This node determines an optimum gain for how much of the power spectrum of estimated noise is to be removed from a power spectrum including signals + noise. Further, it outputs the probability of speech presence (See Section 6.3.8). However, this node constantly outputs 1 as the probability of speech presence. It outputs the difference between a separated sound’s power spectrum and the estimated noise’s power spectrum.

No files are required.

When to use

It is used when performing noise estimation with the HRLE node.

Typical connection

Figure 6.47 shows a connection example of CalcSpecSubGain . Inputs are power spectra after separation with GHDSS and those of the noise estimated in HRLE . Its outputs connect VOICE_PROB and GAIN to SpectralGainFilter .

Parameter name |
Type |
Default value |
Unit |
Description |

ALPHA |
1.0 |
Gain for spectral subtraction |
||

BETA |
0.0 |
Floor for GAIN |
||

SS_METHOD |
2 |
Selection of power/amplitude spectra |

Input

- INPUT_POWER_SPEC
:

`Map<int, ObjectRef>`type.`Vector<float>`type data pair of a sound source ID and a power spectrum of the separated sound.- NOISE_SPEC
:

`Map<int, ObjectRef>`type.`Vector<float>`type data pair of a sound source ID and a power spectrum of the estimated sound.

Output

- VOICE_PROB
:

`Map<int, ObjectRef>`type.`Vector<float>`type data pair of a sound source ID and a power spectrum of the probability of speech presence.- GAIN
:

`Map<int, ObjectRef>`type.`Vector<float>`type data pair of a sound source ID and a power spectrum of the optimum sound.- OUTPUT_POWER_SPEC
:

`Map<int, ObjectRef>`type.`Vector<float>`type data pair of the sound source ID and the power spectrum of the separated sound with the estimated noise deducted.

Parameter

- ALPHA
:

`float`type. Gain for spectral subtraction.- BETA
:

`float`type. Spectral floor.- SS_METHOD
:

`int`type. Selection of power/amplitude spectra for the spectral subtraction.

This node determines an optimum gain for how much of the estimated noise’s power spectrum of the estimated noise is to be removed when a noise power spectrum is removed from a power spectrum of signals + noise. It also outputs the probability of speech presence. (See Section 6.3.8.) However, this node constantly outputs 1 as the probability of speech presence. It outputs the difference of the power spectrum of the separated sound and that of the estimated noise. Assuming that the power spectrum from which noise was is $Y_ n(k_ i)$, the power spectrum of the separated sound is $X_ n(k_ i)$ and that of the noise estimated is $N_ n(k_ i)$, the output from OUTPUT_POWER_SPEC is expressed as follows.

$\displaystyle Y_ n(k_ i) $ | $\displaystyle = $ | $\displaystyle X_ n(k_ i)- N_ n(k_ i) $ | (36) |

Here, $n$ indicates an analysis frame number. $k_ i$ indicates a frequency index. The optimum gain $G_ n(k_ i)$ is expressed as follows.

$\displaystyle G_ n(k_ i) $ | $\displaystyle = $ | $\displaystyle \left\{ \begin{array}{cr} {\rm ALPHA}\frac{Y_ n(k_ i)}{X_ n(k_ i)}, & {if~ ~ } Y_ n(k_ i)> {\rm BETA}, \\ {\rm BETA},& {if~ ~ otherwise}. \end{array} \right. $ | (37) |

When processing simply with $Y_ n(k_ i)$, power can become negative. The purpose of this node is to calculate a gain for removing power spectra of noise beforehand so that power cannot be negative, since it might become difficult to treat such a power spectrum in subsequent processing.