An audio noise reduction method and system

Abstract

An embodiment of this specification discloses an audio noise reduction method and system. Among them, the method includes: acquiring a Doppler ultrasound signal, where the Doppler ultrasound signal includes a user's blood flow signal and a noise signal; performing segmentation processing on the Doppler ultrasound signal; for each segment of the Doppler ultrasound signal, extracting a parameter related to the power of the noise signal, and according to the value of the parameter, selecting a target noise reduction algorithm from at least two noise reduction algorithms to perform noise reduction processing on this segment of the Doppler ultrasound signal. And synthesizing each segment of the Doppler ultrasound signal after noise reduction to generate an audio signal corresponding to the user's blood flow signal.

Description

Technical Field

[0001] This specification relates to the field of imaging diagnosis, and particularly to an audio noise reduction method and system. Background Art

[0002] Medical Doppler ultrasound detection can interact with the human body in a non-invasive manner by means of a certain medium, and then obtain images and / or sounds of internal tissues and organs of the target to assist physicians in diagnosing and treating diseases. Currently, there is a large amount of background noise in medical Doppler ultrasound detection, which affects the detection effect.

[0003] Therefore, it is necessary to provide an audio noise reduction method to effectively suppress the background noise of Doppler ultrasound detection. Summary of the Invention

[0004] One aspect of the embodiments of this specification provides an audio noise reduction method. The audio noise reduction method includes: obtaining a Doppler ultrasound signal, where the Doppler ultrasound signal includes a user's blood flow signal and a noise signal; performing segmentation processing on the Doppler ultrasound signal; for each segment of the Doppler ultrasound signal, extracting a parameter related to the power of the noise signal; according to the value of the parameter, selecting a target noise reduction algorithm from at least two noise reduction algorithms to perform noise reduction processing on the segment of the Doppler ultrasound signal; and synthesizing each segment of the Doppler ultrasound signal after noise reduction to generate an audio signal corresponding to the user's blood flow signal.

[0005] Another aspect of the embodiments of this specification provides an audio noise reduction system. The system includes: an ultrasonic signal acquisition module for obtaining a Doppler ultrasound signal, where the Doppler ultrasound signal includes a user's blood flow signal and a noise signal; an ultrasonic signal segmentation processing module for performing segmentation processing on the Doppler ultrasound signal; an ultrasonic signal noise reduction processing module for, for each segment of the Doppler ultrasound signal, extracting a parameter related to the power of the noise signal, and for selecting a target noise reduction algorithm from at least two noise reduction algorithms according to the value of the parameter to perform noise reduction processing on the segment of the Doppler ultrasound signal; and an audio signal generation module for synthesizing each segment of the Doppler ultrasound signal after noise reduction to generate an audio signal corresponding to the user's blood flow signal.

[0006] Another aspect of the embodiments of this specification provides an image processing device including at least one storage medium and at least one processor, where the at least one storage medium is used to store computer instructions; the at least one processor is used to execute the computer instructions to implement the audio noise reduction method.

[0007] Another aspect of the embodiments of this specification provides a computer-readable storage medium, where the storage medium stores computer instructions, and when a computer reads the computer instructions in the storage medium, the computer executes the audio noise reduction method. Description of the Drawings

[0008] This specification will be further described by way of exemplary embodiments, which will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0009] Figure 1 is a schematic diagram of an exemplary application scenario of an audio noise reduction system shown in some embodiments of this specification;

[0010] Figure 2 is an exemplary flowchart of an audio noise reduction method shown in some embodiments of this specification;

[0011] Figure 3 An exemplary flowchart of generating parameters related to the noise intensity of an evaluation noise signal shown in some embodiments of this specification;

[0012] Figure 4 An exemplary flowchart of generating a blood flow audio signal shown in some embodiments of this specification;

[0013] Figure 5 is an exemplary block diagram of an audio noise reduction system shown in some embodiments of this specification;

[0014] Figure 6 is an exemplary diagram of windowing a time signal by a window function shown in some embodiments of this specification;

[0015] Figure 7 is an exemplary diagram of an audio signal without noise reduction shown in some embodiments of this specification;

[0016] Figure 8 is an exemplary diagram of an audio output signal after audio noise reduction processing shown in some embodiments of this specification;

[0017] Figure 9 is an exemplary diagram of a carotid artery blood flow image shown in some embodiments of this specification;

[0018] Figure 10 is an exemplary diagram of an un-denoised carotid artery forward blood flow audio signal shown in some embodiments of this specification;

[0019] Figure 11 is an exemplary diagram of an un-denoised carotid artery reverse blood flow audio signal shown in some embodiments of this specification;

[0020] Figure 12 is an exemplary diagram of a denoised carotid artery forward blood flow audio signal shown in some embodiments of this specification;

[0021] Figure 13 It is an exemplary schematic diagram after denoising the carotid artery negative blood flow audio signal shown in some embodiments of this specification. Detailed implementation manners

[0022] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the accompanying drawings required for the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some examples or embodiments of this specification. For those of ordinary skill in the art, without creative efforts, this specification can also be applied to other similar scenarios based on these drawings. Unless obvious from the language context or otherwise stated, the same reference numerals in the figures represent the same structure or operation.

[0023] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the said words can be replaced by other expressions.

[0024] As shown in this specification and the claims, unless the context clearly indicates an exception, words such as "a", "an", "one" and / or "the" are not specifically singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of the clearly identified steps and elements, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0025] Flowcharts are used in this specification to illustrate the operations performed by the systems according to the embodiments of this specification. It should be understood that the previous or subsequent operations do not necessarily need to be executed precisely in order. On the contrary, they can be executed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or several steps can be removed from these processes.

