Method and Apparatus for Estimating Variability of Background Noise for Noise Suppression
An electronic device measures noise variability of background noise present in a sampled audio signal, and determines whether the measured noise variability is higher than a high threshold value or lower than a low threshold value. If the noise variability is determined to be higher than the high threshold value, the device categorizes the background noise as having a high degree of variability. If the noise variability is determined to be lower than the low threshold value, the device categorizes the background noise as having a low degree of variability. The high and low threshold values are between a high boundary point and a low boundary point. The high boundary point is based on an analysis of files including noises that exhibit a high degree of variability, and the low boundary point is based on an analysis of files including noises that exhibit a low degree of variability.
1 . A method comprising:
measuring noise variability of background noise present in a sampled audio signal;
determining whether the measure of noise variability is higher than a high threshold value or lower than a low threshold value;
if the noise variability is determined to be higher than the high threshold value, categorizing the background noise as having a high degree of variability;
if the noise variability is determined to be lower than the low threshold value, categorizing the background noise as having a low degree of variability;
wherein the high threshold value and low threshold value are between a high boundary point and a low boundary point;
wherein the high boundary point is based on an analysis of a first data set including noises that exhibit a high degree of variability; and
wherein the low boundary point is based on an analysis of a second data set including noises that exhibit a low degree of variability.
2 . The method of claim 1 , further comprising:
if the background noise is categorized as having a high degree of variability, suppressing the background noise using a first noise suppression algorithm; and
if the background noise is categorized as having a low degree of variability, suppressing the background noise using a second noise suppression algorithm.
3 . The method of claim 1 , further comprising:
if the noise variability is determined to be between the low threshold value and the high threshold value, categorizing the background noise as having a degree of variability of a previous frame.
4 . The method of claim 1 , wherein the measuring of noise variability of the background noise comprises:
determining whether a frame including the background noise is a noise update frame;
if the frame is determined not to be a noise update frame, categorizing the background noise as having a degree of variability of a previous frame; and
if the frame is determined to be a noise update frame, determining whether the frame is part of a sequence of contiguous noise frames.
5 . The method of claim 4 , wherein the measuring of the noise variability of the background noise further comprises:
if the frame is determined not to be part of a sequence of contiguous noise frames, categorizing the background noise as having the degree of variability of the previous frame.
6 . The method of claim 4 , wherein if the frame is determined to be part of a sequence of contiguous noise frames, the measuring of the noise variability of the background noise further comprises:
determining a maximum value of smoothed channel noise and a minimum value of smoothed channel noise in the sequence of contiguous noise frames;
computing a smoothed maximum dB difference using the maximum value of smoothed channel noise and the minimum value of smoothed channel noise; and
calculating the noise variability of the background noise using a ratio of a difference between the smoothed maximum dB difference and the low boundary point to a difference between the high boundary point and the low boundary point.
7 . The method of claim 1 , wherein the measure of noise variability of the background noise is calculated using the following equation:
MNV
=
1
NC
×
nb
∑
k
=
1
NC
∑
l
=
1
nb
(
D_smooth
(
k
,
l
)
-
D_smooth
_low
(
k
,
l
)
)
(
D_smooth
_high
(
k
,
l
)
-
D_smooth
_low
(
k
,
l
)
)
,
wherein MNV denotes the measure of noise variability of the background noise, NC denotes a number of channels, nb+1 denotes a number of contiguous noise frames, k denotes a channel index, l denotes a look-back index, D_smooth(k, l) denotes a smoothed maximum dB difference of smoothed channel noise, D_smooth_high(k, l) denotes the high boundary point, and D_smooth_low(k, l) denotes the low boundary point.
8 . The method of claim 7 , wherein the measure of noise variability of the background noise is calculated using the following equation:
MNV
=
1
NC
×
n
∑
k
∈
S
∑
l
∈
Z
(
D_smooth
(
k
,
l
)
-
D_smooth
_low
(
k
,
l
)
)
(
D_smooth
_high
(
k
,
l
)
-
D_smooth
_low
(
k
,
l
)
)
,
wherein S=(1, . . . , NC) and N≦NC denotes a number of elements in the set S, and
wherein Z=(1, . . . , nb) and n≦nb denotes a number of elements in the set Z.
9 . The method of claim 1 , wherein the measuring of noise variability of the background noise comprises:
measuring noise level of the background noise;
determining whether the measured noise level of the background noise is lower than a noise level threshold value;
if the noise level of the background noise is determined to be lower than the noise level threshold value, calculating a bias energy value;
adding the bias energy value to smoothed channel noise to generate modified smoothed channel noise; and
measuring the noise variability of the background noise using the modified smoothed channel noise.
10 . The method of claim 1 , wherein the measuring of noise variability of the background noise comprises:
for a sequence of contiguous noise frames, computing an average frame energy using frame energies of the sequence of contiguous noise frames;
for each frame in the sequence of contiguous noise frames, subtracting the average frame energy from the frame energy to generate a frame energy difference;
subtracting the frame energy difference from corresponding channel noise energies to generate compensated channel noise energies; and
measuring the noise variability of the background noise using the compensated channel noise energies.
