WO2021043408A1

WO2021043408A1 - Wind noise detection

Info

Publication number: WO2021043408A1
Application number: PCT/EP2019/073734
Authority: WO
Inventors: Anu Huttunen; Riitta Niemisto
Original assignee: Huawei Technologies Co., Ltd.
Priority date: 2019-09-05
Filing date: 2019-09-05
Publication date: 2021-03-11
Also published as: JP7422219B2; EP4005239A1; JP2022547493A; KR20220054646A; CN114287136A

Abstract

The aspects of the disclosed embodiments provide a new way to detect wind or handling noise in an apparatus such as a headset or mobile communication device. The presence of wind noise is determined by calculating the ratio between the microphone signal power and the beamformed signal power and comparing the ratio against a threshold value. One value can identify the presence of wind noise while another value can identify a lack of wind noise. The method of the disclosed embodiments is also computationally efficient and easy to combine with other processing in a device, such as a mobile communication device.

Description

WIND NOISE DETECTION

TECHNICAL FIELD

[0001] The aspects of the present disclosure relate generally to wind noise detection and more particularly to wind noise detection in a device such as a headset using multiple microphones.

BACKGROUND

[0002] Wind noise can corrupt microphone signals when a headset or any mobile device is used outside in windy conditions. Wind noise can also be induced when the user is moving fast, for example running, walking fast, or bicycling. Wind noise can have negative effects on phone calls, hear through, or augmented hearing features of these types of devices.

[0003] In a device with multiple microphones beamforming can be used to enhance the signal to noise ratio (SNR). However, wind noise becomes louder in a beamformed signal. There are certain phase and amplitude differences between the different microphone signals depending on the direction from which they arrive to the microphone array. During wind noise phase and amplitude difference of the microphone signals do not have the assumed relations. In a worst case scenario, the microphone signals might add up during wind noise, which can result in twice as much wind noise in the beamformed signal as compared to the microphone signal. Handling noise, such as when the user touches the microphone or microphone hole, can also add up in a manner similar to wind noise.

[0004] There are several ways to detect wind noise. For example, high power at low frequencies can indicate wind noise. Microphone signals can be subtracted from one another and a high difference can indicate wind noise. Wind noise can also be detected by calculating the ratio between the difference and sum of microphone signals and comparing the result against a threshold. However, these measures are very prone to error and easily lead to false detections.

[0005] Accordingly, it would be desirable to be able to detect wind noise in a manner that addresses at least some of the problems identified above.

SUMMARY

[0006] It is an object of the disclosed embodiments to provide wind detection in an apparatus such as a headset or a mobile communication device. This object is solved by the subject matter of the independent claims. Further advantageous modifications can be found in the dependent claims.

[0007] According to a first aspect the above and further objects and advantages are obtained by an apparatus that includes a processor. In one embodiment, the processor is configured to determine a power of at least one microphone signal of the apparatus, monitor a power of a beamformed signal of at least two microphones of the apparatus, compare the microphone signal power to the beamformed signal power and detect wind noise in microphone signals of the apparatus based on the comparison. The aspects of the disclosed embodiments reliably detect wind noise and reduce false detections. The method is computationally efficient and easy to combine with other processing in the apparatus, such as a mobile communication device or headset.

[0008] In a possible implementation form of the apparatus the processor is further configured to compare the microphone signal power to the beamformed signal power by calculating a ratio of the microphone signal power to the beamformed signal power and detect the wind noise based on the calculated ratio. The aspects of the disclosed embodiments reliably detect wind noise from the power ratio between the microphone signal power and the beamformed signal power. The process is computationally efficient.

[0009] In a possible implementation form of the apparatus the processor is further configured to detect the wind noise when the calculated ratio is less than a predetermined threshold value. The aspects of the disclosed embodiments reliably detect wind noise from the power ratio between the microphone signal power and the beamformed signal power. This ratio can be one for sounds coming from the target direction, more than one for ambient noise, and below one for wind noise.