[0026] Doppler ultrasound technology is an important means for non-invasive diagnosis of vascular diseases. The existence of vascular diseases will cause changes in the blood flow velocity waveform, such as the average frequency or maximum frequency curve of the Doppler signal. Currently, a large amount of background noise is usually accompanied in medical Doppler ultrasound detection. The sources of this background noise include the weak scattering of the detected tissue and system noise, etc. This kind of noise usually accompanies the entire audio signal and is distributed in all frequency bands, greatly reducing the signal-to-noise ratio of the audio signal and seriously interfering with the audio output of the blood flow signal.

[0027] In related technologies, the methods for denoising audio signals mainly include Wiener filtering, spectral subtraction filtering, deep learning filtering, and so on. Some of these methods require a large amount of data training to obtain results, and some require comprehensive evaluation by analyzing multiple time periods to obtain the current result.

[0028] How to achieve real-time denoising of audio signals in the case of unknown noise has become an urgent problem to be solved. Therefore, some embodiments of this specification propose an audio denoising method and system. Through this audio denoising method, real-time removal of audio background noise can be achieved without prior estimation of background noise. The technical solutions disclosed in this specification are elaborated in detail below through the description of the drawings.

[0029] Figure 1 It is a schematic diagram of an exemplary application scenario of an audio denoising system shown in some embodiments of this specification.

[0030] As Figure 1 shown, the audio denoising system 100 may include an ultrasound device 110, a processing device 120, a network 130, a storage device 140, and a terminal 150. In some embodiments, the ultrasound device 110, the processing device 120, the network 130, the storage device 140, and the terminal 150 may be connected and / or communicate with each other in a wired and / or wireless manner.

[0031] The ultrasound device 110 may be used to obtain ultrasound data of a target area on an object (user). The ultrasound data may include ultrasound imaging data and ultrasound audio data. The ultrasound imaging data may be used for imaging, for example, blood flow images; the ultrasound audio data may be used to output audio signals, for example, blood flow sound signals. In some embodiments, the ultrasound device may utilize the physical properties of ultrasonic waves and the differences in acoustic properties of the target area on the object to obtain ultrasound data of the target area on the object, and the ultrasound data may be displayed and / or recorded in the form of data, waveforms, curves, or images to show the characteristics related to the target area on the object. Only by way of example, the ultrasound device may include one or more ultrasound probes for emitting ultrasonic waves to the target area (for example, an object or its organ, tissue located on the treatment bed 111). After passing through organs and tissues with different acoustic impedances and different attenuation characteristics, the ultrasonic waves generate different reflections and attenuations, thereby forming echoes that can be received by the one or more ultrasound probes. The ultrasound device may process (for example, amplify, convert) and / or display the received echoes to generate ultrasound data. In some embodiments, the ultrasound device may include a Doppler ultrasound device, a color Doppler ultrasound device, a cardiac color ultrasound device, a three-dimensional color ultrasound device, etc. or any combination thereof. In some embodiments, the ultrasound data may be Doppler ultrasound signals.

[0032] In some embodiments, the ultrasound device 110 may send Doppler ultrasound signals to the processing device 120, the storage device 140, and / or the terminal 150 via the network 130 for further processing. For example, the Doppler ultrasound signals acquired by the ultrasound device may be data in a non-image form, and the non-image form data may be sent to the processing device 120 for generating an ultrasound spectral image and / or a blood flow audio signal. As another example, the Doppler ultrasound signals may be stored in the storage device 140.

[0033] In some embodiments, the ultrasound device 110 may further include other imaging devices 112. In some embodiments, the other imaging devices 112 may include X-ray imaging equipment, magnetic resonance imaging devices, nuclear medicine devices, thermal imaging devices, medical optical devices, etc., or any combination thereof.

[0034] The processing device 120 may process the data and / or information obtained from the ultrasound device 110, the storage device 140, and / or the terminal 150. For example, the processing device 120 may process the Doppler ultrasound signals acquired from the imaging device in the ultrasound device 110 and generate an audio signal of the target area. In some embodiments, the audio signal may be sent to the terminal 150 and output on one or more speakers in the terminal 150. In some embodiments, the processing device 120 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device 120 may be local or remote. For example, the processing device 120 may access the information and / or data stored in the ultrasound device 110, the storage device 140, and / or the terminal 150 via the network 130. As another example, the processing device 120 may be directly connected to the ultrasound device 110, the storage device 140, and / or the terminal 150 to access the information and / or data stored thereon. As yet another example, the processing device 120 may be integrated in the ultrasound device 110. In some embodiments, the processing device 120 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0035] In some embodiments, the processing device 120 may be a single processing device that communicates with the ultrasound device and processes the data received from the ultrasound device.

[0036] Network 130 may include any suitable network that can facilitate information and / or data exchange of the audio noise reduction system 100. In some embodiments, one or more components of the audio noise reduction system 100 (e.g., the ultrasonic device 110, the processing device 120, the storage device 140, or the terminal 150) may be connected and / or communicate with other components of the audio noise reduction system 100 via the network 130. For example, the processing device 120 may obtain ultrasonic data from the ultrasonic device 110 via the network 130. As another example, the processing device 120 may obtain user instructions from the terminal 150 via the network 130, and the instructions may be used to instruct the ultrasonic device 110 to perform ultrasonic detection. In some embodiments, the network 130 may include one or more network access points. For example, the network 130 may include wired and / or wireless network access points, such as base stations and / or Internet access points, through which one or more components of the audio noise reduction system 100 can be connected to the network 130 to exchange data and / or information.