11 . A device comprising:
a microphone that receives an audio signal;
a processor that is electrically coupled to the microphone, wherein the processor:
measures noise variability of background noise present in the audio signal;
determines whether the measure of noise variability is higher than a high threshold value or lower than a low threshold value;
if the noise variability is determined to be higher than the high threshold value, categorizes the background noise as having a high degree of variability; and
if the noise variability is determined to be lower than the low threshold value, categorizes the background noise as having a low degree of variability;
a memory that is electronically coupled to the processor, wherein the memory stores the high threshold value, the low threshold value, a high boundary point, and a low boundary point;
wherein the high threshold value and the low threshold value are between the high boundary point and the low boundary point;
wherein the high boundary point is based on an analysis of a first data set including noises that exhibit a high degree of variability; and
wherein the low boundary point is based on an analysis of a second data set including noises that exhibit a low degree of variability.
12 . The device of claim 11 , wherein the processor further:
suppresses the background noise using a first noise suppression algorithm, if the background noise is categorized as having a high degree of variability; and
suppresses the background noise using a second noise suppression algorithm, if the background noise is categorized as having a low degree of variability.
13 . The device of claim 11 , wherein if the noise variability is determined to be between the low threshold value and the high threshold value, the processor further categorizes the background noise as having a degree of variability of a previous frame.
14 . The device of claim 11 , wherein the processor further:
determines whether a frame including the background noise is a noise update frame;
if the frame is determined not to be a noise update frame, categorizes the background noise as having a degree of variability of a previous frame; and
if the frame is a noise update frame, determines whether the frame is part of a sequence of contiguous noise frames.
15 . The device of claim 14 , wherein if the frame is determined not to be part of a sequence of contiguous noise frames, the processor further categorizes the background noise as having a degree of variability of a previous frame.
16 . The device of claim 14 , wherein if the frame is part of a sequence of contiguous noise frames, the processor further:
determines a maximum value of smoothed channel noise and a minimum value of smoothed channel noise in the sequence of contiguous noise frames;
computes a smoothed maximum dB difference using the maximum value of smoothed channel noise and the minimum value of smoothed channel noise; and
calculates the noise variability of the background noise using a ratio of a difference between the smoothed maximum dB difference and the low boundary point to a difference between the high boundary point and the low boundary point.
17 . The device of claim 11 , wherein the processor measures the noise variability of the background noise using the following equation:
MNV
=
1
NC
×
nb
∑
k
=
1
NC
∑
l
=
1
nb
(
D_smooth
(
k
,
l
)
-
D_smooth
_low
(
k
,
l
)
)
(
D_smooth
_high
(
k
,
l
)
-
D_smooth
_low
(
k
,
l
)
)
,
wherein MNV denotes the measure noise variability of the background noise, NC denotes a number of channels, nb+1 denotes a number of contiguous noise frames, k denotes a channel index, l denotes a look-back index, D_smooth(k, l) denotes a smoothed maximum dB difference of smoothed channel noise, D_smooth_high(k, l) denotes the high boundary point, and D_smooth_low(k, l) denotes the low boundary point.
18 . The device of claim 17 , wherein the processor measures the noise variability of the background noise using the following equation:
MNV
=
1
NC
×
n
∑
k
∈
S
∑
l
∈
Z
(
D_smooth
(
k
,
l
)
-
D_smooth
_low
(
k
,
l
)
)
(
D_smooth
_high
(
k
,
l
)
-
D_smooth
_low
(
k
,
l
)
)
,
wherein S=(1, . . . , NC) and N≦NC denotes a number of elements in the set S, and
wherein Z=(1, . . . , nb) and n≦nb denotes a number of elements in the set Z.
19 . The device of claim 11 , wherein when the processor measures the noise variability of background noise present in the audio signal, the processor further:
measures noise level of the background noise;
determines whether the measured noise level of the background noise is lower than a noise level threshold value;
if the noise level of the background noise is determined to be lower than the noise level threshold value, calculates a bias energy value;
adds the bias energy value to smoothed channel noise to generate modified smoothed channel noise; and
measures the noise variability of the background noise using the modified smoothed channel noise.
20 . The device of claim 11 , wherein when the processor measures the noise variability of background noise in the audio signal, the processor further:
for a sequence of contiguous noise frames, computes an average frame energy using frame energies of the sequence of contiguous noise frame;
for each frame in the sequence of contiguous noise frames, subtracts the average frame energy from the frame energy to generate a frame energy difference;
subtracts the frame energy difference from corresponding channel noise energies of each frame to generate compensated channel noise energies; and
measures the noise variability of the background noise using the compensated channel noise energies.
21 . A non-transitory computer readable storage medium having stored thereon a program executable by a computing processor to perform a method, the method comprising:
measuring noise variability of background noise present of a sampled audio signal;
determining whether the measure of noise variability is higher than a high threshold value or lower than a low threshold value;
if the noise variability is determined to be higher than the high threshold value, categorizing the background noise as having a high degree of variability;
if the noise variability is determined to be lower than the low threshold value, categorizing the background noise as having a low degree of variability;
wherein the high threshold value and low threshold value are between a high boundary point and a low boundary point;
wherein the high boundary point is based on an analysis of a first data set including noises that exhibit a high degree of variability; and
wherein the low boundary point is based on analysis of a second data set including noises that exhibit a low degree of variability.