WO2012099560A1

WO2012099560A1 - Method and apparatus for wireless microphone detection and verification

Info

Publication number: WO2012099560A1
Application number: PCT/US2011/000090
Authority: WO
Inventors: Benyuan Zhang
Original assignee: Thomson Licensing
Priority date: 2011-01-18
Filing date: 2011-01-18
Publication date: 2012-07-26

Abstract

A method and apparatus for spectrum sensing of general wireless microphone signals are provided. The spectrum sensing algorithm developed makes use of the property that the spectrum of a wireless microphone signal resembles a tone-like spike, and that most wireless microphone operators use a pilot tone. Based on these properties, a simple spectrum sensing algorithm for the wireless microphone signal is designed based on frequency domain data processing without the large memory requirements of time domain methods and without the need for recursive memory accesses. The tones detected are further verified to see if they are located at one of the possible wireless microphone frequencies. The proposed method can determine the presence of wireless microphone signals even with strong adjacent channel interference.

Description

METHOD AND APPARATUS FOR WIRELESS MICROPHONE DETECTION

AND VERIFICATION

FIELD OF THE INVENTION

The present principles relate to the detection and verification of wireless microphone signals and systems.

BACKGROUND OF THE INVENTION Cognitive Radio was proposed to implement negotiated, or opportunistic, spectrum sharing to improve spectrum efficiency. Recently, the Federal Communications Commission (FCC) has approved operation of unlicensed radio transmitters in the local broadcast television spectrum at frequencies which are unused by licensed services (this unused TV spectrum is often termed "white space") under certain rules. A major regulation is that the white space devices will be required to sense, at levels as low as -114 dBm, TV signals (digital and analog), wireless microphone (WM) signals, and signals of other services that operate in the TV bands on an intermittent basis. Hence spectrum sensing is an important enabling technique for the deployment of cognitive radios in TV white space. Note that the noise power in a 6 MHz TV channel under normal temperature is about -96 dBm assuming that the noise figure of a sensing device is 10 dB. Thus, the sensing requirement set by the FCC is about -18 dB in terms of signal-to-noise power ratio (SNR) resulting in a rather difficult task. A uniform framework of spectrum sensing of ATSC/NTSC signals has been proposed in a companion application (PCT/US10/000961) for white space devices. .

In the United States, wireless microphones are low-power secondary licensed signals in TV bands and are regulated by FCC Radio Broadcast Rules in Title 47 Code of Federal Regulations (CFR), Part 74 ( 47 CFR 74 ). There are four main regulations for wireless microphone usage: (l) The wireless microphones are allowed to operate in unused VHF or UHF TV bands listed in 47 CFR 74. (2 ) The frequency selection shall be offset from the upper or lower band limits by 25 kHz or an integral multiple thereof. (3) One or more adjacent 25 kHz segments within the assignable frequencies may be

l combined to form a channel whose maximum bandwidth shall not exceed 200 kHz. (4) The maximum transmitter power is 50 mW in VHF bands and 250 mW in UHF bands. In other countries, wireless microphone operations are regulated by different agencies, but with technical characteristics generally similar to those in the United States. The majority of the wireless microphone devices use analog Frequency Modulation (FM) although digital modulation, for example, QAM is sometimes used, or hybrid

analog/digital modulations.

Detection of wireless microphone signals presents several known problems.

First, strong adjacent channel interference will impact the system performance and make the wireless microphone signals embedded in interference. Second, the signal to noise ratio of wireless microphone signals usually is very low so extremely sensitive detectors are required. Third, there are lots of unwanted tone-like spike signals in the wireless microphone working band which will generate a significant number of false detections for the detection equipment.