[0010] In a possible implementation form of the apparatus the processor is further configured to switch off beamforming of the at least two microphones when wind noise is detected and select a microphone of the at least two microphones for which a least amount of wind noise is detected for further audio signal processing. Once the wind noise is detected, it can be reduced by switching off the beamforming and using only single microphone processing. The microphone that has the least amount of wind noise is chosen.

[0011] In a possible implementation form of the apparatus the processor is further configured to switch off beamforming of the at least two microphones when wind noise is detected and select a microphone of the at least two microphones for further audio signal processing. Once the wind noise is detected, the wind noise interference can be reduced by switching off the beamforming and using only single microphone processing. One microphone can be selected, such as a microphone that is closer to the mouth than another microphone.

[0012] In a possible implementation form of the apparatus the processor is further configured to compare a microphone signal power of at least one other one of the at least two microphones to the beamformed signal power and detect the wind noise based on the comparison. Monitoring more than one microphone of the apparatus enhances the detection. There can be more than two microphones and there is no upper limit for the number of microphones.

[0013] In a possible implementation form of the apparatus, the processor is further configured to calculate the ratio of the microphone signal power and the beamformed signal power over a frequency band. Wind detection is enhanced computing the microphone signal power-to- beam power ratio over a frequency band.

[0014] In a possible implementation form of the apparatus the processor is further configured to calculate the ratio of the microphone signal power to the beamformed signal power over multiple frequency bands and select a minimum value of the calculated power over multiple frequency bands for comparison to the pre-determined threshold value. Wind noise detection is enhanced by computing the signal-to-beam power ratio in frequency bands and then selecting the minimum over these bands.

[0015] In a possible implementation form of the apparatus, the processor is further configured to average the calculated power ratio over time. Wind noise detection can also be enhanced by averaging the power ratio, or the minimum value of the power ratio over the frequency bands, over time.

[0016] In a possible implementation form of the apparatus, the processor is further configured to determine a surrounding noise level N. If the surrounding noise level is below a predetermined threshold, the processor is configured to bypass wind noise detection.

[0017] In a possible implementation form of the apparatus, the apparatus includes at least two microphones.

[0018] In a possible implementation form of the apparatus, the apparatus is a mobile communication device.

[0019] In a possible implementation form of the apparatus, the apparatus is an audio signal processing device.

[0020] In a possible implementation form of the apparatus, the apparatus is a headset with at least two microphones.

[0021] In a possible implementation form of the apparatus, the apparatus is a smartwatch with at least two microphones.

[0022] In a possible implementation form of the apparatus, the apparatus is a wearable with at least two microphones.

[0023] According to a second aspect the above and further objects and advantages are obtained by a method for detecting wind noise in microphone signals of an apparatus. In one embodiment, the method includes determining a power of at least one microphone signal of the apparatus, determining a power of a beamformed signal of at least two microphones of the apparatus, compare the microphone signal power to the beamformed signal power, and detect wind noise in microphone signals of the apparatus based on the comparison. The aspects of the disclosed embodiments reliably detect wind noise and reduce false detections. The method is computationally efficient and easy to combine with other processing in the apparatus, such as a mobile communication device or headset.

[0024] In a possible implementation form of the method, the method further includes comparing the microphone signal power to the beamformed signal power by calculating a ratio of the microphone signal power to the beamformed signal power and detecting the wind noise in the microphone signals based on the calculated ratio. The aspects of the disclosed embodiments reliably detect wind noise from the power ratio between the microphone signal power and the beamformed signal power. The process is computationally efficient.

[0025] In a possible implementation form of the method, the method includes detecting the wind noise when the calculated ratio is less than a predetermined threshold value. Wind noise is detected when the signal-to-beam power ratio is below a lower threshold and the detection is released only when the signal-to-beam power ratio is above a higher threshold. The setting of the thresholds can be varied depending on the product, and such features such as microphone positions, beam design and other parameter values.

