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WO2018161429A1 - 一种噪声检测方法及终端设备 - Google Patents

一种噪声检测方法及终端设备 Download PDF

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Publication number
WO2018161429A1
WO2018161429A1 PCT/CN2017/083765 CN2017083765W WO2018161429A1 WO 2018161429 A1 WO2018161429 A1 WO 2018161429A1 CN 2017083765 W CN2017083765 W CN 2017083765W WO 2018161429 A1 WO2018161429 A1 WO 2018161429A1
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Prior art keywords
frequency
noise
audio signal
frame
terminal device
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PCT/CN2017/083765
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English (en)
French (fr)
Inventor
张健
张海宏
陶蓓
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华为技术有限公司
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Priority to CN201780026318.9A priority Critical patent/CN109074814B/zh
Publication of WO2018161429A1 publication Critical patent/WO2018161429A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M9/00—Arrangements for interconnection not involving centralised switching
    • H04M9/08—Two-way loud-speaking telephone systems with means for conditioning the signal, e.g. for suppressing echoes for one or both directions of traffic
    • H04M9/082—Two-way loud-speaking telephone systems with means for conditioning the signal, e.g. for suppressing echoes for one or both directions of traffic using echo cancellers
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/02—Constructional features of telephone sets
    • H04M1/19—Arrangements of transmitters, receivers, or complete sets to prevent eavesdropping, to attenuate local noise or to prevent undesired transmission; Mouthpieces or receivers specially adapted therefor
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the present application relates to the field of communication systems and the field of voice processing, and in particular, to a noise detection method and a terminal device.
  • Time Division Duplexing (TDD) noise and board vibration are "current sounds" caused by intermittently emitting large currents in mobile phones.
  • the Power Amplifier (PA) of the Time Division Multiple Access (TDMA) mobile phone of the Global System for Mobile communication (GSM) system transmits a large power every 4.616 ms.
  • the current causes the electro-acoustic device to receive interference and demodulate the TDD noise.
  • the battery voltage drops at the same frequency, acting on the ceramic capacitor, causing mechanical vibration and passing through the main board to form a plate vibration.
  • the input signal x(t) is first transformed from the time domain to the spectral domain to obtain the spectral signal S(f), and secondly, the spectral signal S(f) is transformed from the spectral domain to the cepstrum domain.
  • the threshold determines that the input signal is TDD noise; if the spectral signal C(q 1 ) corresponding to the frequency q 1 of the input signal in the cepstrum domain is not greater than a preset threshold, it is determined that the input signal is not TDD noise.
  • the cepstrum domain cannot quantitatively indicate the size of the input signal due to the characteristics of the cepstrum domain itself. Therefore, the existing detection technique can only judge that the input signal is TDD noise, or the input signal is not TDD noise. However, when it is determined that the input signal is TDD noise, the TDD noise level cannot be determined.
  • the embodiment of the present application provides a noise detecting method and a terminal device for detecting a noise energy amount in a signal.
  • the first aspect of the present application provides a noise detection method, including:
  • the amplitude spectrum of each frame signal is calculated according to the first formula; secondly, the noise frequency is determined according to the candidate noise frequency distribution, wherein the candidate noise frequency distribution is obtained by performing cepstrum analysis on each frame signal; finally, in determining After the amplitude spectrum and the noise frequency of each frame signal, and correspondingly calculating the noise energy value of each frame signal according to the above noise frequency and amplitude spectrum, it can be understood that the above noise energy value can represent the noise level in each frame signal.
  • the noise detection method further includes:
  • each frequency of the amplitude value of each frame of the audio signal exceeds a preset threshold is determined as each target frequency, wherein the frequency search interval is a frequency interval in the cepstrum domain;
  • Each target frequency having the largest geometric mean value corresponding to the amplitude value in each frame of the audio signal is determined as each candidate noise frequency.
  • the target frequency is first determined in each frame of the audio signal, and then the target in each frame of the audio signal The frequency is filtered out of the candidate noise frequency. In this way, each candidate noise frequency is effectively selected from each frame of the audio signal.
  • the noise method before calculating the amplitude value spectrum of each frame of the audio signal according to the first formula, the noise method further includes:
  • the sampled signal is subjected to framing and windowing to obtain at least two frames of audio signals.
  • the framing operation and windowing processing of the sampled signal can improve the performance of the algorithm for calculating the audio signal, and can also obtain the TDD noise or the sound of the board vibration over time. The relationship of change.
  • the noise detecting method before the sampling signal is subjected to framing and windowing to obtain at least two or more audio signals, the noise detecting method further includes:
  • the input audio signal is N-times up-sampled to obtain a sampled signal, where N is a positive integer not less than 2.
  • the input audio signal is sampled by using N times of interpolation and upsampling, so that the noise frequency is far away from the low frequency interference, thereby improving the accuracy of the noise detection detection.
  • the preset calculation method includes a loudness calculation method.
  • the loudness calculation method can be used to calculate the noise energy size efficiently and accurately.
  • the embodiment of the present application provides a terminal device, where the terminal device has the function of implementing the behavior of the terminal device in the foregoing method embodiment.