[0037] The storage device 140 may store data and / or instructions. In some embodiments, the storage device 140 may store data obtained from the terminal 150 and / or the processing device 120. In some embodiments, the storage device 140 may store data and / or instructions that the processing device 120 may execute or use to execute the exemplary methods described in this application. In some embodiments, the storage device 140 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-cloud, etc., or any combination thereof.

[0038] In some embodiments, the storage device 140 may be connected to the network 130 to communicate with one or more components of the audio noise reduction system 100 (e.g., the processing device 120, the terminal 150, etc.). One or more components of the audio noise reduction system 100 may access the data or instructions stored in the storage device 140 via the network 130. In some embodiments, the storage device 140 may be directly connected to or communicate with one or more components of the audio noise reduction system 100 (e.g., the processing device 120, the terminal 150, etc.). In some embodiments, the storage device 140 may be a part of the processing device 120.

[0039] The terminal 150 may include a mobile device 150-1, a tablet computer 150-2, a laptop computer 150-3, etc., or any combination thereof. In some embodiments, the terminal 150 may remotely operate the ultrasound device 110. In some embodiments, the terminal 150 may operate the ultrasound device 110 via a wireless connection. In some embodiments, the terminal 150 may receive information and / or instructions input by a user and send the received information and / or instructions to the ultrasound device 110 or the processing device 120 via the network 130. In some embodiments, the terminal 150 may receive data and / or information from the processing device 120. In some embodiments, the terminal 150 may be a part of the processing device 120. In some embodiments, the terminal 150 may be omitted.

[0040] Figure 2 is an exemplary flowchart of an audio noise reduction method according to some embodiments of this specification. In some embodiments, the process 200 may be executed by a processing device (e.g., the processing device 120). For example, the process 200 may be stored in a storage device (such as the built-in storage unit or an external storage device of the processing device) in the form of a program or instructions, and when the program or instructions are executed, the process 200 may be implemented. The process 200 may include the following operations.

[0041] Step 202, obtain a Doppler ultrasound signal.

[0042] The Doppler ultrasound signal may refer to the signal that is transmitted by an ultrasound probe to the user's tissue and then received after a certain time delay. The user may also be referred to as the target object, which may include a patient or other medical experimental objects (e.g., other animals such as laboratory mice), etc. The target object may also be a part of a patient or other medical experimental object, including organs and / or tissues, such as blood vessels, heart, lungs, ribs, abdominal cavity, etc.

[0043] In some embodiments, the Doppler ultrasound signal includes the user's blood flow signal and a noise signal. The blood flow signal may refer to the signal that reflects the blood flow of the user's tissue detected by Doppler ultrasound. The noise signal may refer to the signal generated due to the weak scattering of the user's detected tissue and system reasons, etc.

[0044] In some embodiments, the Doppler ultrasound signal may be an ultrasound signal obtained by processing the original echo signal received by the ultrasound probe in a certain manner. For example, the processing device may receive the ultrasound echo signal returned from the user and perform beamforming to obtain the beamformed signal. Beamforming may refer to the values corresponding to each point of the user's tissue obtained by processing (calculating) the echo signal received by the probe. Each point of the user's tissue may be the discrete point corresponding to the user's tissue described by a mathematical method with multiple discrete points. The processing device may sum the beamformed signals within the sampling frame set at the target position of the user. The target position may be the blood flow position within the user's blood vessel, and the target position may be marked and determined on the ultrasound image corresponding to the user. The sampling frame may be used to collect the required beamformed signals. Summing may be to superimpose multiple beamformed signals within the sampling frame by some mathematical methods, and superimpose the echo signals sampled at all times together. The echo signal at the position to be detected can be obtained through the sampling frame. The processing device may perform wall filtering on the summed signal to obtain the Doppler ultrasound signal. Wall filtering can be understood as performing a convolution operation on the summed signal. Wall filtering can be performed by a wall filter, and the type of the wall filter is not limited in this specification. Through wall filtering, the low-frequency signals in the signal can be filtered out, and the high-frequency signals can be retained. Since the vibration speed of the blood vessel wall is very slow, the low-frequency signals can be considered to be generated by the vibration of the blood vessel wall, and this part of the signal can be considered as non-blood flow signals.

[0045] In some embodiments, the processing device may obtain the Doppler ultrasound signal by reading from a storage device, a database, or calling a relevant data interface, etc.

[0046] Step 204, perform segmentation processing on the Doppler ultrasound signal.

[0047] Segmentation processing may refer to dividing the Doppler ultrasound signal into signals of multiple different time periods.

[0048] In some embodiments, the processing device may obtain a segment of Doppler ultrasound signal corresponding to each time period by applying a window function to different time periods of the Doppler ultrasound signal respectively, and further obtain multiple segments of Doppler ultrasound signal corresponding to multiple different time periods.

[0049] Exemplarily, the processing device may perform segmentation processing on the Doppler ultrasound signal in the manner described in the following embodiments.

[0050] In some embodiments, the processing device may first demodulate the Doppler ultrasound signal to obtain a time signal p(t). At this time, the Doppler ultrasound signal is a time signal. Here, t is the time independent variable, and the time signal p(t) belongs to the complex domain. Then, a window function is used to window the time signal p(t). The window size of the window function is equal to the number of data points for the short-time Fourier transform. As Figure 6 shown, Figure 6 FIG. is an exemplary diagram of windowing the time signal by a window function according to some embodiments of the present specification. The window function is denoted as H(t L ), the window size is t L , and the length of each window movement (abbreviated as the step size) is t step . The windowing process can be expressed by formula (1).