[0026] In a possible implementation form of the method, the method further includes switching off beamforming of the at least two microphones when wind noise is detected and selecting a microphone of the at least two microphones for which a least amount of wind noise is detected for further audio signal processing. Once the wind noise is detected, it can be reduced by switching off the beamforming and using only single microphone processing. The microphone that has the least wind noise can be chosen.

[0027] In a possible implementation form of the method, the method includes switching off beamforming of the at least two microphones when wind noise is detected and selecting a microphone of the at least two microphones for further audio signal processing. Once the wind noise is detected, it can be reduced by switching off the beamforming and using only single microphone processing. One microphone can be selected, such as a microphone that is closer to the mouth than another.

[0028] In a possible implementation form of the method, the method further includes comparing a microphone signal power of another one of the at least one microphone of the apparatus and the beamformed signal power and detecting the wind noise based on the comparison. Monitoring more than one microphone of the apparatus enhances the detection. There can be more than two microphones and there is no upper limit for the number of microphones.

[0029] In a possible implementation form of the method, the comparing further includes calculating the ratio of the microphone signal power and the beamformed signal power over a frequency band. Wind detection is enhanced computing the signal-to-beam power ratio over a frequency band.

[0030] In a possible implementation form of the method, the comparing further includes calculating the ratio of the microphone signal power to the beamformed signal power over multiple frequency bands, selecting a minimum value of the calculated power over the multiple frequency bands and comparing the selected minimum value to the pre-determined threshold value for determining the wind noise. Wind noise detection is enhanced by computing the signal-to-beam power ratio in frequency bands and then selecting the minimum over these bands.

[0031] In a possible implementation form of the method, the calculated power ratio is averaged over time. Wind noise detection can also be enhanced by averaging the power ratio, or the minimum value of the power ratio over the frequency bands, over time.

[0032] In a possible implementation form of the method, the method includes determining a surrounding noise level N. If the surrounding noise level is below a predetermined threshold, wind noise detection is bypassed.

[0033] In a possible implementation form of the method, the apparatus includes at least two microphones.

[0034] In a possible implementation form of the method, the apparatus is a mobile communication device.

[0035] In a possible implementation form of the method, the apparatus is an audio signal processing device.

[0036] In a possible implementation form of the method, the apparatus is a headset with at least two microphones.

[0037] In a possible implementation form of the apparatus, the apparatus is a smartwatch with at least two microphones.

[0038] In a possible implementation form of the apparatus, the apparatus is a wearable with at least two microphones. [0039] These and other aspects, implementation forms, and advantages of the exemplary embodiments will become apparent from the embodiments described herein considered in conjunction with the accompanying drawings. It is to be understood, however, that the description and drawings are designed solely for purposes of illustration and not as a definition of the limits of the disclosed invention, for which reference should be made to the appended claims. Additional aspects and advantages of the invention will be set forth in the description that follows, and in part will be obvious from the description, or may be learned by practice of the invention. Moreover, the aspects and advantages of the invention may be realized and obtained by means of the instrumentalities and combinations particularly pointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In the following detailed portion of the present disclosure, the invention will be explained in more detail with reference to the example embodiments shown in the drawings, in which:

[0041] Figures 1 illustrates a perspective schematic view of exemplary apparatus incorporating aspects of the disclosed embodiments.

[0042] Figure 2 illustrates an exemplary method incorporating aspects of the disclosed embodiments.

[0043] Figure 3 illustrates aspects of an exemplary method incorporating aspects of the disclosed embodiments.

[0044] Figure 4 illustrates aspects of an exemplary method incorporating aspects of the disclosed embodiments.

[0045] Figure 5 is a graph illustrating results of the wind detection method of the disclosed embodiments.

[0046] Figure 6 illustrates aspects of any exemplary method incorporating aspects of the disclosed embodiments.