  • This function can be implemented in hardware or in hardware by executing the corresponding software.
  • the hardware or software includes one or more modules corresponding to the functions described above.
  • an embodiment of the present application provides a terminal device, including: a processor, a memory, a bus, a transmitter, and a receiver; the memory is configured to store a computer to execute an instruction, and the processor is connected to the memory through the bus.
  • the processor executes the computer-executed instruction stored in the memory to cause the terminal device to perform the noise detecting method according to any one of the above first aspects.
  • an embodiment of the present application provides a computer readable storage medium, configured to be stored as the foregoing terminal device.
  • the computer software instructions used, when run on a computer, cause the computer to perform the noise detection method of any of the above first aspects.
  • an embodiment of the present application provides a computer program product comprising instructions, which when executed on a computer, enable the computer to perform the noise detecting method of any of the above first aspects.
  • FIG. 1 is a schematic diagram of an embodiment of a noise detecting method in an embodiment of the present application
  • FIG. 2 is a schematic diagram of an embodiment of a terminal device according to an embodiment of the present application.
  • FIG. 3 is a schematic diagram of another embodiment of a terminal device according to an embodiment of the present application.
  • FIG. 4 is a schematic diagram of another embodiment of a terminal device according to an embodiment of the present application.
  • the embodiment of the present application provides a noise detecting method and a terminal device for detecting a noise energy amount in a signal.
  • the noise detecting method in the embodiment of the present application is mainly used for detecting TDD noise and board vibration in a mobile phone, and of course, can also be used for noises of other harmonic forms, and the present application does not impose any limitation.
  • the noise detection method in the embodiment of the present application will be described below in conjunction with a specific embodiment.
  • Embodiment 1 As shown in FIG. 1 , an embodiment of a noise detecting method in an embodiment of the present application includes:
  • the input audio signal is subjected to N times of interpolation upsampling to obtain a sampling signal, where N is a positive integer greater than or equal to 2.
  • the input audio signal is X
  • the output sampling signal is Z.
  • the N-time interpolation upsampling of the input audio signal does not change the amplitude value of the input audio signal, and actually increases the sampling frequency by N times.
  • the present application does not impose any restrictions on other sampling schemes that can achieve the same technical effects.
  • the sampling signal is performed.
  • the framing operation and the windowing process result in at least two frames of audio signals.
  • W can be a common window function (such as a rectangular window, a triangular window, a Hanning window, a Hamming window, and One of the Gaussian windows, etc., or other newly designed window functions, is not limited in this application.
  • the framing operation can not only improve the performance of the algorithm for calculating the audio signal, but also the relationship between the TDD noise or the amplitude of the plate vibration sound over time.
  • the framing operation does not change the amplitude value of the input audio signal, and the windowing process mainly mitigates the spectrum leakage problem.
  • the amplitude spectrum of each frame of the audio signal is calculated according to the first formula, and the amplitude spectrum of each frame of the audio signal is obtained.
  • M i abs (fft ( Y i)); expression of M i
  • the cepstrum of each frame of the audio signal is calculated according to the second formula, and the cepstrum of each frame of the audio signal is obtained.
  • C i represents an ith frame audio signal in the cepstrum domain
  • C i can be expressed as:
  • C i real(ifft(log(M i )));
  • log represents the logarithm;
  • ift represents the fast inverse Fourier transform, real represents the real number.
  • each candidate noise frequency in the frequency search interval is a frequency range corresponding to the cepstrum domain, and the frequency search interval thereof.
  • the specific determination manner may be preset according to the experience of the prophet, or may be determined according to different mobile phone systems, and the application does not impose any restrictions.
  • determining each candidate noise frequency within the frequency search interval is:
  • the frequency search interval in the cepstrum domain is determined according to the experience of the prophet. It should be noted that there are many amplitude values in the cepstrum domain, but the amplitude values in the cepstrum domain cannot quantitatively represent the audio signal of each frame. Size
  • the preset threshold is used to select each frequency of each frame signal that exceeds the preset threshold as the target frequency, and the preset threshold is substantially a preset amplitude value (for example, the maximum amplitude in the cepstrum domain may be specifically
  • the value of the preset threshold can be set according to the actual application scenario, and the present application does not impose any restrictions on this;
  • each target frequency selected from each frame signal is used as the fundamental frequency f base , and the second harmonic frequency f 2 and the third harmonic frequency f 3 corresponding to the fundamental frequency are sequentially calculated to obtain a fundamental frequency f base .
  • the three frequencies are calculated according to the geometric square value formula, and the geometric square value p(f base ) of the corresponding amplitude value in the cepstrum domain, wherein, Ground, the geometric squared formula can be:
  • Other forms of geometric squared formulas are also possible, and no limitation is imposed on this application.
  • the target frequency with the largest geometric square value in each of the calculated audio signals is used as the candidate noise frequency of the current frame.
  • the noise frequency is determined from each candidate noise frequency.