[0051] g n (t n ) = p n (t n )H(t L ) (1)

[0052] where g n (t n ) represents the Doppler ultrasound signal data of the nth window obtained by windowing. n represents the nth window. Each window contains L data points, and t n ∈[(n - 1)t step , (n - 1)t step + t L , n = 1, 2, 3,..., N. The data in each window can be determined as a segment of the Doppler ultrasound signal.

[0053] In some embodiments, the window function used may be a Hanning window.

[0054] Segmenting the Doppler ultrasound signal can ensure that the signal at the current moment is not mixed into the next moment (i.e., ensure the real-time nature of the signal). Therefore, the data in each time period is multiplied by the window function for segmentation. In some embodiments, the window function used may be a Gaussian function.

[0055] In some embodiments, after segmentation, multiple segments of Doppler ultrasound signals with a certain time length can be obtained.

[0056] Step 206: Perform noise reduction processing on each segment of the Doppler ultrasound signal.

[0057] Noise reduction processing may refer to digitally processing the Doppler ultrasound signal to remove or reduce the background noise therein.

[0058] In some embodiments, the processing device may perform a short-time Fourier transform on each segment of the Doppler ultrasound signal to obtain frequency-domain data corresponding to each segment of the Doppler ultrasound signal. The processing device may perform noise reduction processing on each segment of the Doppler ultrasound signal by performing the operations of steps 2062 to 2064.

[0059] In some embodiments, the process of performing a short-time Fourier transform on each segment of the Doppler ultrasound signal can be represented by the following formula (2).

[0060]

[0061] Where G n (f) represents the frequency-domain data obtained after performing a Fourier transform on the nth segment of the Doppler ultrasound signal, and P n (t n )H(tL) represents the nth window data obtained by windowing.

[0062] Step 2062: Extract parameters related to the power of the noise signal.

[0063] The power of the noise signal may refer to the square of the modulus value of the frequency-domain data after performing a Fourier transform on the Doppler ultrasound signal.

[0064] For the frequency-domain data G n (f) after Fourier transform, its modulus value is |G n (f)|, and taking the square of the modulus value is the power of the noise signal, denoted as |G n (f)| 2 .

[0065] The parameters related to the power of the noise signal can be calculated based on the average value and the maximum value of the power of the noise signal. Among them, the average value of the power of the noise signal can be expressed as V n , and the maximum value of the power of the noise signal can be expressed as M n . V n is related to the strength of the noise signal and can be used to reflect the noise intensity of the noise signal. M n is related to the strength of the blood flow signal and can be used to reflect the power of the blood flow signal.

[0066] In some embodiments, the processing device may determine the parameter based on the magnitude or proportional relationship between the average value and the maximum value of the power of the noise signal. For more descriptions on determining this parameter, reference can be made to Figure 3 and its related descriptions, which will not be elaborated here.

[0067] Step 2064: Select a target noise reduction algorithm from at least two noise reduction algorithms according to the value of the parameter to perform noise reduction processing on the segment of the Doppler ultrasound signal.

[0068] In some embodiments, a threshold value of a parameter can be preset, and a target noise reduction algorithm is selected from at least two noise reduction algorithms according to the magnitude relationship between the value of the parameter and the preset threshold value of the parameter. Different noise reduction algorithms are used to remove power parameters with different weights from the frequency domain data respectively.

[0069] In some embodiments, the processing device can select a first noise reduction algorithm in response to the value of the parameter being greater than the threshold. When the value of the parameter is greater than the threshold, it indicates that there is blood flow sound in the Doppler ultrasound signal, and the blood flow audio power is much greater than the power of the noise signal. At this time, the denoised signal can be directly obtained. For example, the power of the noise signal minus the average value of the noise signal to obtain the denoised signal. Exemplarily, the noise reduction process using the first noise reduction algorithm can be represented by the following formula (3).

[0070]

[0071] Wherein, represents the denoised signal, |G n (f)| 2 is the power of the noise signal, V n is the average value of the noise signal.

[0072] In some embodiments, the processing device can select a second noise reduction algorithm in response to the value of the parameter being less than or equal to the threshold. When the value of the parameter is less than or equal to the threshold, it indicates that there is blood flow sound in the Doppler ultrasound signal, but the blood flow audio power and the noise power are close. The way to obtain the denoised signal can be to subtract the average value of the noise signal from the power of the noise signal. The difference from the first noise reduction algorithm is that the second noise reduction algorithm and the first noise reduction algorithm include at least one different noise reduction parameter. For example, the weight of the average value of the noise signal in the first noise reduction algorithm is different from the weight of the average value of the noise signal in the second noise reduction algorithm. The noise reduction parameter can refer to the weight of the average value of the noise signal in the noise reduction algorithm. Exemplarily, the noise reduction process using the second noise reduction algorithm can be represented by the following formula (4).

[0073]

[0074] Wherein, represents the denoised signal, |G n (f)| 2 is the power of the noise signal, V n is the average value of the noise signal, y is the weight of the average value of the noise signal, and the value range of y can be set according to experience. For example, y ∈ [0.01, 0.1]. It can be understood that the weight coefficient of the average power of the noise signal in the first noise reduction algorithm is 1.

[0075] In some embodiments, the processing device may determine whether the calculation result of the noise reduction algorithm is less than zero. When it is less than zero, since the modulus value cannot be less than zero, the calculation result of the noise reduction algorithm may be directly set to zero.

