Noise cancellation using segmented frequency-dependent phase cancellation

Abstract

To provide a method for constructing an accurate anti-noise signal required for a signal spectrum on a real time basis.SOLUTION: In an aerial silencer system, a processing circuit 10 acquires a noise signal 40, and generates a plurality of segmented noise signals 44 by subdividing the acquired noise signal into different frequency bands 42. For each segmented noise signal, time of the segmented noise signal is individually shifted by frequency dependent delay time 46 which depends on a selected frequency of the segmented noise signal, and a plurality of shifted segmented noise signals is generated. The segmented noise signals are synthesized, a composite anti-noise signal 50 is formed and is outputted to a signal stream and noise is reduced by destructive interference.SELECTED DRAWING: Figure 6

Description

[Technical Field]

[0001] Cross-reference to related applications This application claims the benefit of priority to U.S. Patent Application No. 15 / 497,417, filed April 26, 2017, and U.S. Provisional Patent Application No. 62 / 455,180, filed February 6, 2017, the disclosures of which are incorporated herein by reference in their entireties.

[0002] This disclosure relates generally to automatic electronic noise cancellation techniques, and more particularly to noise cancellation techniques that generate frequency-dependent anti-noise components in multiple spectral segments precisely calculated for a system and application. [Background technology]

[0003] Scientists and engineers have been working on the problem of electronic automatic noise cancellation (ANC) for decades. The basic physics of wave propagation suggests that it would be possible to create an "anti-noise" wave that is 180 degrees out of phase with the noise signal, eliminating it entirely through destructive interference. This works well for simple, repetitive, low-frequency sounds. However, it doesn't work well for dynamic, rapidly changing sounds, or sounds containing higher frequencies. Summary of the Invention [Problem to be solved by the invention]

[0004] The best current systems (which employ a hybrid design combining feedforward and feedback) can reduce repetitive noises (such as engine or fan noise) at frequencies up to 2 kHz, using modified LMS (least mean square) adaptive filtering to generate an anti-noise signal by iteratively estimating a transfer function that will produce the lowest practical noise at the output. While companies continue to invest in improving ANC results, their efforts appear to be focused on improving these existing techniques. Furthermore, despite the availability of significant processing power, ANCs using various adaptive filters have an upper frequency limit below 4 kHz, purportedly capable of attenuating signals by 10 dB to 30 dB. [Means for solving the problem]

[0005] In contrast to conventional approaches, the disclosed system can cancel virtually any frequency range in an offline mode and at least the entire audio spectrum in real time at commercially common processing speeds, more effectively than currently used methods.

[0006] Processing speeds and computing power continue to grow at a consistent and rapid pace (e.g., Moore's Law has been true since 1965). Some commercial and military markets are less cost-sensitive (than most consumer product applications) and can accommodate the higher costs of current maximum speed / power. Additionally, the extraordinary power of quantum computing is on the horizon. Accordingly, the inventors envision and encompass embodiments of the disclosed systems and methods that are expected to eventually become commercially viable over time. Thus, for simplicity in this disclosure, the inventors have limited the number of embodiments to five primary embodiments, defined by the minimum number of different hardware system architectures necessary to realize the myriad applications of the invention. The hardware system architectures are part of the invention in that they are integrated in specific ways into variations of what the inventors call the "core engine" signal processing methodology. The primary elements of these five embodiments are illustrated in Figures 1 through 5. Broadly speaking, the five embodiments can be described as airborne systems, telecommunications and personal use systems, offline signal processing systems, encryption / decryption systems, and signal signature recognition, detection, and reception systems.

[0007] The disclosed technology constructs, utilizes, and applies the precise anti-noise needed to a signal spectrum in real time. The system / algorithm is flexible, allowing for greater or less resolution and control depending on the application needs (or indeed, considering the cost constraints of processing power or other limiting factors imposed on product engineers utilizing this invention). The integration of this versatile and effective approach into specific hardware and software system architectures facilitates a variety of applications. Based on the system architectures envisioned thus far, the inventors have broadly categorized these into five areas: airborne systems, telecommunications and personal use systems, offline signal processing systems, encryption / decryption systems, and signal signature recognition, detection, and reception systems. This list of possible systems is not intended to limit the scope of this disclosure, but consideration is encouraged.

[0008] In addition to being useful for the audio spectrum, the disclosed techniques can be used for electromagnetic signals as well. Thus, the disclosed techniques can cancel virtually any frequency range in an offline mode using currently available processors, and at least the entire audio spectrum in real time at commercially available processing speeds. As processor speeds increase, or by aggregating the power of multiple processors, it is expected that any electromagnetic signal will be amenable to real-time processing using the present invention.

[0009] The algorithm processes individual frequency segments individually, dramatically improving noise cancellation performance across the entire audio spectrum by calculating the ideal anti-noise for that system or application. In fact, the algorithm can successfully cancel the entire audio spectrum in offline and signal processing applications. It is also more effective across the entire audio spectrum for headphones and over-the-air systems, and can process higher frequencies than any other system available. Processing individual frequency segments (and allowing ranges or bands of frequencies to be grouped, as discussed below) allows the algorithm to be customized to perform well in specific applications within or far beyond the audio spectrum.

[0010] Processing individual frequency segments makes it possible to create anti-noise for dynamic noise sources that change rapidly over time. (Current commercial methods are limited to periodic, steady-state sounds such as engine noise.) Processing individual frequency segments also reduces the need for multiple input microphones for headphones / earphones.

[0011] In audio applications, this algorithm reduces the number of microphones required for microphone noise cancellation and also reduces the need for complex "beamforming" algorithms to distinguish desired speech from ambient noise. This is particularly true for telecommunications headsets, because anti-noise designed for earpieces needs to effectively cancel unwanted signals when added to the microphone input signal with a small delay adjustment (perhaps using passive components to provide the necessary delay). Feedback processing can be adjusted and saved in presets as needed.