  • a possible way to determine the noise frequency from each candidate noise frequency is: counting the number of occurrences of each of the candidate noise frequencies, and then the most occurrences, and the total number of occurrences of all frequencies
  • the frequency of the number of times (frames) exceeds the preset threshold is determined as the noise frequency, and there is a frequency f t , and the number of occurrences accounts for 60% of the number of occurrences of all frequencies (one candidate frequency per frame, that is, the total number of frames). Greater than 50% threshold. That is, the noise is considered to be TDD noise or plate vibration, and f t is its noise frequency.
  • the present application does not impose any limitation. If the number of times without any candidate frequency exceeds the preset threshold, it can be judged that this noise is not TDD noise or plate vibration, that is, it is not necessary to calculate its energy value.
  • the noise energy value corresponding to the noise frequency in each frame of the audio signal is calculated according to the noise frequency, the amplitude spectrum, and a preset calculation method, wherein the noise energy value is represented by The size of the TDD noise or the plate vibration sound, if the noise energy value is higher, the TDD noise or the plate vibration sound is larger; if the noise energy value is lower, the TDD noise or the plate vibration sound is smaller.
  • one possible way to calculate the noise energy value is to calculate the noise energy value of the TDD noise or the plate vibration sound by using the loudness calculation method. At the same time, the energy value corresponding to the second harmonic frequency f 2 and the third harmonic frequency f 3 corresponding to the noise frequency can be calculated. In addition, for other calculation methods, the application does not impose any restrictions.
  • the corresponding amplitude spectrum is obtained in advance by calculating each frame signal, and then the noise frequency is determined by the candidate noise frequency distribution obtained by performing cepstrum analysis on each frame signal, and finally, according to the above amplitude spectrum and the above The noise frequency is calculated correspondingly to obtain the noise energy value of each frame signal. Therefore, the embodiment of the present application can effectively detect the noise energy amount in each frame signal.
  • an embodiment of a terminal device in this embodiment of the present application includes:
  • a first calculating module 201 configured to calculate an amplitude spectrum of each frame of the audio signal according to the first formula
  • the first determining module 202 is configured to determine a noise frequency according to each candidate noise frequency, where each candidate noise frequency is obtained by performing cepstrum analysis on each frame of the audio signal;
  • a second calculating module 203 configured to calculate, according to the noise frequency, the amplitude spectrum, and a preset calculation method The noise energy value of each frame of the audio signal.
  • the terminal device further includes: a second determining module 304, a third calculating module 305, and a third determining module 306; wherein each module function is as follows:
  • the second determining module 304 is configured to determine, in the preset frequency search interval, each frequency of the amplitude value of each frame of the audio signal exceeding a preset threshold as each target frequency;
  • a third calculating module 305 configured to calculate, as a fundamental frequency, a fundamental frequency, a second harmonic frequency of the fundamental frequency, and a geometric mean value of the amplitude value respectively corresponding to the third harmonic frequency of the fundamental frequency;
  • the third determining module 306 is configured to determine each target frequency corresponding to the maximum geometric mean as each candidate noise frequency.
  • the terminal device further includes: a first processing module 307, wherein the first processing module 307 is configured to perform framing and windowing processing on the sampling signal, Obtain at least two frames of audio signals.
  • the terminal device further includes: a second processing module 308, wherein the second processing module 308 is configured to perform N times of insertion of the input audio signal. Sampling, the sampled signal is obtained, wherein the N is a positive integer not less than 2.
  • the corresponding amplitude spectrum is obtained in advance by calculating each frame signal, and then the noise frequency is determined by the candidate noise frequency distribution obtained by performing cepstrum analysis on each frame signal, and finally, according to the above amplitude spectrum and the above The noise frequency is calculated correspondingly to obtain the noise energy value of each frame signal. Therefore, the embodiment of the present application can effectively detect the noise energy amount in each frame signal.
  • the foregoing embodiment 2 describes the terminal device in the embodiment of the present application in detail from the aspect of the virtual functional device.
  • the following describes the terminal device in the embodiment of the present application from the aspect of the physical structure, which may be specifically as follows:
  • the third embodiment as shown in FIG. 4, another embodiment of the terminal device in the embodiment of the present application includes: a receiver 401, a transmitter 402, a processor 403, a memory 404, and a bus 405.
  • the memory 404 can include read only memory and random access memory and provides instructions and data to the processor 403.
  • a portion of the memory 404 may also include a non-volatile random access memory (English name: Non-Volatile Random Access Memory, English abbreviation: NVRAM).
  • the memory 404 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof:
  • Operation instructions including various operation instructions for implementing various operations
  • Operating system Includes a variety of system programs for implementing various basic services and handling hardware-based tasks.
  • the processor 403 in the embodiment of the present application may be used to perform operations corresponding to the first communication network element in the foregoing embodiment, and may include the following operations:
  • each candidate noise frequency is obtained by performing cepstrum analysis on each frame of the audio signal
  • the amplitude spectrum is calculated by a preset calculation method to obtain a noise energy value of each frame of the audio signal.
  • the processor 403 may be configured to: determine, in a preset frequency search interval, each frequency in the audio signal of each frame that exceeds a preset threshold as each target frequency;
  • each target frequency as a fundamental frequency, calculating a fundamental frequency, a second harmonic frequency of the fundamental frequency, and a geometric mean value of the amplitude value corresponding to the third harmonic frequency of the fundamental frequency;
  • Each target frequency corresponding to the maximum geometric mean is determined as each candidate noise frequency.