Blind spectrum sensing methods, e.g., Eigenvalue-Based algorithms, can be applied to sense a wireless microphone signal regardless of its modulation type. Another method is to look for a spectrum peak in the frequency domain. The bandwidth of wireless microphone signals is less than 200 kHz, much smaller than that of a TV band ( 6 MHz). As a result, the power of wireless microphone signals is very concentrated while the noise power is uniformly distributed over the whole 6 MHz band. Thus, a spectrum peak usually will appear in a spectrum of a wireless microphone signal. However, both of the previously mentioned methods produce high false alarm rates when a strong adjacent channel interference is present. The problem of sensing wireless microphone signals with the presence of adjacent channel interference is very difficult. Valid wireless microphone carrier spectrum locations can be located at 237 points within a 6 MHz TV band. The first nominal and last nominal channels are 50 kHz frome either of the 6 MHz spectrum edges, with the other nominal channels spaced 25 kHz apart. Since the center frequency of a wireless microphone signal may be only 50 kHz from the adjacent channel edge in the FCC's Adjacent Channel Interference test model, signals around this frequency band are severely affected by the interference leaked from TV signals in the lower adjacent channels. Thus, the wireless microphone signal may be fully shaded by the adjacent channel interference.

To address the problem of detection of FM wireless microphone signals under strong interference, a simple spectrum sensing method that utilizes an important property of an FM signal, i.e., its autocorrelation function, can be approximated as a sinusoidal function provided that the frequency deviation is much smaller than its carrier frequency and the correlation delay is small (US10/001467 and US 61/217523).

Computer simulations demonstrate that this proposed spectrum sensor can reliably detect the target signals when a strong adjacent channel interference exists and the signal power is as low as - 114 dBm, as set by the Federal Communications

Commission (FCC) in their reports on so-called white space device.

SUMMARY OF THE INVENTION

These and other drawbacks and disadvantages of the prior art are addressed by the present principles, which are directed to a method and apparatus for spectrum sensing of general wireless microphone signals.

In this invention, a method and apparatus for detection and verification of wireless microphone signals is described which is based on frequency domain data processing without the large memory requirements of time domain methods and without the need for recursive memory accesses. The proposed method can determine the presence of wireless microphone signals even with strong adjacent channel interference.

According to one aspect of the present principles, there is provided an apparatus for spectrum sensing of wireless microphone signals. The apparatus includes a memory for storing digital signals, a switch for enabling selected portions of memory to be routed to a Fast Fourier Transform circuit, a Fast Fourier Transform circuit for generating an N-point Fast Fourier Transform on stored digital signals enabled by the switch, an accumulator circuit for accumulating M results of the N-point Fast Fourier Transforms, a processor for finding an average of the M accumulations of the N-point Fast Fourier Transforms and for performing R differentiate and absolute value operations on the average of M accumulations, and a peak and verify circuit that identifies peaks in the differentiate and absolute value operation output and verifies whether identified peaks are wireless microphone signals. According to another aspect of the present principles, there is provided a method for spectrum sensing of wireless microphone signals. The method includes the steps of storing a digital signal, enabling selected portions of the digital signal to be transformed by a Fast Fourier Transform, performing an N-point Fast Fourier Transform on the selected portions of the digital signal, accumulating M results of the N-point Fast Fourier Transforms, averaging the accumulated M Fast Fourier Transform results, performing R differentiate and absolute value operations on the averages of M Fast Fourier Transform results, identifying peaks in the output of the differentiate and absolute value operation output, and verifying whether the identified peaks are valid wireless mircophone signals.

These and other aspects, features and advantages of the present principles will become apparent from the following detailed description of exemplary embodiments, which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Figure 1 illustrates a system block diagram of one embodiment of the spectrum sensing apparatus for wireless microphone signals using the principles of the present invention.

Figure 2 shows a typical wireless microphone frequency spectrum.

Figure 3 shows a typical tone-like noise signal.

Figure 4 shows a flowchart of the system operation using the principles of the present invention.

Figure 5 shows one embodiment of an apparatus using the present principles.