[0012] The calibration mode reduces the need for expensive tuning of the system for different physical systems, both during product development and in mass production.

[0013] The use of frequency bands or ranges in versions of the algorithm provides a variety of advantages, including the following:

[0014] i. Reduce the amount of processing power and memory required.

[0015] ii. Quickly and easily facilitate maximizing system performance for specific applications.

[0016] iii. Allows creation and use of presets for various types of noise, environments, etc.

[0017] iv. Enables the use of algorithms to increase the intelligibility of certain signals in noisy environments, which can be used for listening aids (such as making it easier to recognize speech in a noisy restaurant), audio surveillance applications (parsing speech from ambient noise), recognizing device signatures in networks or noise fields, and encryption / decryption applications.

[0018] Further areas of applicability will become apparent from the description provided herein. This summary description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure. [Brief explanation of the drawings]

[0019] The drawings described herein are for purposes of illustrating selected embodiments only, not all possible embodiments, and are not intended to limit the scope of the present disclosure. [Figure 1] FIG. 1 is a block diagram of a first embodiment of a silencer device useful for reducing or canceling noise in an airborne system. [Figure 2] FIG. 2 is a block diagram of a second embodiment of a silencer device useful for reducing or canceling noise in a telecommunications microphone, telecommunications headset, or headphone / earphone system. [Figure 3] FIG. 3 is a block diagram of a third embodiment of a silencer device useful for reducing or canceling noise in a signal processing system. [Figure 4] FIG. 4 is a block diagram of a fourth embodiment of a silencer device useful for encrypting and decrypting confidential communications. [Figure 5] FIG. 5 is a block diagram of a fifth embodiment of a silencer device useful for removing noise from electromagnetic transmissions and isolating specific equipment signatures or communications from background noise on power lines (such as in power line communications and smart grid applications). [Figure 6] FIG. 6 is a block diagram illustrating how a digital processor circuit can be programmed to execute the core engine algorithms used in the silencer device. [Figure 7] FIG. 7 is a flow chart diagram further illustrating a method of programming the digital processor circuit to execute the core engine algorithms used in the silencer device. [Figure 8] FIG. 8 is a signal processing diagram illustrating the processing technique implemented by the core engine algorithm of FIG. [Figure 9] FIG. 9 is a detailed signal processing diagram showing the calibration mode used in conjunction with the core engine algorithm. [Figure 10] FIG. 10 is a core engine process diagram. [Figure 11] FIG. 11 is an exemplary low power single unit airborne silencer system configured as a desktop personal quiet zone system. [Figure 12] FIG. 12 is an exemplary low power single unit aerial silencer system configured as a window mounted unit. [Figure 13] FIG. 13 is an exemplary low power single unit airborne silencer system configured as a package mounted in an air plenum. [Figure 14] FIG. 14 is an exemplary high-power multi-unit aerial silencer system configured for highway noise abatement. [Figure 15] FIG. 15 is an exemplary high-power multi-unit aerial silencer system configured to reduce vehicle noise. [Figure 16] FIG. 16 is an exemplary high-power, multi-unit airborne silencer system configured to create a cone of silence to prevent private conversations from being overheard by others. [Figure 17] FIG. 17 is an exemplary smartphone integration embodiment. [Figure 18] FIG. 18 is an exemplary noise-canceling headphone embodiment. [Figure 19] FIG. 19 is another exemplary noise-canceling headphone embodiment. [Figure 20] FIG. 20 shows an example processor implementation. [Figure 21] FIG. 21 illustrates an exemplary encryption-decryption embodiment. [Figure 22]FIG. 22 illustrates an exemplary signature detection concept.

[0020] Corresponding reference characters indicate corresponding parts in the several views of the drawings. DETAILED DESCRIPTION OF THE INVENTION

[0021] Exemplary embodiments are described more fully hereinafter with reference to the accompanying drawings.

[0022] The disclosed silencer device can be utilized in a variety of different applications. For illustrative purposes, five exemplary embodiments will be described in detail herein. It will be appreciated that these examples provide an understanding of some of the different uses to which the silencer device can be employed. Other uses and applications are possible within the scope of the appended claims.

[0023] Referring to FIG. 1, a first exemplary embodiment of a silencer device is shown. This embodiment is designed for noise cancellation in airborne systems. It detects incoming environmental noise, generates a noise-canceling signal, and propagates it throughout the surrounding area. As shown, this embodiment includes a digital signal processor circuit 10 with associated memory 12. This memory 12 stores configuration data, referred to herein as application-specific presets. The digital signal processor circuit can be implemented using commercially available multimedia processor integrated circuits, such as the Broadcom BCM2837 Quad Core ARM Cortex A53 processor. Details of how the digital signal processor circuit is programmed are described below. In a preferred embodiment, the digital signal processor circuit can be implemented using a Raspberry Pi computer, such as the Raspberry Pi 3 Model B. This device includes the signal processor circuit 10, as well as a VideoCore IV GPU, onboard SDRAM, Wi-Fi and Bluetooth transceiver circuitry, 802.11n wireless LAN circuitry, and support for Bluetooth 4.1 communications. Also provided are 26 GPIO ports, four USB 2 ports, a 100Base-T Ethernet port, a DSI / CSI port, a 4-pole composite video / audio port, and an HDMI 1.4 port, which can be used to provide connectivity between inputs and outputs to signal processor circuit 10, as shown in the block diagrams of Figures 1 through 4.