  • the processor 403 is configured to perform the following steps: performing N-times up-sampling on the input audio signal to obtain the sampling signal, where the N is a positive integer not less than 2;
  • the sampled signal is subjected to framing and windowing to obtain at least two frames of audio signals.
  • the processor 403 controls the operation of the first communication network element, and the processor 403 may also be referred to as a central processing unit (English full name: Central Processing Unit, English abbreviation: CPU).
  • Memory 404 can include read only memory and random access memory and provides instructions and data to processor 403. A portion of memory 404 may also include NVRAM.
  • the components of the first communication network element are coupled together by a bus system 405.
  • the bus system 405 may include a power bus, a control bus, a status signal bus, and the like in addition to the data bus. However, for clarity of description, various buses are labeled as bus system 405 in the figure.
  • Processor 403 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the foregoing method may be completed by an integrated logic circuit of hardware in the processor 403 or an instruction in a form of software.
  • the processor 403 may be a general-purpose processor, a digital signal processor (English name: Digital Signal Processing, English abbreviation: DSP), an application specific integrated circuit (English name: Application Specific Integrated Circuit, English abbreviation: ASIC), ready-made programmable Gate array (English name: Field-Programmable Gate Array, English abbreviation: FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • the methods, steps, and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the steps of the method disclosed in the embodiments of the present application may be directly implemented by the hardware decoding processor, or may be performed by a combination of hardware and software modules in the decoding processor.
  • the software module can be located in a conventional storage medium such as random access memory, flash memory, read only memory, programmable read only memory or electrically erasable programmable memory, registers, and the like.
  • the storage medium is located in memory 404, and processor 403 reads the information in memory 404 and, in conjunction with its hardware, performs the steps of the above method.
  • FIG. 4 The related description of FIG. 4 can be understood by referring to the related description and effect of the method part of FIG. 1, and no further description is made here.