DETAILED DESCRIPTION

The principles described herein are a method and apparatus for detection and verification of wireless microphone signals for a TV white space device. TV white space is the part of the local unused TV broadcast spectrum where channels are not being used for TV or other authorized services. A TV white space device must reliably determine the availability of unused frequencies in the local areas. Some basic approaches that white space devices could employ to identify unused frequencies would be a "listen before talk" or a "detect and avoid" strategy. Spectrum sensing techniques are used to listen for the signals of TV stations, wireless microphone signals and other incumbent services. The principles herein describe a method and apparatus for white space signal detection using frequency sensing techniques for authorized wireless microphone signals.

Generally, an easy way to identify the frequency components in a signal is to use the amplitude of the spectrum at various points. In order to overcome low Signal-to-Noise (SNR) problem of some signals, it is common to average the signal for a long duration to achieve the necessary signal strength for measurement. When a known strong adjacent channel interference exists, averaging the signal spectrum for long periods of time may not be enough to detect the wireless microphone signal.

Another way to sense the wireless microphone spectrum signal is to perform a time domain correlation followed by a cosine based matched filter. In this type of method, a large buffer is needed to store sample data and the time domain correlation operation needs to recursively access memory, so the processing time can be prohibitive.

The wireless microphone detection methods described herein are able to detect wireless microphones, including those authorized under Part 74 of the Federal Communications Commission (FCC) rules for wireless microphones. Because an authorized wireless microphone can use digital modulation or analog FM modulation, its spectrum appears as a tone. According to the FCC wireless microphone rules, valid wireless microphone signals are possible at 237 frequencies within a 6 MHz TV spectrum. The first nominal and last nominal channels are 50 kHz from each edge of a 6 MHz TV spectrum. The other nominal channels are 25 kHz apart. An authorized wireless microphone could use any of these nominal frequencies as a valid carrier frequency within a 6 MHz TV spectrum. In addition, a wireless microphone may also use a pilot tone in order to combat the interference encountered with digital or analog modulation. This pilot tone signal is adopted by almost all wireless microphone manufacturers. The pilot tone is either a 32 kHz or 19 kHz tone signal. This property is exploited by the principles described herein. In a real world environment, many tone-like signals exist which appear as spikes in the frequency domain, but which are actually noise. These noise spikes are sometimes as strong as a real wireless microphone signal. In these situations, it becomes difficult to distinguish the microphone carrier from the tone-like spike arising from the noise. If a white space detector cannot distinguish between the tone-like noise and a real wireless microphone carrier frequency, the device will trigger a false detection. The white space spectrum in this situation would show the wireless microphone carrier and the tone-like spike. If a low resolution FFT was performed, some tone-like spikes will occupy the location at the nominal carrier frequency locations. A high resolution FFT can be used to identify tone-like spectral locations in the 6 MHz TV channel. However, high resolution FFTs can be expensive to implement. For example, a 20,000 point FFT is required for a sample frequency of 20 MHz with 1 kHz resolution.

Another concern is that the system clock could be a free running clock with jitter or an instability that reduces its accuracy. In this case, it would be very difficult to identify the nominal carrier frequency.

A general RF receiver system consists of frequency selection low noise amplifier or tuner, Auto Gain Control (AGC) circuitry, Auto Frequency Control (AFC), an Analog-to-Digital Converter (ADC), a bandpass filter, a lowpass filter, and data rate converter. The more functionality that the system contains, the greater will be the system costs and the longer it will take for the system to become stable. A frequency sensing device needs to occasionally check the current channel availability to avoid channel contention. Because the white space channel is a licensed band, unlicensed usage has to be yielded to the licensed user. But periodic operation of a detector may create an unstable system and degrade the entire system performance. Under the principles described herein, a tuner, gain control, ADC, and bandpass filters are used.

A received RF signal enters the tuner and generates an Intermediate Frequency (IF) based signal. The specific IF frequency depends on the tuner. For a shielded discrete tuner (CAN tuner), normally a 44 MHz frequency is used. For some silicon tuners, variable IF frequencies (such as 4.5 MHz, 6 MHz, 6.38 MHz...) are used which can be programmed through a digital interface. A bandpass filter is used to select the intended 6 MHz band. Gain control ensures that the input signals to the ADC are within the ADC working range. In one embodiment of this invention, the gain control is an open-loop function. Based on the received signal strength such as I² + Q² (where I and Q are the sampled real and imaginary signals, respectively), it is very easy to set a proper gain as long as the system's signal is not saturated.