[0047] Figure 7 is a schematic block diagram of an exemplary apparatus that can be used to practice aspects of the disclosed embodiments. DETAILED DESCRIPTION OF THE DISCLOSED EMBODIMENTS

[0048] Referring to Figure 1, a schematic block diagram of an exemplary apparatus 100 incorporating aspects of the disclosed embodiments is illustrated. The aspects of the disclosed embodiments are directed to detecting wind noise in one or more microphones of an apparatus or device such as a headset, mobile communication device, wearable such as a smartwatch with at least two microphones or an audio signal processing device. While the aspects of the disclosed embodiments will generally be described herein with respect to a headset or mobile communication device, the aspects of the disclosed embodiments are not so limited. Also, the aspects of the disclosed embodiments can be applied to detecting handling noise, such as when the microphone(s) of a device are being touched by the user. In alternate embodiments, the wind detection can be applied to any speech or audio signal processing device where wind or handling noise can affect the quality of the microphone signal.

[0049] As is illustrated in Figure 1, the exemplary apparatus 100 can include at least one processor 102 and at least two audio signal input devices 104a, 104b. In alternate embodiments, the apparatus 100 can include any suitable number of audio signal input devices 104a- 104b, other than including two. The aspects of the disclosed embodiments are not limited by the number of audio signal input devices 104a-104n exceeding two. For the purposes of the description herein, the audio signal input devices 104a-104n will be referred to as microphones.

[0050] Referring to Figure 1 , Pm denotes the power of a microphone signal Sm and Pb denotes the power of a beamformed signal of two or more of the microphones 104a-104n. It will be understood that in some cases the beamformed signal can be calculated from some but not all, of the microphones 104a-104n.

[0051] Responsive to received signals, such as audio or noise, the microphones 104a-104n are generally configured to generate respective microphone signals Smi-Smn. In one embodiment, the processor 102 can be configured to determine and/or calculate the power Pmi-Pmn of one or more of the respective microphone signals Smi-Smn. For the purposes of the description herein, the power Pm of a microphone signal Sm will be referred to as microphone signal power P_m, and the power Pb of the beamformed signal as beamformed signal power Pb.

[0052] Wind noise, such as when the apparatus 100 is used outside, can detrimentally affect one or more of the microphone signals Smi-Smn. This wind noise interference can inhibit or otherwise deteriorate the ability to communicate with the mobile communication device.

[0053] In one embodiment, a power P_m of a microphone signal Sm, such as power P_mi of microphone signal Smi of microphone 104a, is calculated. The power Pmi of the microphone signal Smi of microphone 104a is then compared to the beamformed power signal Pb calculated from at least two of the microphone signals. Wind noise in one or more of the microphone signals of the apparatus 100 can be detected based on the comparison.

[0054] In one embodiment, high values of the ratio Pm/Pb can indicate ambient or directive noise (no wind noise). When the ratio Pm/Pb has a value of one, this can indicate speech from the target direction (no wind noise). When the ratio Pm/Pb has a low value, this can indicate wind noise or even handling noise.

[0055] The aspects of the disclosed embodiments detect wind noise by calculating the ratio between the microphone signal power P_m to the beamformed signal power Pb and comparing the result against a threshold value. Where there is more than one microphone signal Smi-Smn, such as shown in the example of Figure 1, the determination and use of the corresponding microphone signal power Pmi-Pmn enhances the detection of wind noise. The beamformed signal power Pb keeps the audio signal such as speech in the target direction unchanged and attenuates the sounds coming from other directions.

[0056] In one embodiment, filter-and-sum beamforming can be utilized to generate the beamformed signal power Pb, where the two or more of microphone signals Sm i -Smn used to calculate the beamformed signal power Pb (generally referred to herein as the microphone signals Smi-Smn) are filtered and then added. When there is wind noise, the amplitude and phase difference between the microphone signals Sm i-Smn may not be what is anticipated. The phase and amplitude relations between the microphone signals Smi -Smn depend on the direction from where the sound is coming to the microphones 104a-104n, also referred to herein as a microphone array. When there is wind noise, the amplitude and phase relations can vary quickly and can be quite random. In some cases, the microphone signals Smi-Smn can add up, which can be detrimental to proper audio signal processing.