  • the disclosed system, apparatus, and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the unit is only a logical function division.
  • there may be another division manner for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, and may be in an electrical, mechanical or other form.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated unit if implemented in the form of a software functional unit and sold or used as a standalone product, may be stored in a computer readable storage medium.
  • a computer readable storage medium A number of instructions are included to cause a computer device (which may be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present application.
  • the foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and the like. .

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  • Human Computer Interaction (AREA)
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Abstract

一种噪声检测方法及终端设备，用于检测信号中噪声能量大小。该噪声监测方法包括：根据第一公式计算每帧音频信号的幅度谱（103）；根据各候选噪声频率确定噪声频率（106），该各候选噪声频率由对每帧音频信号进行倒谱分析得到（104，105）；根据噪声频率、幅度谱以及预设计算方法进行计算得到每帧音频信号的噪声能量值（107）。

Description

一种噪声检测方法及终端设备
本申请要求于2017年03月07日提交中国专利局、申请号为201710131996.3、发明名称为“一种时分双工噪声检测方法和设备”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及通信系统领域以及语音处理领域，尤其涉及一种噪声检测方法及终端设备。
背景技术
时分双工(Time Division Duplexing，TDD)噪声和板振都是由于手机间歇性发射大电流引起的“电流声”。具体来说，以全球移动通信系统(Global System for Mobile communication，GSM)的时分多址(Time Division Multiple Access，TDMA)制式手机的功率放大器(Power Amplifier，PA)大约每隔4.616ms都会发射一个大电流，使得电声器件收到干扰，解调出TDD噪声。与此同时，电池电压以同样的频率跌落，作用在陶瓷电容上，导致其出现机械振动并通过主板传递，形成板振声音。
现有检测技术，首先，将输入信号x(t)先由时域变换至频谱域得到频谱信号S(f)，其次，再将上述频谱信号S(f)由频谱域变换至倒谱域得到倒谱信号C(q)，最后，在倒谱域中找到TDD噪声对应的频率q1(如设信号采样频率为fs，TDD噪声频率为217Hz，则倒谱域的频率的计算公式为：q1＝fs/(2*217))，并设置预置阈值来判断输入信号是否为TDD噪声，若输入信号在倒谱域中频率q1对应的频谱信号C(q1)大于预置阈值，则确定输入信号是TDD噪声；若输入信号在倒谱域中频率q1对应的频谱信号C(q1)不大于预置阈值，则确定输入信号不是TDD噪声。
在现有检测技术中，由于倒谱域本身的特性导致倒谱域无法定量表示出输入信号的大小，因此，通过现有检测技术只能判断出输入信号是TDD噪声，或输入信号不是TDD噪声，而当确定输入信号是TDD噪声时，仍然无法确定出其TDD噪声大小。
发明内容
本申请实施例提供了一种噪声检测方法及终端设备，用于检测信号中噪声能量大小。
第一方面本申请实施例提供了一种噪声检测方法，包括：
首先，根据第一公式计算每帧信号的幅度谱；其次，根据候选噪声频率分布确定噪声频率，其中，上述候选噪声频率分布为通过对每帧信号进行倒谱分析之后得到的；最后，在确定每帧信号的幅度谱和噪声频率之后，进而根据上述噪声频率和幅度谱进行相应的计算得到每帧信号的噪声能量值，可以理解的是，上述噪声能量值可以表征每帧信号中噪声大小。
从本申请实施例第一方面提供的技术方案中，可以看出本申请实施例具有以下优点：
通过对每帧信号进行计算预先得到相应的幅度谱，进而通过由对每帧信号进行倒谱分 析得到的候选噪声频率分布，确定出噪声频率，最终，根据上述幅度谱和上述噪声频率进行相应的计算得到每帧信号的噪声能量值，因此，本申请实施例可以有效地检测出每帧信号中噪声能量大小。在一种可能的设计中，在第一方面的第一种可能的实现方式中，在根据各候选噪声频率确定噪声频率之前，该噪声检测方法还包括：
在预置的频率搜索区间内，将每帧音频信号中幅度值超过预设阈值的各频率确定为各目标频率，其中，频率搜索区间为倒谱域中的频率区间；
将每个目标频率作为基频，计算基频，基频的二次谐波频率，和所述基频的三次谐波频率分别对应的幅度值的几何平均值；
将每帧音频信号中幅度值对应的几何平均值最大的各目标频率确定为各候选噪声频率。
其次，在第一方面的第一种可能的实现方式中，在倒谱域中预置的频率搜索区间内，先将每帧音频信号中确定出目标频率，再从每帧音频信号中的目标频率筛选出候选噪声频率。这样，有效地将各候选噪声频率从每帧音频信号中选择出来。
在一种可能的设计中，在第一方面的第二种可能的实现方式中，在根据第一公式计算每帧音频信号的幅度值谱之前，该噪声方法还包括：
将采样信号进行分帧和加窗处理，得到至少两帧音频信号。