In the detection system described herein, there are two stages for authorized wireless microphone detection. First, tone signals are detected in the spectrum and second, these tone signals must be verified as valid wireless microphone signals. The data output from the ADC is fed into the N-point complex FFT. In one exemplary embodiment, N equals 2048. Because the FFT needs to process all of the data simultaneously, an N point data block has to be available at the same time, and so the digital samples should be continuous. Ping pong buffers A and B are used to avoid data from being lost during FFT processing.

To overcome the noise and other interference from the environment that enters into the receiver system, the magnitude of the FFT result is accumulated for M times. In one exemplary embodiment, M equals 512. Based on the amplitude results of the M accumulation of the N point FFT, R differential operations are performed. In one exemplary embodiment, R equals 5. The differential operation is as follows: the average amplitude of M accumulations of an N-point FFT is first differentiated and then, the absolute value of the differentiation result is taken. The second differential operation is performed next. This procedure is repeated until the operation is performed R times. The absolute value from the above R differential operations is needed to extract the tone from the N-point spectrum. The typical wireless microphone spectrum and the tone-like noise signal spectrum are shown in Figures 2 and 3, respectively. Multiple peaks may exist in the result. Normally, the goal is to find the location of the maximum value. Then, the neighboring locations are identified and checked to see if they are localized secondary peaks similar to those in Figure 2. If these peaks exist, it is very easy to identify the distance to the main peak and then predict the frequency offsets approximately according to the sampling frequency and the number point of the FFT. Other peak seeking methods for the main and the localized secondary peaks can be used.

If the spectrum is very noisy, a low pass filter could be applied to reduce the rate of false detection. If multiple peaks exist, the highest peak is found first. After verifying whether it is a valid carrier frequency for wireless microphone signals, the highest peak is masked and its neighboring points are noise signals. We move on to the second highest peak and repeat this procedure until all peaks have been found. Multiple criteria, such as absolute value, peak-to-average ratio or peak-to-second peak ratio) could be used to qualify the peak for the next stage, which is verification. The threshold for verification, T, could be determined based on experiments at which point the wireless microphone or solid tone-like noise signals are easily identified.

In the next stage, the wireless microphone pilot tone property is used to distinguish the wireless microphone signal from pure noise tones. The typical wireless microphone and tone-like noise signal spectra are shown in Figure 2 and Figure 3 separately. Normally, the tone-like noise spike doesn't have nearby secondary tone signals. In some cases, the wireless microphone signal contains the pilot tone signals in order to overcome noise interference, which some vendors currently use to identify the wireless microphone signal itself. The tone signal in the spectrum is located on both sides of the microphone carrier frequency and apart from the main carrier frequency by about 32 kHz or 19 kHz. The secondary pilot tone signal could be as low as 10 dB below the main carrier tone signal, so a long accumulation of FFT magnitudes should be used. The two characteristics of the frequency domain for pilot tones should be noted. First, the distance from the carrier frequency to the pilot tone should be the same in spite of the frequency offset at the receiver. Second, a peak value of a pilot tone should be the same. This second condition becomes complicated when there is a strong interference existing in the neighboring channel, but the property of the pilot tone signal can still be used.

The wireless microphone verification procedure is as follows.' In stage one, we would identify a tone from an apparent wireless microphone signal. Using the tone signal peak as base, we will seek the nearby localized secondary peaks on both sides of the major peak. If localized secondary peaks are in the range of 32 kHz or 19 kHz based on the sample rate, we can check the peak value to determine if it is a wireless microphone signal or a pure noise tone. If there are multiple tone peaks in the search results, we will go through all the peaks to check for a valid wireless microphone signal. If there is no valid wireless microphone signal, we declare the spectrum clean to use. Otherwise, a wireless microphone signal exists. When the strong interference from an adjacent channel exists, the second property discussed above could be destroyed. In this case, we may use one side pilot tone around the carrier frequency for verification.