[0076] After performing noise reduction processing through the noise reduction algorithm, the processing device may obtain the noise-reduced frequency-domain signal based on the phase signal corresponding to the modulus value of the result of the noise reduction processing and the frequency-domain data. The phase signal corresponding to the modulus value may be expressed as angle[G n (f)], and the phase signal may be obtained together when taking the modulus value of the frequency-domain data after Fourier transform.

[0077] Exemplarily, obtaining the noise-reduced frequency-domain data may be represented by the following formula (5).

[0078]

[0079] Wherein, represents the noise-reduced frequency-domain data, and angle[G n (f)] represents the phase signal.

[0080] Step 208, synthesize each segment of the Doppler ultrasound signal after noise reduction to generate an audio signal corresponding to the blood flow signal of the user.

[0081] Since when detecting blood flow by Doppler ultrasound, the blood flow direction can be divided into positive and negative directions. The positive direction indicates that the blood flow is towards the probe, and the negative direction indicates that the blood flow is away from the probe. This phenomenon is also reflected in the frequency-domain data. For example, in the noise-reduced frequency-domain data, the positive frequency components represent positive blood flow; the negative frequency components represent negative blood flow. Therefore, can be divided into two parts, namely the positive frequency part and the negative frequency part The way of dividing into two parts can refer to the existing way and will not be elaborated here. The blood flow direction can also be distinguished in audio. For example, the positive and negative blood flows are played in different channels.

[0082] In some embodiments, the processing device may respectively take the and conjugates of and and re-obtain the positive and negative frequency components, which are composed of the original positive and negative frequencies and their respective conjugates. Exemplarily, it can be represented by the following formulas (6) and (7).

[0083]

[0084]

[0085] Wherein, represents the positive frequency component, represents the negative frequency component. Among them, the lengths of the positive and negative frequency data are both the same as the original data respectively.

[0086] After that, the processing device can perform inverse Fourier transforms on the obtained positive and negative frequency components respectively and multiply them by the window function. The process can be expressed by the following formulas (8) and (9).

[0087]

[0088]

[0089] Among them, represents the result obtained by performing inverse Fourier transform on the positive frequency component and multiplying it by the window function, represents the result obtained by performing inverse Fourier transform on the negative frequency component and multiplying it by the window function, H(t L ) represents the window function. The window function here is the same as the window function used when segmenting the Doppler ultrasound signal.

[0090] Then, the processing device can sequentially superimpose the forward blood flow audio signals corresponding to each segment of the Doppler ultrasound signal in the time domain to generate a continuous forward blood flow audio signal in the time domain. For example, the processing device can accumulate by moving the window of the window function according to the step size to obtain the audio outputs in both positive and negative directions. Exemplarily, the process can be expressed by the following formula (10).

[0091]

[0092] Among them, g + (t) represents the continuous forward blood flow audio signal generated in the time domain, ∑[H(t L )] 2 represents the sum of squares of the window function, t n ∈[(n - 1)t step , (n - 1)t step + t L , n = 1, 2, 3..., N. Taking the sum of squares can mean adding at the same moment. For the sake of easy understanding, an example is given. When n = 1, 2, 3, L = 6, step = 3, g + (t) can contain 12 time points, so it can be written as a 12-dimensional vector and can be expressed by the following formula (11).

[0093]

[0094] In some embodiments, the processing device may also sequentially superimpose the negative blood flow audio signals corresponding to each segment of the Doppler ultrasound signal in the time domain to generate a continuous negative blood flow audio signal in the time domain. This process can be represented by the following formula (12).

[0095]

[0096] Where g - (t) represents the generated continuous negative blood flow audio signal in the time domain, ∑[H(t L )] 2 represents the sum of squares of the window function, t n ∈[(n - 1)t step , (n - 1)t step +t L , n = 1, 2, 3 ···, N. Similarly, when n = 1, 2, 3, L = 6, step = 3, g - (t) may include 12 time points, and thus can also be written as a 12-dimensional vector, which can be represented by the following formula (13).

[0097]

[0098] In some embodiments of this specification, by judging the power of the noise signal in the Doppler ultrasound signal, real-time filtering of the audio output of Doppler ultrasound detection can be achieved, without the need to estimate the background noise (unknown noise) in advance, without a large number of sample trainings (real-time denoising), and without regression analysis. It is simple and easy to implement, greatly reducing the background noise and better retaining the useful audio signals. At the same time, this method has simple calculations, extremely little time consumption, and does not affect the audio output speed.

[0099] In some embodiments, through single-frequency signal testing, the method of the technical solution disclosed in the embodiments of this specification can increase the original signal-to-noise ratio of 37.65 dB to approximately 55.41 dB. To verify the effect of the noise reduction method disclosed in the embodiments of this specification, the audio output signal of a single frequency is processed, and the signal-to-noise ratio after denoising is calculated. Exemplarily, the calculation method of the signal-to-noise ratio can be as shown in the following formula (14).

[0100]

[0101] Where m represents the number of maximum values in g(t), g(t h ) represents the maximum value, and max[g(t)] represents the maximum value. Figure 7 is an exemplary schematic diagram of the un-denoised audio signal according to some embodiments of this specification, Figure 7The figure shown is the frequency domain diagram of a single-frequency audio output signal. The horizontal axis is frequency, with the unit of Hz, and the vertical axis At this time, the SNR = 37.65 dB. By using the audio noise processing method disclosed in this specification, we can obtain Figure 8 , Figure 8 is an exemplary schematic diagram of the audio output signal after audio noise reduction processing according to some embodiments of this specification. At this time, the SNR = 55.41 dB. Obviously, the audio noise reduction method disclosed in the embodiments of this specification can increase the signal-to-noise ratio of the audio signal by approximately 17 dB, effectively removing the background noise in the audio signal.