[0024] The airborne noise cancellation system of Figure 1 includes one or more input microphones 14 positioned in physical locations that allow for detection of noise sources desired to be canceled. Each microphone 14 is coupled to a digital audio converter or DAC 16 that converts, such as by sampling, the analog signal waveform from the coupled microphone into digital data. While different sampling rates may be used that are appropriate for the task, the illustrated embodiment uses a sampling rate of 48 kHz. The sampling rate is selected taking into account the frequency range in which the majority of the noise energy resides, the distance between the input and feedback microphones, and other factors related to the particular application and goals.

[0025] Coupled between the DAC 16 and the digital signal processor circuit 10 is an optional gating circuit 18 that passes noise energy above a predetermined threshold and blocks energy below that threshold. The gating circuit 18 may be implemented using software running on the processor or using a stand-alone noise gate integrated circuit. The purpose of the gating circuit is to distinguish between ambient background noise levels that are not considered objectionable and higher noise levels associated with objectionable noise. For example, if an airborne noise cancellation system is installed to reduce intermittent road noise from a nearby highway, the gate threshold would be configured to open when traffic energy is detected and close when only leaf rustling is present in a nearby forest. In this way, the gate reduces the load on the digital signal processor 10 and helps prevent undesirable pumping.

[0026] The gate is user-configurable, allowing the user to set a noise threshold so that only sounds above that threshold are treated as noise. For example, in a quiet office, the ambient noise level may be around 50dB SPL. In such an environment, the user might set the noise threshold to only act on signals above 60dB SPL.

[0027] Coupled to the output of the digital signal processor circuit 10 is a digital-to-analog converter, or ADC 20. The ADC works in conjunction with a DAC 16, which converts the output of the digital signal processor circuit 10 into an analog signal. The analog signal represents a specially configured noise cancellation signal designed to cancel the noise detected by the input microphone 14. This noise cancellation signal is then launched or propagated into the air by a suitable amplifier 22 and speaker or transducer system 24, where it combines with and cancels noise sources heard from vantage points within the effective transmission area of ​​the speaker or transducer system 24. Essentially, the speaker system 24 is positioned between the noise source and the listener or receiving point, so that the listener / receiver receives the signal that reaches them or their device, except when the signal from the noise source is canceled by the noise cancellation signal from the speaker or transducer system 24.

[0028] If desired, the circuit may also include a white or pink noise source that is sent to amplifier 22, which essentially mixes a predetermined amount of white or pink noise with the analog signal from digital signal processing circuit 10 (via ADC 20). This noise source helps soften the effect of the noise cancellation signal by masking transients that would otherwise be heard when the noise cancellation signal is combined with the noise source signal downstream from the speaker.

[0029] A feedback microphone 26 is positioned before (downstream from) the speaker system 24. The feedback microphone is used to sample the signal stream after introducing anti-noise into the stream. This microphone provides a feedback signal to the digital signal processor circuit 10, which is used to adapt an algorithm that controls how the digital signal processor circuit generates an appropriate noise cancellation signal. Although not shown in FIG. 1 , the output of the feedback microphone can be processed with appropriate amplification and / or analog-to-digital converter circuitry to provide the feedback signal used in the noise cancellation algorithm. In some applications where only the amplitude of the noise relative to the noise cancellation signal is of interest, the output of the feedback microphone can be processed in the analog domain to obtain an amplitude voltage signal that can be averaged or otherwise processed as needed. In other applications where a more rigorous evaluation of the noise versus noise cancellation signal is desired, a phase comparison with the input microphone signal may be performed. In most implementations of such a system, both the phase and amplitude of individual segments of the feedback signal will be analyzed in comparison to the input microphone signal or the desired result for the application (the creation and processing of the individual segments is described later in this specification). The output of the feedback microphone may be sampled and converted to the digital domain using an A / D converter. The feedback microphone can be connected to either the microphone input or the line input connected to the video / audio port of the digital signal processor circuit.

[0030] For further examples of the noise abatement system of FIG. 1, see the section below entitled "Different Use Case Embodiments."

[0031] A second embodiment of the silencer device is shown in FIG. 2. As described below, this embodiment features two signal paths: a receive audio signal path that reduces noise in the user's earphones, headphones, or speakers 24a; and a transmit audio signal path in which sound captured by the microphone of the phone 34 is processed to reduce ambient noise also captured by the microphone of the phone 34. Thus, the receive audio signal path may be used to improve what the user hears in the earphones, headphones, or speakers by reducing or eliminating ambient sounds in the environment, thereby improving the listening experience of music or telephone conversations. The transmit audio signal path may be used to partially or completely cancel ambient sounds, such as wind noise, that may be received by the microphone of the user's phone 34. Of course, the same noise cancellation techniques can be used in other systems, not just telephones, such as live recording microphones, broadcast microphones, etc.

[0032] Referring to FIG. 2, an exemplary embodiment shown is suitable for use in a headset system and shares some components with the embodiment of FIG. 1. In this embodiment, the input microphone is implemented using one or more noise-sensing microphones 14a located on the outside of the headset or headphones / earphones. Analog / digital circuitry associated with or integrated with each noise-sensing microphone converts ambient noise into a digital signal that is sent to the digital signal processing circuit 10. A feedback microphone 26 is located within the headphones / earphones with audio coupling to the headphone speakers 24a, or the feedback microphone can be omitted entirely, as this is a more tightly controlled physical implementation. In systems including a feedback microphone, the feedback microphone data may include desired entertainment content (e.g., music, audio / video soundtrack, etc.) and / or audio signals, as well as noise and anti-noise, and may include components of a composite signal that may be compared to the noise and anti-noise signals, or a combination of the input signal and the desired signal.

[0033] Note that this system differs significantly from conventional systems because its ability to effectively cancel sounds with frequency components higher than 2000Hz reduces the need for the acoustic isolation methods used in conventional noise-canceling headphones, allowing for lighter and cheaper products to be manufactured, facilitating effective deployment in "earbud" form factors.

