CN111091846B - Noise reduction method and echo cancellation system applying same - Google Patents
Noise reduction method and echo cancellation system applying same Download PDFInfo
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- G—PHYSICS
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- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L21/0216—Noise filtering characterised by the method used for estimating noise
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/27—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the analysis technique
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- G—PHYSICS
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- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
- G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
- G10L21/0208—Noise filtering
- G10L2021/02082—Noise filtering the noise being echo, reverberation of the speech
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Abstract
The invention belongs to the technical field of echo cancellation, and particularly relates to a noise reduction method and an echo cancellation system applying the same. The noise reduction method comprises the following steps: acquiring a burr threshold pow _ threshold according to the far-end sound signal; data w in the estimated sound signal smaller than the spur threshold pow threshold are attenuated. In the above technical solution, the spur threshold value pow _ threshold obtained based on the far-end sound signal can better evaluate whether the data in the estimated sound signal contains residual spur noise, so as to weaken the corresponding data w to remove the spur noise.
Description
Technical Field
The invention belongs to the technical field of echo cancellation, and particularly relates to a noise reduction method and an echo cancellation system applying the same.
Background
The basic principles of current acoustic echo cancellation are: the method comprises the steps of using a self-adaptive filter to carry out parameter identification on an unknown echo channel omega, establishing a far-end signal model based on the correlation between a loudspeaker signal and generated multi-channel echoes, simulating an echo path, and adjusting through a self-adaptive algorithm NLMS to enable the impulse response of the echo channel to be approximate to a real echo path. Then, the signal received by the microphone is subtracted by the estimated value, and the echo cancellation function can be realized. However, after the acoustic echo cancellation uses the NLMS algorithm to calculate the preliminary effect, there is often some spur noise.
The invention patent application of application publication No. CN105791611A, 2016, 7, 20 discloses an echo cancellation method and device. Whether the participating signals meet the preset output condition or not is detected, and when the participating signals do not meet the preset output condition, the residual signals are multiplied by the first attenuation factor to obtain output signals, so that the electronic equipment can further attenuate and output when detecting that the residual signals still contain stronger echo signals, and the problem that the residual signals obtained by subtracting the estimation signals from the near-end signals still contain stronger echo signals and influence the channel quality due to the fact that the estimation signals obtained by estimating the far-end signals by the NLMS algorithm are inaccurate is solved. However, in the technical scheme, the correlation between the far-end sound signal and the near-end sound needs to be evaluated to judge whether the near-end signal has an echo signal, so that the algorithm is complex to calculate, and the calculation requirement on the processor is high.
Disclosure of Invention
In order to solve the above technical problem, the present invention provides a noise reduction method, including:
acquiring a burr threshold pow _ threshold according to the far-end sound signal;
data w in the estimated sound signal smaller than the spur threshold pow threshold are attenuated.
In the above technical solution, the burr threshold pow _ threshold obtained based on the far-end sound signal can better evaluate whether the data in the sound signal contains residual burr noise, so as to weaken the corresponding data w to remove the burr noise.
Further, the data w in the impairment estimation sound signal smaller than the spur threshold pow _ threshold refers to: let w = w × coef.
Further, the obtaining the spur threshold pow _ threshold according to the far-end sound signal includes: calculating the maximum energy far _ max _ pow of the far-end sound according to the far-end sound signal; and adjusting a base burr threshold pow _ base _ threshold according to the far-end sound maximum energy far _ max _ pow to obtain the burr threshold pow _ threshold. Therefore, different burr thresholds are adopted for the far-end sound signals with different intensities to determine the data needing to be weakened to remove noise in the estimation signal, and the estimation sound signal distortion is avoided.
Preferably, the adjusting the base spike threshold value pow _ base _ threshold according to the far-end sound maximum energy far _ max _ pow to obtain the spike threshold value pow _ threshold means: pow _ threshold equals k times pow _ base _ threshold if far _ max _ pow is greater than the first threshold; otherwise, pow _ threshold equals pow _ base _ threshold.
Further, the calculating the far-end sound maximum energy far _ max _ pow according to the far-end sound signal includes: calculating the energy of each frame signal in the far-end sound signal; and taking the maximum value in the energy of each frame signal of the far-end sound signal as the maximum far-end sound energy far _ max _ pow.
Preferably, the reduction coefficient coef =2^ (ln (far _ max _ pow/near _ max _ pow)); wherein near _ max _ pow refers to the maximum energy of the near-end sound.
Further, the obtaining of the near-end sound maximum energy near _ max _ pow includes: calculating the energy of each frame signal in the near-end sound signal; and taking the maximum value in the energy of each frame signal of the near-end sound signal as the maximum energy near _ max _ pow of the far-end sound.