[0102] To further illustrate the effectiveness of the technical solution disclosed in this specification, medical Doppler ultrasound is used to detect carotid artery blood flow, and its blood flow schematic diagram is as shown in Figure 9 . Figure 9 is an exemplary schematic diagram of the carotid artery blood flow image according to some embodiments of this specification. It can be seen from the blood flow image that the blood flow direction is almost entirely positive. Therefore, theoretically, the negative blood flow is almost zero. However, due to the presence of background noise, the audio corresponding to the negative blood flow still has noise, as shown in Figure 11 . Figure 11 is an exemplary schematic diagram of the un-denoised carotid artery negative blood flow audio signal according to some embodiments of this specification. Figure 10 is an exemplary schematic diagram of the un-denoised carotid artery positive blood flow audio signal according to some embodiments of this specification. Using the audio noise reduction method disclosed in this specification, the blood flow audio signals obtained are as shown in Figure 12 and Figure 13 . Figure 12 is an exemplary schematic diagram of the carotid artery positive blood flow audio signal after denoising according to some embodiments of the specification, Figure 13 is an exemplary schematic diagram of the carotid artery negative blood flow audio signal after denoising according to some embodiments of this specification. Comparing Figure 11 and Figure 13 it can be seen that Figure 13 there is basically no background noise in the negative blood flow audio signal shown. At the same time, comparing Figure 10 and Figure 12 it can be seen that the denoising method well preserves the positive blood flow audio signal. In summary, the audio noise reduction algorithm proposed in some embodiments of this specification can effectively suppress the audio background noise while better preserving the useful information.

[0103] Figure 3Exemplary flowchart for generating parameters related to the noise intensity of the evaluation noise signal as shown in some embodiments of this specification. In some embodiments, process 300 may be executed by a processing device. For example, process 300 may be stored in a storage device (such as the built-in storage unit or external storage device of the processing device) in the form of a program or instruction, and when the program or instruction is executed, process 300 may be implemented. As Figure 3 shown, process 300 may include the following operations.

[0104] Step 302, convert the Doppler ultrasound signal into frequency-domain data.

[0105] In some embodiments, the processing device may convert the Doppler ultrasound signal into frequency-domain data by performing a short-time Fourier transform on the Doppler ultrasound signal. The number of data of the short-time Fourier transform may be the same as the window size of the selected window function.

[0106] Step 304, based on the frequency-domain data, determine the first power parameter of the noise signal and the second power parameter of the user's blood flow signal.

[0107] The first power parameter of the noise signal may refer to the average power in the frequency domain, and the second power parameter of the noise signal is the maximum power in the frequency domain.

[0108] Step 306, based on the first power parameter and the second power parameter, generate parameters related to evaluating the noise intensity of the noise signal.

[0109] The parameters related to evaluating the noise intensity of the noise signal can be understood as parameters related to the power of the noise signal.

[0110] In some embodiments, the parameters related to evaluating the noise intensity of the noise signal may be the ratio of the first power parameter to the second power parameter.

[0111] In some embodiments, the parameters related to evaluating the noise intensity of the noise signal may also be the difference, square difference, mean square difference, etc. between the first power parameter and the second power parameter. This specification does not limit this.

[0112] For the description of power parameters and short-time Fourier transform, reference can be made to Figure 2 the relevant description, which will not be elaborated here.

[0113] Figure 4 Exemplary flowchart for generating a blood flow audio signal as shown in some embodiments of this specification. In some embodiments, process 400 may be executed by a processing device. For example, process 400 may be stored in a storage device (such as the built-in storage unit or external storage device of the processing device) in the form of a program or instruction, and when the program or instruction is executed, process 400 may be implemented. As Figure 4As shown, process 400 may include the following operations.

[0114] Step 402, splitting the noise-reduced frequency-domain data into a positive-frequency part and a negative-frequency part.

[0115] In some embodiments, the processing device may split the noise-reduced frequency-domain data into a positive-frequency part and a negative-frequency part based on a common frequency-domain data splitting method. For more details, reference can be made to existing relevant content, which will not be elaborated here.

[0116] Step 404, converting the positive-frequency part into a time-domain signal to generate a positive blood flow audio signal corresponding to the positive blood flow in the user's blood flow signal.

[0117] After splitting the noise-reduced frequency-domain data into a positive-frequency part and a negative-frequency part, the processing device may respectively take the conjugates of the positive-frequency part and the negative-frequency part and obtain the positive-frequency and negative-frequency parts again; the processing device may perform an inverse Fourier transform on the positive-frequency part and multiply it by a window function to convert it into a time-domain signal; the processing device may accumulate the time-domain signal corresponding to the positive-frequency part by moving the window function window in steps to obtain a positive blood flow audio signal corresponding to the positive blood flow in the user's blood flow signal.

[0118] Step 406, converting the negative-frequency part into a time-domain signal to generate a negative blood flow audio signal corresponding to the negative blood flow in the user's blood flow signal.

[0119] Among them, the method of generating the negative blood flow audio signal is the same as the method of generating the positive blood flow audio signal.

[0120] Regarding Figure 4 the specific implementation methods involved in each step, reference can be made to Figure 2 the relevant description, which will not be elaborated here.