[0034] In this embodiment, the noise processing of the silencer device can be performed independently for each earpiece, or in the case of low cost headphones / earphones / headset products, processing could be performed together for both earpieces of a stereo system, or could be handled by an outboard processor on a smartphone or other device.

[0035] Another important difference in this embodiment is that a calibration mode is also used to calculate the appropriate amplitude adjustment required for each frequency band range to compensate for the effect that the physical characteristics of the headphone or earphone structure have on ambient / undesirable noise before it reaches the ear (calibration modes and frequency band ranges are described later in this specification).

[0036] Similarly, the anti-noise generated by the system of this embodiment is often combined with a desired music or audio signal via a mixer 30 (the desired music or audio signal being provided by a phone 30, music player, communication device, etc.), and the two are output together through a common speaker. In that use case, the feedback microphone would only function in calibration mode and may be omitted in production units in some circumstances. Alternatively, in multi-speaker applications of this embodiment (e.g., VR and gaming headsets), the feedback microphone could function continuously.

[0037] In a headset system that includes an audio microphone, the output of the digital signal processor circuit 10 can be sent not only to the audio microphone circuit but also to the headphone speaker circuit as described above. This is shown in FIG. 2, where a first output signal is sent from the digital signal processor circuit 10 to a first mixer 30, which supplies it to an audio playback system (amplifier 22 and headphone speaker 24a). A second output signal is sent from the digital signal processor circuit 10 to a second mixer 32, which supplies it to a telephone 34 or other audio processing circuit. The ambient noise signal is sampled at a location away from the communication / audio microphone so that it does not cancel the desired audio signal. This separate anti-noise signal may or may not have its frequency band amplitude adjusted during the calibration mode as described above, depending on the application.

[0038] For critical communications applications such as media broadcast or fighter aircraft communications, separate signal processing circuitry may be desired for microphone noise cancellation, allowing for accurate cancellation of known noise signatures, providing the ability to not cancel important information frequency bands, and facilitating other customization for these mission-critical applications via user configuration or presets.

[0039] For further examples of the noise abatement system of FIG. 2, see the section below entitled "Different Use Case Embodiments."

[0040] A third, more generalized embodiment is shown in FIG. 3. In this embodiment, the input signal can come from any source, and the output signal can be coupled to circuitry or equipment (not shown) that normally processes the input signal. Therefore, the embodiment of FIG. 3 is designed to be in-line with or interleaved within signal processing equipment or transmission. This embodiment typically does not use feedback. System parameters (set using settings labeled "preset" 12) allow for compensation for known noise characteristics. If these characteristics are unknown, the system can be tuned to cancel noise by processing segments of material that contain noise but no signal (e.g., a "pre-roll" portion of a video segment can be used to calibrate the system to the noise signature). Applications for this embodiment include removing noise from event or audio surveillance recordings, or in "live" situations with an appropriate amount of "broadcast delay," such as the "7-second" delay currently used to allow censorship of profanity during live broadcasts.

[0041] While the above example illustrates a standalone digital signal processor circuit 10, it will be apparent that a processor within a mobile device, such as a smartphone, can be used to execute the signal processing algorithms, or the core engine can be implemented as a "plug-in" to a software suite or integrated into another signal processing system. Accordingly, the description herein will use the term core engine to refer to the signal processing algorithms, described in more detail below, executed on a processor, such as a standalone digital signal processor circuit or a processor embedded in a smartphone or other device. As shown in the embodiment of FIG. 3, the core engine can be used to process noise in an offline mode or in signal processing applications where an input microphone, an output amplified speaker, or a feedback microphone may not be required.

[0042] For further examples of the noise abatement system of FIG. 3, see the section below entitled "Different Use Case Embodiments."

[0043] A fourth embodiment is shown in Figure 4. In this embodiment, the number of parties who wish to share information with each other via file, broadcast, signal transmission, or other means and wish to restrict access to that information is two. In this embodiment, both the "encoding party" and the "decoding party" require access to the equipment containing the present invention.

[0044] The secret to setting the frequency band ranges used to encode information is the encryption / decryption "key." The encryption and decryption "key" settings are created taking into account the characteristics of the noise or other signal into which the information is to be embedded, and these frequency band range settings include both frequency and amplitude information. This allows the encoded signal to be embedded, for example, in an innocuous wideband transmission of other material, or in an extremely narrow segment of a transmission that appears to be a "white noise" signal. In the example of embedding an intelligence transmission in what appears to be white noise, the encryption would require a white noise recording of an appropriate length to carry the entire message. This white noise recording would be processed by the present invention only for a very narrow frequency range or set of ranges (i.e., a very narrow slice of the noise would be "carved out" by the core engine); frequencies outside this defined set of ranges would be passed to the system unchanged (the default amplitude of frequencies not included in the encryption side's frequency band definition would be set to 1), and the amplitude of the frequency range that would contain the intelligence could be adjusted to make it easier to hide in the noise. The information to be shared could be encoded onto a "carrier signal" within a narrow frequency range using frequency modulation or other techniques (providing another layer of encryption), and this would be combined with the system output (noise with "carved" slices), effectively embedding the information in what appears to be a random white noise signal in this example (or another type of broadband or signal transmission, if desired).

[0045] The "decryption" side needs to know the frequency and amplitude settings of the frequency band range that indicates where the information was recorded to act as a "decryption key." The decryption side needs to set the default amplitude of frequencies that are not included in the frequency band range definition to 0. This means that the system will not generate any output for frequencies that are not included in the frequency band definition, so only the desired signal will be decoded.

[0046] The ability to select a default amplitude for frequencies not included in the defined frequency band range is one of the defining features of this embodiment.