其次，在第一方面的第二种可能的实现方式中，对采样信号进行分帧操作和加窗处理既可以提高计算音频信号的算法性能，还可以得出TDD噪声或板振声音大小随时间的变化关系。
在一种可能的设计中，在第一方面的第三种可能的实现方式中，在将采样信号进行分帧和加窗处理，得到至少两帧以上音频信号之前，该噪声检测方法还包括：
将输入音频信号进行N倍的插零上采样，得到采样信号，其中，N为不小于2的正整数。
其次，在第一方面的第三种可能的实现方式中，使用N倍的插零上采样对输入音频信号进行采样处理，可以使得噪声频率远离低频的干扰，从而提高噪声检测检测的准确性
在一种可能的设计中，在第一方面的第四种可能的实现方式中，上述预设计算方法包括响度计算方法。
其次，在第一方面的第四种可能的实现方式中，使用响度计算方法可以有效并准确地将噪声能量大小计算出来。
第二方面，本申请实施例提供一种终端设备，该终端设备具有实现上述方法实施例中终端设备行为的功能。该功能可以通过硬件实现，也可以通过硬件执行相应的软件实现。该硬件或软件包括一个或多个与上述功能相对应的模块。
第三方面，本申请实施例提供一种终端设备，包括：处理器、存储器、总线、发射器和接收器；该存储器用于存储计算机执行指令，该处理器与该存储器通过该总线连接，当该终端设备运行时，该处理器执行该存储器存储的该计算机执行指令，以使该终端设备执行如上述第一方面任意一项的噪声检测方法。
第四方面，本申请实施例提供了一种计算机可读存储介质，用于储存为上述终端设备 所用的计算机软件指令，当其在计算机上运行时，使得计算机可以执行上述第一方面中任意一项的噪声检测方法。
第五方面，本申请实施例提供了一种包含指令的计算机程序产品，当其在计算机上运行时，使得计算机可以执行上述第一方面中任意一项的噪声检测方法。
另外，第二方面至第五方面中任一种设计方式所带来的技术效果可参见第一方面中不同设计方式所带来的技术效果，此处不再赘述。
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图1为本申请实施例中噪声检测方法的一个实施例示意图；
图2为本申请实施例中终端设备的一个实施例示意图；
图3为本申请实施例中终端设备的另一个实施例示意图；
图4为本申请实施例中终端设备的另一个实施例示意图。
具体实施方式
本申请实施例提供了一噪声检测方法及终端设备，用于检测信号中噪声能量大小。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”、“第三”、“第四”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的实施例能够以除了在这里图示或描述的内容以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。
本申请实施例中噪声检测方法主要用于检测手机中TDD噪声和板振声音，当然，还可以用于其他谐波形式类似的噪声，对此本申请不做任何限制。
为了便于理解本申请实施例中噪声检测方法，下面将结合具体的实施例来对本申请实施例中噪声检测方法进行说明。
实施例一，如图1所示，本申请实施例中噪声检测方法的一个实施例，包括：
101、将输入音频信号进行N倍的插值上采样，得到采样信号。
本实施例中，可选地，对输入音频信号进行N倍的插值上采样，得到采样信号，其中，N为大于或等于2的正整数。具体可以为：设输入音频信号为X，输出的采样信号为Z，根据上采样原理可以得到X和Z之间的关系，可以用关系式：Z＝upsample(X，N)表示，其中，需要说明的是upsample表示上采样函数。
其次，还需要说明的是，对输入音频信号进行N倍的插值上采样不会改变该输入音频信号的幅度值大小，实际上是将采样频率提高为原来的N倍。此外，对于其他能达到相同技术效果的采样方案，本申请不做任何限制。
102、将采样信号进行分帧和加窗处理，得到至少两帧音频信号。
本实施例中，可选地，在将输入信号进行采样，得到采样信号之后，将采样信号进行 分帧操作和加窗处理，得到至少两帧音频信号。
可选地，分帧操作可具体为：以Z(k)表示采样信号Z中第k个点，设每帧音频信号的长度为l，每两帧音频信号之间的移动步长为s，则第i帧音频信号Zi为Z中的l个点，其中，Zi可以表示为：Zi＝[z*(1+(i-1)*s)，z*(2+(i-1)*s)，......，z*(l+(i-1)*s)]，l，i均为正整数，s为大于0且小于l的正整数，其中，s的常用典型值为l/2，l/4或l/8等。
可选地，加窗处理可具体为：设加窗函数为W，则W可表示为：Wi＝[W(1)，W(2)，.....，W(l)]，其中需要说明的是，窗函数W可根据实际需要进行选择，对此本申请不做限制，加窗函数W具体可为常用窗函数(如矩形窗，三角窗，汉宁窗，海明窗和高斯窗等)中的一种，或者也可以是其他新设计的窗函数，对此本申请也不做任何限制。
可选地，在经过上述分帧操作和加窗处理之后，得到的至少两帧音频信号Yi可表示为：Yi＝[W(1)*Zi(1)，W(2)*Zi(2)，......，W(l)*Zi(l)]。
其次，需要说明的是，分帧操作既可以提高计算音频信号的算法性能，还可以得出TDD噪声或板振声音大小随时间的变化关系。此外，分帧操作均不会改变输入音频信号的幅度值大小，加窗处理主要是缓解频谱泄露问题。
103、根据第一公式计算每帧音频信号的幅度谱。
本实施例中，在经过分帧操作和加窗处理得到至少两帧音频信号之后，根据第一公式对每帧音频信号的幅度谱进行计算，并得到每帧音频信号的幅度谱。
可选地，在一种可能的计算方式中，以Mi表示频谱域中第i帧音频信号，则Mi可表示为：Mi＝abs(fft(Yi))；Mi的表达式中abs表示取绝对值；fft表示快速傅里叶变换。
104、根据第二公式计算每帧音频信号的倒谱。
本实施例中，在计算得到每帧音频信号的幅度谱之后，根据第二公式对每帧音频信号的倒谱进行计算，并得到每帧音频信号的倒谱。
可选地，在一种可能的计算方式中，以Ci表示倒谱域中第i帧音频信号，则Ci可表示为：Ci＝real(ifft(log(Mi)))；其中，log表示取对数；ifft表示快速逆傅里叶变换，real表示取实数。
105、在频率搜索区间内确定各候选噪声频率。