Preferably, the estimated sound is an output signal of an automatic echo cancellation process based on the far-end sound and the near-end sound.
Preferably, the automatic echo cancellation process uses an NLMS algorithm to perform echo cancellation.
The invention also provides an echo cancellation system, which is characterized in that: the denoising method according to any one of the above.
The invention has the following beneficial effects:
the far-end sound signal, the near-end sound signal and the estimation signal after the automatic echo cancellation processing are combined for calculation, the estimation signal is further subjected to burr cancellation, and the burr suppression method has low calculation complexity and good suppression effect on burrs.
Drawings
Fig. 1 is a schematic application diagram of an echo cancellation system according to an embodiment of the present invention.
Detailed Description
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. Unless otherwise defined, all terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that the conventional terms should be interpreted as having a meaning that is consistent with their meaning in the relevant art and this disclosure. The present disclosure is to be considered as an example of the invention and is not intended to limit the invention to the specific embodiments.
Example one
The basic principle of current acoustic echo cancellation is: the method comprises the steps of using a self-adaptive filter to carry out parameter identification on an unknown echo channel omega, establishing a far-end signal model based on the correlation between a loudspeaker signal and generated multi-channel echoes, simulating an echo path, and adjusting through a self-adaptive algorithm NLMS to enable the impulse response of the echo channel to be approximate to a real echo path. Then, the estimated value is subtracted from the signal received by the microphone, and the echo cancellation function can be realized. However, after the acoustic echo cancellation uses the NLMS algorithm to calculate the preliminary effect, there is often some spur noise.
The glitch noise is particularly likely to occur when the near-end sound is large and the far-end sound is small, and the glitch noise is often correlated with the near-end sound. The embodiment provides an echo cancellation system, which can perform noise reduction processing on a signal after an NLMS algorithm to eliminate spur noise.
As shown in fig. 1, the echo cancellation system of the present embodiment can be applied to two electronic devices (a first electronic device and a second electronic device) talking with each other, and is used for canceling an echo signal during communication of the electronic devices. In the conversation process, the electronic equipment inputs a far-end voice signal and a near-end voice signal into an Automatic Echo Cancellation (AEC) module of the electronic equipment, the far-end voice signal is a voice signal sent by another electronic equipment in conversation with the electronic equipment, and the near-end voice signal is a voice signal collected by a microphone of the electronic equipment. In this embodiment, the AEC module performs automatic echo cancellation processing on the input near-end speech signal according to the input far-end speech signal by using a normalized least mean square adaptive filtering (NLMS) algorithm, outputs an estimated speech signal out to the noise reduction module after performing automatic echo cancellation processing on the input near-end speech signal, and transmits the estimated speech signal out to another electronic device in conversation with the electronic device after performing noise reduction processing.
The noise reduction module is mainly used for carrying out noise reduction processing on the estimated voice signal output after the automatic echo elimination processing so as to further eliminate the burr noise. The noise reduction method mainly comprises the following steps:
first, a spur threshold pow _ threshold is obtained from the far-end sound signal. The noise reduction module stores a base spike threshold value pow _ base _ threshold set by a system default or a user, and the pow _ base _ threshold in this embodiment may be set to 300 by the system default, or may be set by the user in a range of 300-. The noise reduction module calculates a far-end sound maximum energy far _ max _ pow according to the far-end sound signal, and adjusts a base burr threshold pow _ base _ threshold based on the calculated far-end sound maximum energy far _ max _ pow to determine a burr threshold pow _ threshold:
if far _ max _ pow is greater than the first threshold, pow _ threshold is equal to k times pow _ base _ threshold;
otherwise, pow _ threshold equals pow _ base _ threshold.
The first threshold is a default value of the system stored in the noise reduction module, or the first threshold may be set empirically by the user. For example, the first threshold in this embodiment is a system default value of 1000. K is a system default value greater than 1 stored in the noise reduction module, or may be an empirical value greater than 1 set by the user. For example, k in the present embodiment is a system default value of 2. Therefore, when the base spur threshold pow _ base _ threshold =300 in the present embodiment: if the far-end sound maximum energy far _ max _ pow is 2000, the spur threshold pow _ threshold = pow _ base _ threshold × 2= 600; if the far-end sound maximum energy far _ max _ pow is 800, the spur threshold pow _ threshold = pow _ base _ threshold = 300.