[0047] The security of information transfer is greatly enhanced if an encryption / decryption "key" is shared between the parties by means of an exchange, but it is conceivable that this could also be included in a transmission or file "calibration header" calculated based on a timestamp or other settings, etc.

[0048] For further examples of the noise abatement system of FIG. 4, see the section below entitled "Different Use Case Embodiments."

[0049] A fifth embodiment is shown in Figure 5. In this embodiment, the present invention is used to assist in recognizing, detecting or receiving transmission or device signatures in noise fields, such as those inherent in power lines and the like, and present in dense electromagnetic fields in major cities and other areas.

[0050] The present invention can be used to aid in the detection, recognition, or reception of signals in such noise by creating preset frequency band range settings designed to pass only the target signal. These frequency band range settings contain both the frequency and amplitude information necessary to identify the target signal's "fingerprint" or "signature" relative to the characteristics of the background noise, as determined in a prior analysis. These settings may be achieved by excluding the target signal's frequency components from the frequency band settings, using a default amplitude of zero for frequencies not included in the band settings, and then appropriately adjusting the amplitude and frequency of adjacent frequencies or harmonics to further enhance the target signal. This may aid in the detection of weak signals that would otherwise go unnoticed in a noise field.

[0051] For example, when an air conditioning system compressor turns on, it sends a distinctive impulse to the grid. In a power company substation, the system can utilize the present invention to help predict peak loads by counting impulses from various products. In power line communications applications, the characteristics of "normal" noise and fluctuations can be minimized and the desired communications signal enhanced by utilizing presets designed for that task. Presets can also be designed to detect or enhance remote or weak electromagnetic communications. Similarly, presets can be designed to detect noise field disturbances identified with certain types of objects or other potential threats.

[0052] In this embodiment, multiple instances of the core engine may be utilized in a server (or other multi-core or multiplexed device) to facilitate recognition, detection or reception of various signal or signature types at a single node.

[0053] For further examples of the noise abatement system of FIG. 5, see the section below entitled "Different Use Case Embodiments."

[0054] Core Engine Noise Cancellation Algorithm Overview The essence of the core engine noise canceling algorithm is to create perfect anti-noise for many small discrete segments that contain the noise signal. Digital signal processing circuit 10 (either implemented as a standalone circuit or using the processor of another device such as a smartphone) is programmed to execute a signal processing algorithm that precisely generates a set of individually tuned noise cancellation signals for each set of discrete frequency segments that contain the target noise signal or parts of it.

[0055] FIG. 6 illustrates the basic architecture of the core engine noise cancellation algorithm. As shown, the acquired noise signal 40 is subdivided into different frequency band segments. In a currently preferred embodiment, the width of these segments (and, in some embodiments, the amplitude scaling factor applied to the anti-noise) in various frequency ranges can be set differently for each of the different frequency bands 42. These parameters for the frequency bands can be set via a user interface, presets, or dynamically based on criteria, in various embodiments. Each frequency band range is further subdivided into frequency band segments of a selected width. For each frequency band segment, the digital signal processing circuitry then shifts the phase of that segment by an amount determined by the selected frequency of the segmented noise signal. For example, the selected frequency may be the center frequency of the band segment. Thus, if a particular band segment is from 100 Hz to 200 Hz, the selected center frequency may be 150 Hz.

[0056] By segmenting the incoming noise signal into multiple different frequency segments, the digital signal processing circuitry can tailor the noise cancellation algorithm to the specific requirements of a given application. This is done by selectively controlling the size of each segment to suit the particular application. By way of example, each segment across the entire frequency range of the incoming noise signal can be very small (e.g., 1 Hz). Alternatively, different portions of the frequency range can be subdivided into larger or smaller segments, with smaller (higher resolution) segments used where the most important information content resides or where shorter wavelengths are required, and larger (lower resolution) segments used at frequencies carrying less information or with longer wavelengths. In some embodiments, the processor not only subdivides the entire frequency range into segments, but can also manipulate the amplitude within a given segment differently based on the frequency band range settings.

[0057] When extremely high noise cancellation accuracy is desired, the noise signal is divided into small segments (e.g., 1 Hz or other sized segments) across the entire spectrum or across the entire spectrum of the noise signal, as needed. Such fine-grained subdivision requires significant processing power. Therefore, for applications requiring low-power, low-cost processors, the core engine noise cancellation algorithm is configured to divide the signal into frequency bands or ranges. The number of frequency bands can be adjusted in the core engine software code to suit the requirements of each application. If necessary, the digital processor can be programmed to subdivide the acquired noise signal by applying wavelet decomposition to subdivide the acquired noise signal into different frequency band segments, thereby generating multiple segmented noise signals.

[0058] For a given application, the size of the segments and whether / how they vary across the spectrum are determined by defining the initial conditions of the system and the parameters for the various frequency ranges, which can be set via the user interface and stored in memory 12 as application-specific presets.

[0059] Once the noise signal is segmented (automatically and / or based on user configuration) according to a segmentation plan established by the digital signal processing circuitry, phase corrections are selectively applied to each segment to generate segment waveforms that substantially nullify the noise signal within that segment's frequency band through destructive interference. Specifically, the processing circuitry calculates and applies a frequency-dependent delay 46, taking into account the frequency of the segment and accounting for system propagation or delay times. Because this frequency-dependent delay is calculated and applied to each segment individually, the processing circuitry 10 calculates and applies these phase corrections in parallel or very fast serial fashion. The phase-corrected (phase-shifted) segmented noise signals are then combined at 48 to generate a composite anti-noise signal 50. This composite anti-noise signal 50 is output into the signal stream, reducing the noise through destructive interference. As shown in Figure 6, the anti-noise signal can be introduced into the signal stream via an amplified speaker system or other transducer 24. Alternatively, in certain applications, an appropriate digital or analog mixer can be used to introduce the anti-noise signal into the signal stream.