Figure 4 shows one embodiment of a method using the present principles, 400. At the start, data is input to one portion of buffer memory, 410, until that portion is full. When one portion of buffer memory 410 is full, input is then sent to another portion of the buffer memory while FFT processing is ongoing for the first portion. Buffer fullness is checked in step 420. Data is directed in step 430 from each portion of buffer memory to an FFT processing step 440, which performs an N-point FFT. Accumulation of FFT results over M iterations is performed in step 450. The accumulated N-point FFT results are then averaged, and differential and absolute value operations are performed for R iterations in step 460. The search for peaks in the differential operation output is performed in step 470. Qualification of the found peaks is performed in step 480 to determine whether they are due to valid wireless microphone signals. Step 480 identifies the wireless microphone tone signal, and thereby identifies secondary peaks that lie in the range of 32 kHz or 19 kHz of the tone frequency. If multiple tone signals are found, all peaks are checked to verify valid wireless microphone signals. If a valid wireless microphone signal exists, the white space device must search for other frequencies at which to operate. Otherwise, the spectrum is free for a white space device to use.

Figure 5 shows one embodiment of an apparatus using the present principles.

The apparatus 500 receives a digital input signal. The received digital input signal is a digitized version of a downconverted signal from an antenna, tuned to a TV white space frequency. The received digital signal is in signal communication with the input of Buffer A and Buffer B, 510, which may be two distinct buffers, two subsections of the same buffer, or another storage mechanism. The buffers each receive enough input from the input to perform a complete FFT, described shortly. The output of Buffers A and B is in signal communication with the input of switch 520. The switch acts, in conjunction with Buffers 510, to make Buffers 510 a ping-pong buffer so that no data is lost during FFT processing and so that there is a constant source of enough data to perform one complete N-point FFT. The output of the FFT circuit is in signal communication with the input of accumulator 540, which accumulates M versions of the N-point FFT results. The output of accumulator 540 is in signal communication with processor 550, which performs averaging and differential and absolute value operations on the M versions of the N-point FFT results. The processor output is in signal communication with the input to a peak search and verification circuit 560, which searches for peaks found during the aforementioned FFT, accumulation, averaging, differential and absolute value operations and verifies that a found peak is at a valid wireless microphone frequency. If circuit 560 determines that a peak has been found at a valid wireless microphone frequency, circuit 560 further checks secondary peaks to verify that the secondary peaks are due to a wireless microphone signal. If so, circuit 560 then sets its output to indicate that a valid wireless microphone signal is present at the searched frequency to a white space device. Otherwise, the output is set to indicate a clean spectrum is present at the searched frequency. White space devices, which are unlicensed, must yield spectrum space to valid licensed users.

The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functions may be provided by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. Moreover, explicit use of the term "processor" or "controller" should not be construed to refer exclusively to hardware capable of executing software, and may implicitly include, without limitation, digital signal processor ("DSP") hardware, read-only memory ("ROM") for storing software, random access memory ("RAM"), and non-volatile storage.

Other hardware, conventional and/or custom, may also be included. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the implementer as more specifically understood from the context.

A description will now be given of the many attendant advantages and features of the present principles, some of which have been mentioned above. For example, one advantage of the present principles is that it requires much less buffering of data, and no recursive memory accesses to perform spectrum sensing of wireless microphone signals. Therefore, one embodiment using the advantages of these principles is an apparatus for spectrum sensing of wireless microphone signals comprising a memory for storing digital signals, a switch for enabling selected portions of memory to be routed to a Fast Fourier Transform circuit, a Fast Fourier Transform circuit for generating an N-point Fast Fourier Transform on stored digital signals enabled by the switch, an accumulator circuit for accumulating results of M the N-point Fast Fourier Transforms, a processor for finding an average of the M accumulations of the N-point Fast Fourier Transforms and for performing R differentiate and absolute value operations on the average of M