Second, data w in the estimated sound signal that is less than the spur threshold pow _ threshold is attenuated. The noise reduction module compares each data in the estimated sound signal out output by the AEC module with pow _ threshold, if the value of the data is smaller than the burr threshold, the data is regarded as burr data or background noise data, and the data w is subjected to noise reduction processing according to the formula w = w \ coef; if the value of the data is greater than the glitch threshold then no puncturing is performed. The attenuation coefficient coef is calculated by the formula:
coef=2^(ln(far_max_pow/near_max_pow))
far _ max _ pow is the maximum energy of the far-end sound calculated from the far-end sound signal;
near _ max _ pow is the near-end sound maximum energy calculated from the near-end sound signal.
Preferably, the method for calculating the maximum energy far _ max _ pow of the far-end sound in this embodiment is as follows:
(a) and calculating the energy of each frame signal in the far-end sound signal. The present embodiment calculates the far-end sound maximum energy based on the far-end sound signal within the last 100 ms. In this embodiment, the far-end sound signal is divided into a total of 5 frames at every 20ms as one frame (i.e., one processing unit). Each frame of the far-end sound signal is sampled (8 k sampling rate), and the data type of each sampling point is short type and ranges from-32768 to 32768.
The far-end sound energy far _ pow = sqrt (mean (w (0: end) ^2) of each frame is calculated to obtain 5 far-end sound energy values.
(b) The maximum value of the energy of each frame signal of the far-end sound signal is taken as the far-end sound maximum energy far _ max _ pow. For example, in the present embodiment, the maximum value among the energies of 5 frame signals within 100ms is used as the far-end sound maximum energy far _ max _ pow.
Similarly, the method for calculating the maximum energy far _ max _ pow of the far-end sound in this embodiment is as follows:
(a) the energy of each frame signal in the near-end sound signal is calculated. In the present embodiment, the near-end sound maximum energy is calculated from the near-end sound signal within the last 100ms during the call. In this embodiment, a total of 5 frames of near-end sound signals are divided into one frame (i.e., one processing unit) every 20 ms. Each frame of the near-end sound signal is sampled (8 k sampling rate), and the data type of each sampling point is short type and ranges from-32768 to 32768.
Near-end sound energy near _ pow = sqrt (mean (w (0: end) ^2) of each frame, 5 near-end sound energy values are calculated.
(b) The maximum value among the energies of the respective frame signals of the near-end sound signal is taken as the near-end sound maximum energy near _ max _ pow. For example, in the present embodiment, the maximum value among the energies of 5 frame signals within 100ms is taken as the near-end sound maximum energy near _ max _ pow.
Although embodiments of the present invention have been described, various changes or modifications may be made by one of ordinary skill in the art within the scope of the appended claims.
Claims (6)
1. A method of noise reduction, comprising:
acquiring a burr threshold pow _ threshold according to the far-end sound signal;
attenuating data w in the estimated sound signal that is less than the spur threshold pow threshold;
the obtaining of the spur threshold value pow _ threshold according to the far-end sound signal includes:
calculating the maximum energy far _ max _ pow of the far-end sound according to the far-end sound signal;
adjusting a base burr threshold pow _ base _ threshold according to the far-end sound maximum energy far _ max _ pow to obtain the burr threshold pow _ threshold; the adjusting a base spur threshold value pow _ base _ threshold according to the far-end sound maximum energy far _ max _ pow to obtain the spur threshold value pow _ threshold means:
pow _ threshold equals k times pow _ base _ threshold if far _ max _ pow is greater than the first threshold; otherwise, pow _ threshold is equal to pow _ base _ threshold;
the estimated sound signal is an output signal that is subjected to automatic echo cancellation processing based on the far-end sound and the near-end sound.
2. A method of reducing noise according to claim 1, wherein the data w in the de-emphasized estimated acoustic signal that is less than the spur threshold pow _ threshold is:
let w = w × coef;
coef=2^(ln(far_max_pow/near_max_pow))；
wherein near _ max _ pow refers to the maximum energy of the near-end sound.
3. The method of claim 1, wherein the calculating the far-end sound maximum energy far _ max _ pow according to the far-end sound signal comprises:
calculating the energy of each frame signal in the far-end sound signal;
and taking the maximum value in the energy of each frame signal of the far-end sound signal as the maximum far-end sound energy far _ max _ pow.
4. A method of noise reduction according to claim 2, wherein said obtaining of the near-end sound maximum energy near _ max _ pow comprises:
calculating the energy of each frame signal in the near-end sound signal;
and taking the maximum value in the energy of each frame signal of the near-end sound signal as the near-end sound maximum energy near _ max _ pow.
5. A method of noise reduction according to claim 1, characterized by:
the automatic echo cancellation process adopts NLMS algorithm to perform echo cancellation process.
6. An echo cancellation system, characterized by:
-using the noise reduction method according to any of claims 1 to 5.
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