[0060] In some embodiments, noise reduction can be further enhanced by using a feedback signal. Thus, as shown in FIG. 6, a feedback microphone 26 may be placed in the signal stream downstream from where the anti-noise signal is introduced. In this manner, the feedback microphone detects the results of destructive interference between the noise signal and the anti-noise signal. A feedback signal derived from the feedback microphone is then provided to processing circuitry 10 for use in adjusting the amplitude and / or phase of the anti-noise signal. This feedback process is generally indicated at 52 in FIG. 6. Feedback process 52 involves converting the feedback microphone signal to an appropriate digital signal via analog-to-digital conversion, and then using the feedback microphone signal as a reference to adjust the amplitude and / or phase of the anti-noise signal to maximize noise reduction. When the anti-noise and noise signals destructively interfere in an optimal manner, the feedback microphone signal will detect a null due to the fact that the noise and anti-noise energies are optimally canceling each other out.

[0061] In one embodiment, the amplitude of the synthesized anti-noise signal 50 can be adjusted based on the feedback microphone signal. Alternatively, the amplitude and phase of each frequency band segment can be adjusted individually. This can be done by comparing the amplitude and phase of the signal stream at the feedback point to the input signal and adjusting the anti-noise parameters. Alternatively, the frequency content and amplitude of the feedback signal itself can be examined to indicate necessary adjustments to the anti-noise parameters that will improve results by fine-tuning the amplitude and frequency-dependent delay time 46 of each segment.

[0062] Determining frequency-dependent delay times Signal processing circuit 10 calculates the frequency-dependent time delay for each segment by considering several factors, one of which is the calculated 180 degree phase shift time associated with a predetermined frequency (e.g., the segment center frequency) for each individual signal segment.

[0063] This calculation is done in a calibration mode and may be stored in a table in memory 12 depending on the application and available processing power, or may be continuously recalculated in real time. The exact time delay required to produce the appropriate anti-noise for each frequency "f" is calculated by the formula (1 / f) / 2.

[0064]

number

[0065] Another factor used by the signal processing circuitry is the system offset time, which depends on two factors: air propagation time and system propagation time.

[0066] To generate an accurate noise-canceling signal, the processing circuitry relies on a priori knowledge of the speed of sound propagation through the air, measured as the transit time for a signal to travel from the input microphone to the feedback microphone. As used herein, this transit time is referred to as the air transit time. The processing circuitry also relies on a priori knowledge of the time it takes for processor 10 and associated input and output components (e.g., 14, 16, 18, 20, 22, 24) to generate the noise-canceling signal, referred to herein as the system transit time. These data are necessary to accurately phase-match the noise-canceling signal with the noise signal for perfect cancellation. The speed at which a noise signal propagates through air depends on various physical factors, such as temperature, pressure, density, and humidity. In various embodiments, the processor's computation time and circuit throughput time depend on the processor's speed, the speed of the bus accessing memory 12, and signal delays through input / output circuitry associated with processor 10.

[0067] In the preferred embodiment, these airborne and system propagation times are measured during a calibration mode and stored in memory 12. The calibration mode can be manually requested by a user via a user interface, or processor 10 can be programmed to perform calibrations automatically, either periodically or in response to measured temperature, pressure, density, and humidity conditions.

[0068] For this reason, the preferred embodiment measures the air propagation time from when a noise signal is detected by the input microphone 14 until it is later detected by the feedback microphone 26. Depending on the application, the two microphones may be permanently positioned at a fixed separation (as in the headset embodiment of FIG. 2), or may be positioned at a separation distance that depends on where the two microphones happen to be in the field. The time delay due to the time it takes for the input signal to be processed, output to the speaker system 24 (24a), and received by the feedback microphone 26 corresponds to the system propagation time.

[0069] Once the air propagation time and system propagation time have been measured and stored in the calibration mode, the signal processing circuit 10 calculates the system offset time as the arithmetic difference between the air propagation time and the system propagation time. This difference calculation may be calculated in real time or stored in memory 12. For fixed applications such as headphones, an on-board calibration mode may not be necessary because the calibration is done on the production line or established based on the known, fixed geometry of the headphones. This system offset time may be stored (or in some applications dynamically calculated) as a constant used in the noise reduction calculations described herein.

[0070] For each individual frequency segment being processed, an anti-noise signal is generated by delaying the processed signal by a time equal to the absolute value of the 180-degree phase shift time for that individual frequency segment minus the system offset time. This value is referred to herein as the applied time delay. The applied time delay for each frequency segment can be stored in a table or calculated continuously in various implementations of the algorithm.

[0071] Figure 7 shows in more detail how the signal processing circuitry can be programmed to implement the core engine noise cancelling algorithm. The programming process begins with a series of steps that populate a set of data structures 59 in memory 12, where parameters used by the algorithm are stored for access as needed. Details of the core engine process are discussed below in conjunction with Figure 10.

[0072] Referring to Figure 7, first a record containing the selected chunk size is stored in data structure 59. The chunk size is the length of the time slice processed in each iteration performed by the core engine, represented by the number of samples processed as a group or "chunk" of data. The chunk size is primarily based on the application (frequency range processed and desired resolution), system propagation time, and travel time between input and output of the noise signal transmitted over the air or otherwise (processing must be completed and anti-noise inserted into the signal stream before the original signal passes the anti-noise output point).

[0073] For example, for an airborne system processing the entire audio spectrum, a 5.0 inch distance between input and output microphones, a 48 kHz sampling rate, and a 0.2 ms system propagation time, a chunk size of 16 would be appropriate (at a 48 kHz sampling rate, 16 samples equates to approximately 0.3333 milliseconds of time, during which sound travels approximately 4.5 inches in air at standard temperature and pressure). By limiting system calls and state changes to one per chunk, processor operation can be optimized for efficient processing at the desired chunk size.