accumulations, and a peak and verify circuit that identifies peaks in the differentiate and absolute value operation output and verifies whether identified peaks are wireless microphone signals. Another advantage is the previous apparatus, with the value of N equal to 2048 in the FFT circuit. A further advantage is the previous apparatus with the value of M equal to 512 for the number of accumulation of FFT results performed by the accumulator. A further advantage is the previous apparatus with the value of R equal to 5 for the number of differentiate and absolute value operations performed by the processor. Yet a further advantage is the previous apparatus wherein the peak and verify circuit identifies peaks by finding the maximum value in the differentiate and absolute value operation output, determines if there are localized secondary peaks in the differentiate and absolute value operation output, measures distances of the secondary peaks to the maximum value location, predicts frequency offsets based on the distances, masks the maximum value and secondary peaks, and rechecks spectrum for further peaks using the same procedure. Yet a further advantage is the previous apparatus wherein the peak and verify circuit identifies peaks using a peak seeking and localized secondary peak seeking method and verifies the peaks using at least one criteria comprising absolute value, peak-to-average ratio, or peak-to-second peak ratio.

Another advantage of the present principles is a method for spectrum sensing, comprising storing a digital signal, enabling selected portions of the digital signal to be transformed by a Fast Fourier Transform, performing an N-point Fast Fourier Transform on the selected portions of the digital signal, accumulating M results of the N-point Fast Fourier Transforms, averaging the accumulated M Fast Fourier Transform results, performing R differentiate and absolute value operations on the averages of M Fast Fourier Transform results, identifying peaks in the output of the differentiate and absolute value operation output, and verifying whether the identified peaks are valid wireless mircophone signals. Another advantage is an embodiment of the previous method with the value of N equal to 2048 in the FFT step. A further advantage is the previous method with the value of M equal to 512 for the number of accumulation of FFT results performed in the accumulating step. A further advantage is the previous apparatus with the value of R equal to 5 for the number of differentiate and absolute value operations performed in the performing step. Yet a further advantage is the previous method wherein the identifying step further comprises identifying peaks by finding the maximum value in the differentiate and absolute value operation output, determining if there are localized secondary peaks in the differentiate and absolute value operation output, measuring distances of the secondary peaks to the maximum value location, predicting frequency offsets based on the distances, masking the maximum value and secondary peaks, and rechecking spectrum for further peaks using the same procedure. Yet a further advantage is the previous method wherein the identifying step identifies peaks using a peak seeking and localized secondary peak seeking method and verifies the peaks using at least one criteria comprising absolute value, peak-to-average ratio, or peak-to-second peak ratio.

The present description illustrates the present principles. It will thus be appreciated that those skilled in the art will be able to devise various arrangements that, although not explicitly described or shown herein, embody the present principles and are included within its spirit and scope.

All examples and conditional language recited herein are intended for

pedagogical purposes to aid the reader in understanding the present principles and the concepts contributed by the inventor(s) to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions.

Moreover, all statements herein reciting principles, aspects, and embodiments of the present principles, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.

Thus, for example, it will be appreciated by those skilled in the art that the block diagrams presented herein represent conceptual views of illustrative circuitry embodying the present principles. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudocode, and the like represent various processes which may be substantially represented in computer readable media and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.

In the claims hereof, any element expressed as a means for performing a specified function is intended to encompass any way of performing that function including, for example, a) a combination of circuit elements that performs that function or b) software in any form, including, therefore, firmware, microcode or the like, combined with appropriate circuitry for executing that software to perform the function. The present principles as defined by such claims reside in the fact that the functionalities provided by the various recited means are combined and brought together in the manner which the claims call for. It is thus regarded that any means that can provide those functionalities are equivalent to those shown herein.

Reference in the specification to "one embodiment" or "an embodiment" of the present principles, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment of the present principles. Thus, the appearances of the phrase "in one embodiment" or "in an embodiment", as well any other variations, appearing in various places throughout the specification are not necessarily all referring to the same embodiment.