[0057] Referring to Figure 2, according to the aspects of the disclosed embodiments, the wind noise can be detected from the ratio of the microphone signal power-to-beam power (Pm/Pb). In one embodiment, the power P_m of the microphone signal Sm is determined 202. The power Pb of the beamformed signal is also determined. The ratio of Pm/Pb is calculated 206. From the ratio, it is determined 208 if there is wind noise. For example, when the result of the ratio Pm/Pb is one, this is indicative of sounds coming from the target direction without wind noise. When the ratio is greater than one, this is indicative of ambient noise without wind noise. When the ratio is less than one, this indicates wind noise.

[0058] If it is determined 208 that there is no wind noise 209, beamformer processing can be used with respect to the microphone signals Sm i -Smn. In some cases, the beamforming can be with respect to at least two, but not all, of the microphone signals Smi-Smn. If the determination 208 indicates the presence of wind noise 211, beamforming can be switched off 212. In one embodiment, the best microphone, or microphone that is subject to the least amount of wind noise can be selected. In an alternate embodiment, the microphone that is closest to the user’s mouth may be selected. The selected microphone can be used 214 for further audio signal processing.

[0059] In one embodiment, wind noise detection becomes more accurate if the beamformed signal power Pb is compared to two or more of the microphone signal powers, Pmi- P m . The wind noise detection becomes more accurate as more of the microphone signal powers Pmi-Pmn are used in the comparison.

[0060] For example, respective ones of the microphone signal powers Pmi-Pmn can be compared to the beamformed signal power Pb separately. The lowest determined power ratio can be compared to the threshold value. In an alternate embodiment, the average over all the power ratios can be taken or used as input, such as an input to a machine learning model, for example.

[0061] In alternate embodiments, the beamformed power signal Pb can be compared to both filtered and nonfiltered signals, or only the filtered signal. A filtered signal can be for example the beamformer filter, in filter-and-sum beamforming. Wind detection according to the aspects of the disclosed embodiments can also be enhanced if the microphone signal power P_m is averaged over a period of several seconds.

[0062] Referring to Figure 3, the result of the ratio Pm/Pb is compared 302 to a threshold value thr. In this example, if the value of Pm/Pb is greater 304 than thr, this indicates the lack 306 of wind noise, or the lack of interfering wind noise, in the microphone signal S_m. If the value of Pm/Pb is not greater 308 than thr, this indicates that there is 310 wind noise in the microphone signal Sm.

[0063] Referring to Figure 4, in one embodiment, the detection of wind noise is enhanced by computing the microphone signal power-to-beam power ratio (Pm/Pb) in frequency bands and then selecting the minimum value over these bands. In the example of Figure 4, P_m is the microphone signal power, Pb is the beamformer power, thr is the threshold value, f is the frequency band, and F is the set of frequency bands.

[0064] As illustrated in the example of Figure 4, the values of Pm(f), Pb (f) are calculated

402 for all f in the set of frequency bands F. The values of P_m(f)/Pb (f) are calculated 404 for all frequency bands f in the set of frequency bands F. The value of Rmin=rnin (P_m(f)/Pb (f)) is calculated 406 for all frequency bands f in the set of frequency bands F. It is determined 408 if the value of Rmin is greater than thr. When Rmin > thr, this is indicative of the lack 410 of any wind or of any interfering wind. When < thr, this is indicative of the presence 412 of wind, or the

presence of interfering wind.

[0065] Referring to Figure 5, in one embodiment, the detection of wind noise can also be made using two threshold values. In this example, wind noise is detected when the microphone signal power-to-beam power ratio Pm/Pb is below a lower threshold thr2 and the detection is released only when the signal-to-beam power ratio Pm/Pb is above a higher threshold thrl. The setting of the thresholds thrl and thr2 can depend on the features and aspects of the apparatus 100. Some of these features and aspects can include but are not limited to, the microphone positions, the beam design, and other parameter values. Generally, the thresholds thrl and thr2 can be designed to vary approximately between the range of 0 and -15 dB.

