the input audio signal
Outputs¶
constantq
(vector_complex) - the constant Q transform of the input frame
constantqdc
(vector_complex) - the DC band transform of the input frame. Only needed for the inverse transform
constantqnf
(vector_complex) - the Nyquist band transform of the input frame. Only needed for the inverse transform
framestamps
(integer) - this vector sets the beginnings of each frame in the ‘constantq’ buffer
Parameters¶
binsPerOctave
(integer ∈ [1, ∞), default = 48) :the number of bins per octave
gamma
(integer ∈ [0, ∞), default = 0) :The bandwidth of each filter is given by Bk = 1/Q * fk + gamma
inputSize
(integer ∈ (0, ∞), default = 4096) :the size of the input
maxFrequency
(real ∈ (0, ∞), default = 7040) :the maximum frequency
minFrequency
(real ∈ (0, ∞), default = 27.5) :the minimum frequency
minimumWindow
(integer ∈ [2, ∞), default = 4) :minimum size allowed for the windows
normalize
(string ∈ {sine, impulse, none}, default = none) :coefficient normalization
phaseMode
(string ∈ {local, global}, default = global) :‘local’ to use zero-centered filters. ‘global’ to use a phase mapping function as described in [1]
rasterize
(string ∈ {none, full, piecewise}, default = full) :hop sizes for each frequency channel. With ‘none’ each frequency channel is distinct. ‘full’ sets the hop sizes of all the channels to the smallest. ‘piecewise’ rounds down the hop size to a power of two
sampleRate
(real ∈ [0, ∞), default = 44100) :the desired sampling rate [Hz]
window
(string ∈ {hamming, hann, hannnsgcq, triangular, square, blackmanharris62, blackmanharris70, blackmanharris74, blackmanharris92}, default = hannnsgcq) :the type of window for the frequency filters. It is not recommended to change the default window.
windowSizeFactor
(integer ∈ [1, ∞), default = 1) :window sizes are rounded to multiples of this
Description¶
This algorithm computes a constant Q transform using non stationary Gabor frames and returns a complex time-frequency representation of the input vector. The implementation is inspired by the toolbox described in [1].
- References:
[1] Schörkhuber, C., Klapuri, A., Holighaus, N., & Dörfler, M. (n.d.). A Matlab Toolbox for Efficient Perfect Reconstruction Time-Frequency Transforms with Log-Frequency Resolution.