Claims

CLAIMS:

1. An apparatus for spectrum sensing, comprising:

a detector that detects a tone signal and determines whether there are side tones in a frequency spectrum around the tone signal;

circuitry that determines whether the detected tone signal is a valid wireless microphone signal based on location of said side tones.

2. The apparatus of Claim 1 , wherein:

said detector comprises:

a memory for storing digital signals;

a switch that routes selected portions of memory to a Fast Fourier Transform circuit;

a Fast Fourier Transform circuit that generates an N-point Fast Fourier

Transform on stored digital signals enabled by said switch;

an accumulator circuit that accumulates results of M said N-point Fast Fourier Transforms;

a processor for finding an average of said M accumulations of said N-point Fast Fourier Transforms and for performing R differentiate and absolute value operations on said average of M accumulations; and

said circuitry that determines whether the detected tone signal is a valid wireless microphone signal based on location of said side tones comprises a peak and verify circuit that identifies peaks in said differentiate and absolute value operation output and verifies whether identified peaks are wireless microphone signals.

3. The apparatus of Claim 2, wherein said location of side tones are either 19kHz or 32kHz away from said peak.

4. The apparatus of Claim 2, wherein the value of N is 2048 in said Fast Fourier Transform circuit and the value of M is 512 for the number of accumulations of Fast Fourier Transform results performed by said accumulator.

5. The apparatus of Claim 2, wherein the value of R is 5 for the number of differentiate and absolute value operations performed by said processor.

6. The apparatus of Claim 2, wherein the peak and verify circuit identifies peaks by finding the maximum value in the differentiate and absolute value operation output, determines if there are localized secondary peaks in the differentiate and absolute value operation output, measures distances of said secondary peaks to said maximum value location, predicts frequency offsets based on said distances, masks said maximum value and secondary peaks, and rechecks spectrum for further peaks using the same procedure.

7. The apparatus of Claim 2, wherein the peak and verify circuit identifies peaks using a peak seeking and localized secondary peak seeking method and verifies said peaks using at least one criteria comprising absolute value, peak-to-average ratio, or peak-to-second peak ratio.

8. A method for spectrum sensing, comprising:

detecting a tone signal and determines whether there are side tones in a frequency spectrum around the tone signal;

determining whether the detected tone signal is a valid wireless microphone signal based on location of said side tones.

9. A method for spectrum sensing, comprising:

said detecting step comprises:

storing digital signals;

routing selected portions of memory to a Fast Fourier Transform circuit;

generating an N-point Fast Fourier Transform on stored digital signals enabled by said routing step;

accumulating results of M said N-point Fast Fourier Transforms;

finding an average of said M accumulations of said N-point Fast Fourier Transforms and for performing R differentiate and absolute value operations on said average of M accumulations; and

said determining step comprises identifying peaks in said differentiate and absolute value operation output and verifies whether identified peaks are wireless microphone signals.

10. The method of Claim 7, wherein said location of side tones are either 19kHz or 32kHz away from said peak.

11. The method of Claim 7, wherein wherein the value of N is 2048 in said Fast

Fourier Transform step and the value of M is 512 for the number of accumulations of said Fast Fourier Transform results performed in said accumulating step.

12. The method of Claim 7, wherein the value of R is 5 for the number of differentiate and absolute value operations performed in said performing step.

13. The method of Claim 7, wherein said identifying step further comprises identifying peaks by finding the maximum value in the differentiate and absolute value operation output, determining if there are localized secondary peaks in the differentiate and absolute value operation output, measuring distances of said secondary peaks to said maximum value location, predicting frequency offsets based on said distances, masking said maximum value and secondary peaks, and rechecking spectrum for further peaks using the same procedure.

14. The method of Claim 7, wherein said identifying step identifies

peaks using a peak seeking and localized secondary peak seeking method and verifies said peaks using at least one criteria comprising absolute value, peak-to-average ratio, or peak-to-second peak ratio.