[0066] The example of Figure 5 illustrates the microphone signal power-to-beam power ratio calculated from measured data with two microphones 104a, 104b that are approximately 1.2 cm apart. The minimum over frequency bands Rmin is plotted as the solid curve 502. This is also averaged over time. The averaging is Xave(i +1) = Xframe + a * (Xave(i)-Xframe). Here Xave is the average value, Xframe the value calculated for one frame, and a is a constant that determines how slow is the averaging (e.g. a=0.99). The two thresholds thrl and thr2 are plotted with dashed lines 504, 506. Wind is present until approximately 31 seconds and after that there is no wind.

[0067] In one embodiment, the values calculated in the examples above, such as for example the microphone signal power-to-beam power ratio Pm/Pb or Rmin can be used as a feature in a machine learning algorithm, either alone or in combination with other features.

[0068] Beamforming usually boosts microphone noise in the low frequencies. In silent or non-interfering wind conditions the beam power Pb can be higher than the microphone signal power Pm. In this example, the surrounding noise level N is evaluated 610 and the wind noise detection is bypassed when the noise level N is below a certain threshold (thm).

[0069] Figure 6 illustrates a wind noise detection algorithm using noise level estimation.

As illustrated in the example of Figure 6, the values of P_m(f), Pb (f) are calculated 602 for all f in the set of frequency bands F. The values of P_m(f)/Pb (f) are calculated 604 for all frequency bands f in the set of frequency bands F. The value of Rmin=min (P_m(f)/Pb (f)) is calculated 606 for all frequency bands f in the set of frequency bands F. It is determined 608 if the value of Rmin is greater than thr. When Rmin is greater than thr, this is indicative of the lack 620 of any wind or of any interfering wind.

[0070] When not greater than thr, it is determined 612 if the noise level N is greater

than the threshold value thm. During wind noise, the surrounding noise level N will increase. Thus, whenever there is wind present, the wind noise will be detected. Any commonly used noise estimation 610 method can be used for this task.

[0071] If the value of the surrounding noise level N is not greater than the threshold value thm this is indicative of the lack 620 of any ambient noise or wind noise. If the value of the surrounding noise level N is greater than the threshold value thm this is indicative of the presence 622 of ambient noise or wind noise.

[0072] In one embodiment, once the wind noise is detected, the effect of the wind noise can be reduced by, for example, switching off the beamforming of the apparatus 100. It can be determined which one of the microphones 104a-104n is subject to the least amount of wind noise. In one embodiment, determining which of the microphones 104a-104n is subject to the least amount of noise comprises comparing the microphone signal power P_m levels at certain frequency bands, such as for example below 3500 Hz. The microphone 104a-104n with the lowest power level Pm during wind noise has the least amount of wind. The microphone that is subject to the least amount of wind noise can then be selected for further operation and audio signal processing. If it is determined that there is no wind noise, beamforming is used for the audio signal processing. In alternate embodiments, any suitable manner of wind noise reduction can be implemented once the wind noise is detected in accordance with the aspects of the disclosed embodiments.

[0073] Figure 7 illustrates a block diagram of an exemplary apparatus 1000 appropriate for implementing aspects of the disclosed embodiments. The apparatus 1000 is appropriate for use for example in a wireless communication network.

[0074] The apparatus 1000 includes or is coupled to a processor or computing hardware

1002, a memory 1004, a radio frequency (RF) unit 1006 and a user interface (UI) 1008. In certain embodiments the apparatus 1000 does not include a UI 1008. The apparatus 1000 can also include two or more sound processing devices 1014, also referred to as microphones. In one example, the two or more sound processing devices 1014 comprises an array of at least two microphones, such as those described with respect to Figure 1. Although shown coupled to the UI 1008, the microphones 1014 can be connected to or within the apparatus in any suitable manner. In one embodiment, the apparatus 1000 comprises the apparatus 100 referred to in Figure 1.