[0074] This chunk size record is typically stored at start-up when the noise cancellation device is configured for a given application. In most cases, it is not necessary or desirable to change the chunk size record during operation of the core engine noise cancellation algorithm.

[0075] The frequency band ranges, segment sizes within each frequency band range, and power scaling factors for each frequency band are set as initial conditions for the application and stored in data structure 59. These parameters may be set through a user interface, included as presets in the system, or calculated dynamically.

[0076] The processing circuitry at 62 then measures and stores in data structure 59 a system propagation time corresponding to the time consumed by the processing circuitry and its associated input and output circuits to perform the noise mitigation process. This is done by operating the processing circuitry in a calibration mode, described below, in which a noise signal is supplied to the processing circuitry and acted upon by the processing circuitry to generate an anti-noise signal and output. The elapsed time from the input of the noise signal to the output of the anti-noise signal represents the system propagation time. This value is stored in data structure 59.

[0077] The processing circuitry at 64 also measures the time-of-flight in the air and stores it in data structure 59. This operation is also performed by the processing circuitry in a calibration mode, described below, in which the processing circuitry is switched into a mode in which it does not generate output. The elapsed time from receiving the signal at the input microphone to receiving the signal at the feedback microphone is measured and stored as the time-of-flight in the air.

[0078] The processing circuitry at 66 then calculates the system offset time, defined as the air propagation time minus the system propagation time, and stores this in data structure 59. This value is needed later by the processing circuitry to calculate the applied delay time.

[0079] With the above-mentioned calibration parameters thus calculated and stored, the core engine noise cancelling algorithm can perform segment-specific pre-calculation (or, if sufficient processing power is available, can perform these calculations in real time).

[0080] As shown, step 68 and subsequent steps 70 and 72 are performed in parallel (or serially at high speed) for each segment according to the frequency band configuration. If there are 1000 segments in a given application, steps 68-70 are preferably performed 1000 times in parallel and the data is stored in data structure 59.

[0081] In step 70, the 180-degree phase shift time is adjusted by subtracting the previously stored system offset time for each segment. The processor circuit calculates and stores the absolute value of this value as the applied delay time. The applied delay time is therefore a positive number, representing the amount of phase shift applied to the corresponding segment.

[0082] The core engine uses this stored data to process the frequency segments more quickly (by pre-applying pre-calculated time shifts to all frequency segments). In step 72, the processor circuit performs a phase shift on the segment noise signal by shifting the segment noise signal in time by the amount stored as the delay time to be applied to that segment. Also, if an amplitude adjustment (or fine-tuning of the phase adjustment) is required according to the frequency range setting or feedback processing 52 (FIG. 6), that adjustment is also applied here (in some embodiments, storing the information as a vector allows for simultaneous application of phase shifts and amplitude adjustments). Depending on the system architecture, all segments are processed in parallel or in a fast serial manner.

[0083] Once all segments of a particular chunk have been properly adjusted, the processing circuitry at 74 recombines all the processed segments to generate an anti-noise waveform for output into the signal stream.

[0084] To further understand the process performed by the processing circuitry, reference is made to Figure 8, which shows a more physical representation of how the noise signal is processed. Beginning at step 80, a noise signal 82 is obtained. In Figure 8, the noise signal is plotted as a time-varying signal containing many different frequency components or harmonics.

[0085] In step 84, chunks of the noise signal spectrum are subdivided into segments 86 according to parameters 59 described in connection with Figure 7. For purposes of illustration, Figure 7 assumes that the time-varying noise signal 82 is represented in the frequency domain, with the lowest frequency components assigned to the left-most side of the spectral plot 86 and the highest frequency components or harmonics assigned to the right-most side of the spectral plot. For example, the spectral plot 86 may range from 20 Hz to 20,000 Hz, covering the entire range of commonly accepted human hearing. Of course, the spectrum can be allocated in different ways depending on the application.

[0086] While the noise signal is represented in the frequency domain of the spectrum 86, it should be recognized that the noise signal is inherently a time-varying signal. Thus, the amount of energy in each frequency-domain segment varies over time. To illustrate this variation, a waterfall plot 88 is also shown, showing how the energy in each frequency segment varies along the vertical axis over time.

[0087] For each segment individually, a frequency-dependent phase shift (i.e., applied delay time) is applied, as done in step 90. To illustrate this, waveform 92 represents the noise frequency in a segment before the shift. Waveform 94 represents the same noise frequency after the system offset time has been applied. Finally, waveform 96 represents the resulting noise frequency after the 180-degree phase shift time has been applied (this is for illustrative purposes only; in actual processing, only the applied delay time is applied, which is the absolute value of the 180-degree phase shift time minus the system offset time). This illustration assumes that no amplitude scaling is required for the segment being processed.

[0088] By combining the time-shifted components from each segment in step 98, an anti-noise signal 100 is constructed. When this anti-noise signal is output to the signal stream, as is done in step 102, it is mixed with the original noise signal 104, causing the two noise signals to destructively interfere, effectively canceling or mitigating the noise signal. What remains is an information-carrying signal 108, which can be obtained in step 106.

[0089] Calibration Mode FIG. 9 shows how the processing circuit 10, input microphone 14, amplified speaker 24 and feedback microphone 26 can be used for calibration by selectively enabling and disabling the core engine algorithms when measurements are taken.