本实施例中，可选地，在计算得到每帧音频信号的倒谱之后，在频率搜索区间内确定各候选噪声频率，其中，频率搜索区间为倒谱域对应的频率范围，其频率搜索区间的具体确定方式可以是根据先知经验预先设置，也可以是根据不同的手机制式进行确定，对此本申请不做任何限制。
可选地，在频率搜索区间内确定各候选噪声频率的一种可能的实现方式为：
首先，根据先知经验确定倒谱域中的频率搜索区间，其中，需要说明的是，在倒谱域中会存在许多幅度值，但是倒谱域中的幅度值是无法定量表示出每帧音频信号大小的；
其次，通过预设阈值去选择出每帧信号中超过该预设阈值的各频率作为各目标频率，预设阈值实质上是一个预先设定的幅度值(如具体可以为倒谱域中最大幅度值的0.1倍)，对于预设阈值的具体大小可根据实际应用场景进行设定，本申请对此不做任何限制；
再次，将从每帧信号中选择出来的各目标频率作为基频fbase，并依次计算出该基频对应的二次谐波频率f2和三次谐波频率f3，得到基频fbase，二次谐波频率f2和三次谐波频率f3之后，再根据几何平方值公式计算出三者频率，在倒谱域中对应幅度值的几何平方值p(fbase)，其中，可选地，几何平方值公式可以是：
Figure PCTCN2017083765-appb-000001
也可以是其他形式的几何平方值公式，对此本申请不做任何限制。
最后，在将计算得到的每帧音频信号中几何平方值最大的目标频率作为本帧的候选噪声频率。
106、根据各候选噪声频率确定噪声频率。
本实施例中，在搜索并确定各噪声频率之后，在从各候选噪声频率中确定出噪声频率。
可选地，一种可能的从各候选噪声频率中确定出噪声频率的方式为：统计各候选噪声频率中每种噪声频率的出现次数，进而将出现次数最多，且出现次数所占所有频率总次数(帧数)的比例超过预设门限的频率确定为噪声频率，有频率ft，出现次数占所有频率出现次数(每帧都有1个候选频率，即总数为帧数)的60％，大于门限50％。即认为该噪声为TDD噪声或者板振，ft为其噪声频率，当然，也可以是其他可能的确定方式，对此本申请不做任何限制。如果没有任何一个候选频率的次数超过预设门限，可以判断这个噪声并不是TDD噪声或者板振声音，即不必再计算其能量值。
107、根据噪声频率，幅度谱和预设计算方法进行计算得到每帧音频信号的噪声能量值。
本实施例中，在根据各候选噪声频率确定噪声频率之后，根据噪声频率，幅度谱和预设计算方法计算出每帧音频信号中的噪声频率对应的噪声能量值，其中，该噪声能量值表示TDD噪声或板振声音的大小，若噪声能量值越高，则TDD噪声或板振声音越大；若噪声能量值越低，则TDD噪声或板振声音越小。
可选地，一种可能的计算噪声能量值的方式为：通过使用响度计算方法计算TDD噪声或板振声音的噪声能量值。同时还可以将噪声频率对应的二次谐波频率f2和三次谐波频率f3对应的能量值计算出来。此外，对于其他计算方式，本申请不做任何限制。
本实施例中，通过对每帧信号进行计算预先得到相应的幅度谱，进而通过由对每帧信号进行倒谱分析得到的候选噪声频率分布，确定出噪声频率，最终，根据上述幅度谱和上述噪声频率进行相应的计算得到每帧信号的噪声能量值，因此，本申请实施例可以有效地检测出每帧信号中噪声能量大小。
上述实施例一对本申请中噪声检测方法进行了详细说明，为了便于理解本申请实施例中终端设备，下面将结合具体的实施例对本申请实施例中终端设备进行说明。
实施例二，如图2所示，本申请实施例中终端设备的一个实施例，包括：
第一计算模块201，用于根据第一公式计算每帧音频信号的幅度谱；
第一确定模块202，用于根据各候选噪声频率确定噪声频率，该各候选噪声频率由对每帧音频信号进行倒谱分析得到；
第二计算模块203，用于根据该噪声频率，该幅度谱，和预设计算方法进行计算得到 每帧音频信号的噪声能量值。
如图3所示，可选地，在一种可能的设计中，终端设备还包括：第二确定模块304，第三计算模块305和第三确定模块306；其中，各模块功能具体如下：
第二确定模块304，用于在预置的频率搜索区间内，将每帧音频信号中幅度值超过预设阈值的各频率确定为各目标频率；
第三计算模块305，用于将每个目标频率作为基频，计算基频，该基频的二次谐波频率，和该基频的三次谐波频率分别对应的幅度值的几何平均值；
第三确定模块306，用于将最大几何平均值对应的各目标频率确定为各候选噪声频率。
如图3所示，可选地，在一种可能的设计中，终端设备还包括：第一处理模块307，其中，第一处理模块307，用于将采样信号进行分帧和加窗处理，得到至少两帧音频信号。
如图3所示，可选地，在一种可能的设计中，终端设备还包括：第二处理模块308，其中，第二处理模块308，用于将输入音频信号进行N倍的插零上采样，得到该采样信号，其中，该N为不小于2的正整数。
本实施例中，通过对每帧信号进行计算预先得到相应的幅度谱，进而通过由对每帧信号进行倒谱分析得到的候选噪声频率分布，确定出噪声频率，最终，根据上述幅度谱和上述噪声频率进行相应的计算得到每帧信号的噪声能量值，因此，本申请实施例可以有效地检测出每帧信号中噪声能量大小。
上述实施例二从虚拟的功能性装置方面对本申请实施例中终端设备进行了详细说明，下面从实体结构方面对本申请实施例中终端设备进行说明，可具体如下：
实施例三，如图4所示，本申请实施例中终端设备的另一个实施例，包括：接收器401、发射器402、处理器403、存储器404和总线405。
其中，存储器404可以包括只读存储器和随机存取存储器，并向处理器403提供指令和数据。存储器404的一部分还可以包括非易失性随机存取存储器(英文全称：Non-Volatile Random Access Memory，英文缩写：NVRAM)。
存储器404存储了如下的元素，可执行模块或者数据结构，或者它们的子集，或者它们的扩展集：
操作指令：包括各种操作指令，用于实现各种操作；
操作系统：包括各种系统程序，用于实现各种基础业务以及处理基于硬件的任务。
其中，本申请实施例中处理器403可以用于执行上述实施例中第一通信网元对应的操作，可以包括如下操作：
根据第一公式计算每帧音频信号的幅度谱；
根据各候选噪声频率确定噪声频率，该各候选噪声频率由对每帧音频信号进行倒谱分析得到；
根据该噪声频率，该幅度谱，和预设计算方法进行计算得到每帧音频信号的噪声能量值。
可选的，处理器403可以用于执行如下步骤：在预置的频率搜索区间内，将每帧音频信号中幅度值超过预设阈值的各频率确定为各目标频率；