[0075] The processor 1002 may be a single processing device or may comprise a plurality of processing devices including special purpose devices, such as for example, digital signal processing (DSP) devices, microprocessors, graphics processing units (GPU), specialized processing devices, or general purpose computer processing unit (CPU). The processor 1002, which can be implemented as the processor 102 described with respect to Figure 1, may be configured to implement any one or more of the methods and processes described herein.

[0076] In the example of Figure 7, the processor 1002 is configured to be coupled to a memory 1004 which may be a combination of various types of volatile and non-volatile computer memory such as for example read only memory (ROM), random access memory (RAM), magnetic or optical disk, or other types of computer memory. The memory 1004 is configured to store computer program instructions that may be accessed and executed by the processor 1002 to cause the processor 1002 to perform a variety of desirable computer implemented processes or methods such as the methods as described herein. The memory 1004 may be implemented as part of or in combination with the apparatus 100 described with respect to Figure 1.

[0077] The program instructions stored in memory 1004 are organized as sets or groups of program instructions referred to in the industry with various terms such as programs, software components, software modules, units, etc. Each module may include a set of functionality designed to support a certain purpose to carry out one or more of the aspects of the disclosed embodiments described herein, either alone or in combination. Also included in the memory 1004 are program data and data files which may be stored and processed by the processor 1002 while executing a set of computer program instructions.

[0078] The apparatus 1000 can also include or be coupled to an RF Unit 1006 such as a transceiver, coupled to the processor 1002 that is configured to transmit and receive RF signals based on digital data 1012 exchanged with the processor 1002 and may be configured to transmit and receive radio signals with other nodes in a wireless network. To facilitate transmitting and receiving RF signals the RF unit 1006 includes an antenna unit 1010 which in certain embodiments may include a plurality of antenna elements. The multiple antennas 1010 may be configured to support transmitting and receiving MIMO signals as may be used for beamforming.

[0079] The UI 1008 may include one or more user interface elements such as a touch screen, keypad, buttons, voice command processor, as well as other elements adapted for exchanging information with a user. The UI 1008 may also include a display unit configured to display a variety of information appropriate for a computing device or mobile user equipment and may be implemented using any appropriate display type such as for example organic light emitting diodes (OLED), liquid crystal display (LCD), as well as less complex elements such as LEDs or indicator lamps.

[0080] The aspects of the disclosed embodiments provide a new way to detect wind noise.

The presence of wind noise is determined by calculating the ratio between the microphone signal power and the beamformed signal power and comparing the ratio against a threshold value. Any mobile device used outside suffers from wind noise and in the worst case wind noise can completely destroy the microphone signal, making it impossible to for example make a phone call. The aspects of the disclosed embodiments can reliably detect wind noise. Once the wind noise is detected, the wind noise interference can be reduced using any one of a number of known methods, such as switching off beam-forming and using single microphone processing. Also one might utilize an inner sensor of a headset during wind noise such as VACC (voice accelerometer), VPU (voice pickup sensor) or inner microphone). Lalse detections, which can also deteriorate the quality of the phone call or other audio features, are also avoided. The method of the disclosed embodiments is also computationally efficient and easy to combine with other processing in a device, such as a mobile communication device.

[0081] Thus, while there have been shown, described and pointed out, fundamental novel features of the invention as applied to the exemplary embodiments thereof, it will be understood that various omissions, substitutions and changes in the form and details of devices and methods illustrated, and in their operation, may be made by those skilled in the art without departing from the spirit and scope of the presently disclosed invention. Lurther, it is expressly intended that all combinations of those elements, which perform substantially the same function in substantially the same way to achieve the same results, are within the scope of the invention. Moreover, it should be recognized that structures and/or elements shown and/or described in connection with any disclosed form or embodiment of the invention may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. It is the intention, therefore, to be limited only as indicated by the scope of the claims appended hereto.

Claims

1. An apparatus (100) including a processor (102), wherein the processor (102) is configured to: determine a power (Pmi) of a microphone signal (Smi) of at least one microphone (104a) of the apparatus; determine a power of a beamformed signal (Pb) of at least two microphones (104a, 104n) of the apparatus; compare the microphone signal power (Pmi) to the beamformed signal power (Pb); and detect wind noise in microphone signals of the apparatus (100) based on the comparison.