[0121] It should be noted that the above descriptions are only for illustration and explanation, and do not limit the scope of application of this specification. For those skilled in the art, various modifications and changes can be made to each process under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, changes to the process steps of this specification, such as adding preprocessing steps and storage steps, etc.

[0122] Figure 5 is an exemplary module diagram of an audio noise reduction system according to some embodiments of this specification. As Figure 5 shown, system 500 may include an ultrasonic signal acquisition module 510, an ultrasonic signal segmentation processing module 520, an ultrasonic signal noise reduction processing module 530, and an audio signal generation module 540.

[0123] The ultrasonic signal acquisition module 510 can be used to acquire Doppler ultrasonic signals, and the Doppler ultrasonic signals include the blood flow signals and noise signals of the user.

[0124] The ultrasonic signal segmentation processing module 520 can be used to perform segmentation processing on the Doppler ultrasonic signals.

[0125] The ultrasonic signal noise reduction processing module 530 can be used to extract, for each segment of Doppler ultrasonic signals, parameters related to the power of the noise signals; and to select a target noise reduction algorithm from at least two noise reduction algorithms according to the values of the parameters to perform noise reduction processing on the segment of Doppler ultrasonic signals.

[0126] The audio signal generation module 540 can be used to synthesize each segment of Doppler ultrasonic signals after noise reduction and generate audio signals corresponding to the blood flow signals of the user.

[0127] For the detailed descriptions of the above system modules, reference can be made to the corresponding process part of this specification. For example, Figures 2 to 4 the relevant descriptions are not elaborated here.

[0128] It should be understood that Figure 5 the system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented through hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated design hardware. Those skilled in the art can understand that the above methods and systems can be implemented using computer-executable instructions and / or included in the processor control code. For example, such code is provided on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of this specification can be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips and transistors, or programmable hardware devices such as field programmable gate arrays and programmable logic devices, but also by software implemented by various types of processors, or by a combination of the above hardware circuits and software (for example, firmware).

[0129] It should be noted that the above description of the audio noise reduction system and its modules is only for convenience of description, and does not limit this specification to the scope of the examples given. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, make arbitrary combinations of the various modules, or form a subsystem and connect it with other modules. For example, in some embodiments, the ultrasonic signal acquisition module 510, the ultrasonic signal segmentation processing module 520, the ultrasonic signal noise reduction processing module 530, and the audio signal generation module 540 can be different modules in a system, or a single module can implement the functions of two or more of the above modules. For example, the various modules can share a storage module, or each module can have its own storage module respectively. Such variations are all within the scope of protection of this specification.

[0130] The beneficial effects that this application may bring include but are not limited to: (1) In the case of unknown noise, real-time denoising of the audio signal can be completed without prior estimation of the background noise; (2) Real-time filtering of the audio output of medical Doppler ultrasound detection can be performed, greatly reducing the background noise and better retaining the useful audio signal; (3) This method is simple in calculation, takes very little time, and does not affect the output speed of the audio.

[0131] It should be noted that different embodiments may produce different beneficial effects. In different embodiments, the beneficial effects that may be produced can be a combination of any one or more of the above, or any other beneficial effects that may be obtained.

[0132] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only an example and does not constitute a limitation to this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are proposed in this specification, so such modifications, improvements, and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0133] At the same time, this specification uses specific terms to describe the embodiments of this specification. Such as "one embodiment", "an embodiment", and / or "some embodiments" mean a certain feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that the "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more at different positions in this specification is not necessarily the same embodiment. In addition, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0134] In addition, those skilled in the art will understand that various aspects of this specification can be illustrated and described by several patentable categories or situations, including any new and useful process, machine, product, or composition of matter, or any new and useful improvement thereof. Accordingly, various aspects of this specification can be executed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software can all be referred to as "data blocks", "modules", "engines", "units", "components", or "systems". In addition, various aspects of this specification may be embodied as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0135] A computer storage medium may contain a propagated data signal having computer program code therein, for example, on a baseband or as part of a carrier wave. This propagated signal may take many forms, including electromagnetic, optical, or a suitable combination thereof. A computer storage medium can be any computer-readable medium other than a computer-readable storage medium, which can be connected to an instruction execution system, apparatus, or device to effect communication, propagation, or transmission for use of a program. The program code located on a computer storage medium can be propagated through any appropriate medium, including radio, cable, fiber optic cable, RF, or similar media, or any combination of the above media.

[0136] The computer program code required for the operation of various parts of this specification can be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python, etc., conventional procedural programming languages such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby, and Groovy, or other programming languages. The program code can run entirely on the user's computer, or as a stand-alone software package on the user's computer, or partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any network form, such as a local area network (LAN) or a wide area network (WAN), or connected to an external computer (for example, through the Internet), or in a cloud computing environment, or used as a service such as software as a service (SaaS).

[0137] In addition, unless clearly stated in the claims, the order of the processing elements and sequences, the use of numerical and alphabetical characters, or the use of other names described in this specification are not used to limit the order of the processes and methods in this specification. Although some currently useful embodiments of the invention are discussed through various examples in the above disclosure, it should be understood that such details are for illustrative purposes only. The appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that conform to the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only through software solutions, such as installing the described system on existing servers or mobile devices.

[0138] Similarly, it should be noted that, in order to simplify the presentation of the disclosure in this specification and thus help the understanding of one or more embodiments of the invention, in the foregoing description of the embodiments of this specification, sometimes multiple features are grouped into one embodiment, drawing, or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than those mentioned in the claims. In fact, the features of the embodiments are fewer than all the features of the individual embodiments disclosed above.