将每个目标频率作为基频，计算基频，该基频的二次谐波频率，和该基频的三次谐波频率分别对应的幅度值的几何平均值；
将最大几何平均值对应的各目标频率确定为各候选噪声频率。
可选的，处理器403可以用于执行如下步骤：将输入音频信号进行N倍的插零上采样，得到该采样信号，其中，该N为不小于2的正整数；
将采样信号进行分帧和加窗处理，得到至少两帧音频信号。
处理器403控制第一通信网元的操作，处理器403还可以称为中央处理单元(英文全称：Central Processing Unit，英文缩写：CPU)。存储器404可以包括只读存储器和随机存取存储器，并向处理器403提供指令和数据。存储器404的一部分还可以包括NVRAM。具体的应用中，第一通信网元的各个组件通过总线系统405耦合在一起，其中总线系统405除包括数据总线之外，还可以包括电源总线、控制总线和状态信号总线等。但是为了清楚说明起见，在图中将各种总线都标为总线系统405。
上述本申请实施例揭示的方法可以应用于处理器403中，或者由处理器403实现。处理器403可能是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过处理器403中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器403可以是通用处理器、数字信号处理器(英文全称：Digital Signal Processing，英文缩写：DSP)、专用集成电路(英文全称：Application Specific Integrated Circuit，英文缩写：ASIC)、现成可编程门阵列(英文全称：Field-Programmable Gate Array，英文缩写：FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。可以实现或者执行本申请实施例中的公开的各方法、步骤及逻辑框图。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。结合本申请实施例所公开的方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件模块组合执行完成。软件模块可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器404，处理器403读取存储器404中的信息，结合其硬件完成上述方法的步骤。
图4的相关描述可以参阅图1方法部分的相关描述和效果进行理解，此处不做过多赘述。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统，装置和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的几个实施例中，应该理解到，所揭露的系统，装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (11)

  1. 一种噪声检测方法，其特征在于，包括：
    根据第一公式计算每帧音频信号的幅度谱；
    根据各候选噪声频率确定噪声频率，所述各候选噪声频率由对每帧音频信号进行倒谱分析得到；
    根据所述噪声频率，所述幅度谱，和预设计算方法进行计算得到每帧音频信号的噪声能量值。
  2. 根据权利要求1所述的噪声检测方法，其特征在于，在所述根据各候选噪声频率确定噪声频率之前，所述方法还包括：
    在预置的频率搜索区间内，将每帧音频信号中幅度值超过预设阈值的各频率确定为各目标频率；
    将每个目标频率作为基频，计算基频，所述基频的二次谐波频率，和所述基频的三次谐波频率分别对应的幅度值的几何平均值；
    将最大几何平均值对应的各目标频率确定为各候选噪声频率。
  3. 根据权利要求1或2所述的噪声检测方法，其特征在于，在所述根据第一公式计算每帧音频信号的幅度值谱之前，所述方法还包括：
    将采样信号进行分帧和加窗处理，得到至少两帧音频信号。
  4. 根据权利要求3中所述的噪声检测方法，其特征在于，在所述将采样信号信号进行分帧和加窗处理，得到至少两帧以上音频信号之前，所述方法还包括：
    将输入音频信号进行N倍的插零上采样，得到所述采样信号，其中，所述N为不小于2的正整数。
  5. 根据权利要求1至4中任一项所述的噪声检测方法，其特征在于，所述预设计算方法包括响度计算方法。
  6. 一种终端设备，其特征在于，包括：
    第一计算模块，用于根据第一公式计算每帧音频信号的幅度谱；
    第一确定模块，用于根据各候选噪声频率确定噪声频率，所述各候选噪声频率由对每帧音频信号进行倒谱分析得到；
    第二计算模块，用于根据所述噪声频率，所述幅度谱，和预设计算方法进行计算得到每帧音频信号的噪声能量值。
  7. 根据权利要求6所述的终端设备，其特征在于，所述终端设备还包括：
    第二确定模块，用于在预置的频率搜索区间内，将每帧音频信号中幅度值超过预设阈值的各频率确定为各目标频率；
    第三计算模块，用于将每个目标频率作为基频，计算基频，所述基频的二次谐波频率，和所述基频的三次谐波频率分别对应的幅度值的几何平均值；
    第三确定模块，用于将最大几何平均值对应的各目标频率确定为各候选噪声频率。
  8. 根据权利要求6或7所述的终端设备，其特征在于，所述终端设备还包括：
    第一处理模块，用于将采样信号进行分帧和加窗处理，得到至少两帧音频信号。
  9. 根据权利要求8所述的终端设备，其特征在于，所述终端设备还包括：
    第二处理模块，用于将输入音频信号进行N倍的插零上采样，得到所述采样信号，其中，所述N为不小于2的正整数。
  10. 根据权利要求6至9中任一项所述的终端设备，其特征在于，所述预设计算方法包括响度计算方法。
  11. 一种终端设备，其特征在于，包括：
    接收器、发射器、存储器、总线和处理器；
    所述总线，用于连接所述接收器、所述发射器、所述存储器和所述处理器；
    所述存储器，用于存储操作指令；
    所述处理器，用于通过调用所述操作指令，执行上述权利要求1至5中的操作。
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