2. The apparatus (100) according to claim 1 wherein the processor (102) is further configured to compare the microphone signal power (Pmi) to the power of the beamformed signal (Pb) by calculating a ratio of the microphone signal power (Pmi) to the beamformed signal power (Pb) and detect the wind noise based on the calculated ratio.

3. The apparatus (100) according to claim 2, wherein the processor (102) is further configured to detect the wind noise when the calculated ratio is less than a predetermined threshold value.

4. The apparatus (100) according to any one of the preceding claims, wherein the processor

(102) is further configured to: switch off beamforming of the at least two microphones (104a, 104b) when wind noise is detected; and select a microphone of the at least two microphones (104a, 104b) for which a least amount of wind noise is detected for further audio signal processing.

5. The apparatus (100) according to any of the preceding claims, wherein the processor (102) is further configured to compare a power (Pm2) of at least a second microphone signal (Sm2) of at least one other one (104b) of the at least two microphones (104a, 104b) to the beamformed signal power (Pb), and detect the wind noise based on the comparison of the microphone signal power (P mi) and the microphone signal power (Pm2) to the beamformed signal power (Pb).

6. The apparatus (100) according to any one of the preceding claims, wherein the processor (102) is further configured to calculate the ratio of the microphone signal power (Pmi) and the beamformed signal power (Pb) over a frequency band.

7. The apparatus (100) according to claim 6, wherein the processor (102) is further configured to calculate the ratio of the microphone signal power (Pmi) to the beamformed signal power (Pb) over multiple frequency bands and select a minimum value (Rmin) of the calculated power ratio over multiple frequency bands for comparison to the pre-determined threshold value.

8. The apparatus (100) according to any one of the preceding claims wherein the apparatus (100) is a mobile communication device.

9. A method (200) for detecting wind noise in microphone signals of an apparatus comprising: determining (202) a power (Pmi) of a microphone signal (Smi) of at least one microphone of the apparatus; determining (204) a power of a beamformed signal (Pb) of at least two microphones of the apparatus; comparing (206) the microphone signal power (Pmi) to the beamformed signal power (Pb); and detecting (208) wind noise in microphone signals of the apparatus based on the comparison.

10. The method (200) according to claim 9 further comprising comparing (206) the power (Pmi) of the microphone signal (Smi) to the beamformed signal power (Pb) by calculating (302) a ratio of the microphone signal power (Pmi) to the beamformed signal power (Pb) and detecting (208) the wind noise in the microphone signals based on the calculated ratio.

11. The method (200) according to any one of claim 10, further comprising: detecting (208) the wind noise when the calculated ratio is less (308) than a predetermined threshold value.

12. The method (200) according to any one of claims 9 to 11, further comprising: switching off (212) beamforming of the at least two microphones when wind noise is detected (208); and selecting (214) a microphone of the at least two microphones for which a least amount of wind noise is detected for further audio signal processing.

13. The method (200) according to any one of claims 9-12, further comprising comparing (206) a power (Pm2) of at least one other microphone signal (Sm2) of the at least one microphone of the apparatus and the beamformed signal power (Pb), and detecting (208) the wind noise based on the comparison of the microphone signal power (Pmi) and the microphone signal power (Pm2) to the beamformed signal power (Pb).

14. The method (200) according to any one of claims 9-13 wherein comparing (206) further comprises calculating (404) the ratio of the microphone signal power (Pmi) and the beamformed signal power (Pb) over a frequency band.

15. The method according to any one of claims 9-14 wherein comparing (206) further comprises calculating (402) the ratio of the microphone signal power (Pmi) to the beamformed signal power (Pb) over multiple frequency bands; selecting (406) a minimum value of the calculated power over the multiple frequency bands; and comparing (408) the selected minimum value to the pre-determined threshold value for determining (208) the wind noise.