[0139] In some embodiments, numbers are used to describe components and the quantity of attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximate" or "substantially" in some examples. Unless otherwise stated, "about", "approximate" or "substantially" indicate that the stated number allows a variation of ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, and such approximate values may change according to the characteristics required by individual embodiments. In some embodiments, the numerical parameters should consider the specified significant digits and adopt the method of retaining the general number of digits. Although the numerical ranges and parameters used in some embodiments of this specification to confirm the breadth of their scope are approximate values, in specific embodiments, the setting of such numerical values is as precise as possible within the feasible range.

[0140] For each patent, patent application, patent application publication, and other materials cited in this specification, such as articles, books, specifications, publications, documents, etc., their entire contents are hereby incorporated into this specification by reference. This excludes the application history documents that are inconsistent with or conflict with the content of this specification, and also excludes the documents that limit the broadest scope of the claims of this specification (currently or subsequently appended to this specification). It should be noted that if there are any inconsistencies or conflicts between the descriptions, definitions, and / or uses of terms in the supplementary materials of this specification and the content described in this specification, the descriptions, definitions, and / or uses of terms in this specification shall prevail.

[0141] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly presented and described in this specification.

Claims

1. An audio noise reduction method, comprising: Obtaining a Doppler ultrasound signal, the Doppler ultrasound signal including a blood flow signal and a noise signal of a user; Performing segmentation processing on the Doppler ultrasound signal; For each segment of the Doppler ultrasound signal, Extracting a parameter related to the power of the noise signal; Selecting a target noise reduction algorithm from at least two noise reduction algorithms according to the value of the parameter to perform noise reduction processing on the segment of the Doppler ultrasound signal; and Synthesizing each segment of the Doppler ultrasound signal after noise reduction to generate an audio signal corresponding to the blood flow signal of the user; Wherein, for each segment of the Doppler ultrasound signal, extracting a parameter related to the power of the noise signal includes: Converting the Doppler ultrasound signal into frequency domain data; Based on the frequency domain data, determining a first power parameter of the noise signal and a second power parameter of the user's blood flow signal; the first power parameter is the average power in the frequency domain, the second power parameter is the maximum power in the frequency domain, and the parameter related to the power of the noise signal is the ratio of the first power parameter to the second power parameter.

2. The method according to claim 1, wherein performing segmentation processing on the Doppler ultrasound signal includes: Applying a window function to different time periods of the Doppler ultrasound signal respectively to obtain a segment of the Doppler ultrasound signal corresponding to each time period.

3. The method according to claim 1, wherein selecting a target noise reduction algorithm from at least two noise reduction algorithms to perform noise reduction processing on the segment of the Doppler ultrasound signal includes: In response to the value of the parameter being greater than a threshold, selecting a first noise reduction algorithm; In response to the value of the parameter being less than or equal to the threshold, selecting a second noise reduction algorithm, the second noise reduction algorithm and the first noise reduction algorithm including at least one different noise reduction parameter.

4. The method according to claim 3, wherein the first noise reduction algorithm and the second noise reduction algorithm are used to respectively remove the first power parameter with different weights from the frequency domain data.

5. The method according to claim 1, wherein the method further includes: Splitting the frequency domain data after noise reduction into a positive frequency part and a negative frequency part; Converting the positive frequency part into a time domain signal to generate a positive blood flow audio signal corresponding to the positive blood flow in the blood flow signal of the user; Converting the negative frequency part into a time domain signal to generate a negative blood flow audio signal corresponding to the negative blood flow in the blood flow signal of the user.

6. The method according to claim 5, wherein synthesizing each segment of the Doppler ultrasound signal after noise reduction to generate an audio signal corresponding to the blood flow signal of the user includes: Sequentially superimposing the positive blood flow audio signals corresponding to each segment of the Doppler ultrasound signal in the time domain to generate a positive blood flow audio signal that is continuous in the time domain; Or Sequentially superimposing the negative blood flow audio signals corresponding to each segment of the Doppler ultrasound signal in the time domain to generate a negative blood flow audio signal that is continuous in the time domain.

7. The method according to claim 1, wherein obtaining the Doppler ultrasound signal includes: Receiving an ultrasonic echo signal returned from the user for beamforming to obtain a beamformed signal; Summing the beamformed signals within a sampling frame of a target position of the user set. Perform wall filtering on the summed signal to obtain the Doppler ultrasound signal.

8. An audio noise reduction system, comprising: An ultrasonic signal acquisition module for acquiring a Doppler ultrasound signal, where the Doppler ultrasound signal includes a user's blood flow signal and a noise signal; An ultrasonic signal segmentation processing module for segmenting the Doppler ultrasound signal; An ultrasonic signal noise reduction processing module for each segment of the Doppler ultrasound signal, extracting a parameter related to the power of the noise signal; selecting a target noise reduction algorithm from at least two noise reduction algorithms according to the value of the parameter to perform noise reduction processing on the segment of the Doppler ultrasound signal; and An audio signal generation module for synthesizing each segment of the Doppler ultrasound signal after noise reduction to generate an audio signal corresponding to the user's blood flow signal; wherein, for each segment of the Doppler ultrasound signal, extracting a parameter related to the power of the noise signal includes: converting the Doppler ultrasound signal into frequency domain data; based on the frequency domain data, determining a first power parameter of the noise signal and a second power parameter of the user's blood flow signal; the first power parameter is the average power in the frequency domain, the second power parameter is the maximum power in the frequency domain, and the parameter related to the power of the noise signal is the ratio of the first power parameter to the second power parameter.

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