[0090] In a currently preferred embodiment, the air propagation time is calculated with the core engine's anti-noise system and output disabled. In this state, the air propagation time is calculated as the time difference between the time when the input noise is picked up at the input of the input microphone 14 and the time when the noise is picked up at the feedback microphone 26. The system propagation time is measured with the core engine's anti-noise system enabled. The same input is again introduced to the input microphone 14. This time, it is processed by the core engine and output through a speaker (e.g., speaker system 24 or other suitable calibration speaker or transducer) placed in front of the feedback microphone 26. While the input signal is being processed by the core engine, its frequency can be changed so that the output impulse is distinguishable from the input impulse noise (or the timing / phase of the two signals can be used to distinguish the system output from the original noise). Both the airborne and system-generated signals will arrive at the feedback microphone. The system propagation time can then be calculated from the time when the input impulse signal arrives at the feedback microphone and the time when the output signal arrives at the feedback microphone.

[0091] It should be noted that this calibration mode can significantly reduce the engineering time required to "tune" a system to account for slight variations in the physical geometry of the microphones used, or slight variations in the physical geometry of a noise-canceling headphone or earphone system. This can significantly reduce the cost of product development. Additionally, by providing an automated method for initial tuning, the calibration mode eliminates the physical challenges of tuning each individual set on the production line due to manufacturing tolerances and variations in individual components, especially microphones. This is another dramatic cost savings during manufacturing.

[0092] Using the system offset time, the processor calculates the specific segment time delay to apply to each segment to generate the precise anti-noise required for that segment. To calculate the precise segment time delay, the processor determines the time required to generate a 180-degree phase shift at the center frequency of a particular frequency segment and adjusts it by the system offset time. Specifically, the segment time delay is calculated as the absolute value of [180-degree phase shift time minus the system offset time].

[0093] After all anti-noise segment time delays have been calculated, the digital signal of each segment is time delayed by the amount calculated for that segment, and all the anti-noise segments so generated are assembled into one anti-noise signal, which is output (e.g., to a speaker system).

[0094] In embodiments using a feedback microphone or other feedback signal source, the processor 10 compares the feedback microphone input signal with the input microphone input signal in both phase and amplitude. The processor uses the phase comparison to adjust the applied delay time and the anti-noise amplitude to adjust the amplitude of the generated noise cancellation signal. In adjusting the amplitude, the processor can individually manipulate the amplitude of each segment within the frequency band range (thus effectively controlling the amplitude of each segment). Alternatively, the frequency composition and amplitude of the feedback signal itself can be used to determine the necessary adjustments to the amplitude and phase of each segment.

[0095] Core Engine Process Details Referring now to FIG. 10, a detailed description of how the signal processing circuit 10 implements the core engine processes is shown. Specifically, FIG. 10 details the software architecture implemented by the signal processing circuit in a preferred embodiment. The user can interact with the processor running the core engine processes in a variety of ways. If desired, the user can activate the silencer configuration mode, as shown at 120. By doing so, the user can optionally configure the width and amplitude of the frequency bands and corresponding segments, as well as the noise threshold gate parameters (data structure 59 (see also FIG. 7)). The chunk size can also be set as a parameter in the user interface.

[0096] Alternatively, the user may simply start the silencer device, as done at 132. In doing so, the user can instruct the core engine process to calibrate the device, as done at 134. The calibration process invokes a calibration engine 126, which is part of the core engine software 124 running in the signal processing circuitry, causing the core engine software 124 to perform the calibration process, as described in detail above, thereby populating data structure 59 with air propagation times, system propagation times, and other calculated parameters. These stored parameters are then used by an anti-noise generation engine 128, which also forms part of the core engine software 124. As shown, the anti-noise generation engine 128 provides a signal to a speaker, which introduces an anti-noise signal into the signal stream, as done at 130.

[0097] Whether required as part of a calibration process during use or as part of a noise mitigation process, the core engine inputs signals from the input and feedback microphones, as done at 136. To mitigate noise during use, a user commands the device to suppress noise via a user interface, as done at 138. As shown, this introduces an anti-noise signal into the signal stream (e.g., into the air 140). If in use, white or pink noise may also be introduced into the signal stream, as done at 142.

[0098] To further illustrate how the signal processing circuitry executing the core engine algorithm operates on exemplary input data, please refer to the following table, which illustrates exemplary user-defined frequency ranges. As can be seen, the applied time delays can be expressed as floating-point numbers corresponding to delay times in seconds. As the data shows, typical applied time delays can be very small, yet the applied time delays are accurately calculated for each given frequency segment. [Table 1]

[0099] In contrast to conventional noise cancellation techniques, this core engine noise cancellation algorithm enables exceptional results at frequencies above 2,000 Hz, and makes it easier to achieve good results across the entire audio spectrum (up to 20,000 Hz) and above (provided there is sufficient processing speed).Currently utilized conventional techniques are only reasonably effective up to about 2,000 Hz and are essentially ineffective above 3,000 Hz.

[0100] Different Use Case Embodiments The basic technology disclosed above can be used in a wide variety of applications, some of which are described below. Embodiment of airborne silencer system (audio)

[0101] The airborne silencer system can be implemented as shown in FIG. 1 and described above. Several different implementations are possible, including a low-power, single-unit system optimized to provide a personal quiet zone. As shown in FIG. 11, the components shown in FIG. 1 are mounted on a desktop or tabletop box containing an amplified speaker for the user. Note that the feedback microphone 26 is positioned within the speaker's sound field. This can be a fixed configuration, or a removable extension arm that doubles as a microphone stand can be utilized to allow the user to position the feedback microphone closer to their location. Another possible implementation is to encode the silencer system into a smartphone or add it as a smartphone app. In this embodiment, the system can utilize the phone's built-in microphone and speaker as well as a headset microphone.

[0102] FIG. 12 shows another embodiment in which the components of FIG. 1 are utilized in a mounting frame adapted to fit over a window in a room, the window frame being indicated by W. In this embodiment, an input microphone 14 captures sound from outside the window, and an amplified speaker 24 introduces anti-noise audio into the room. The feedback microphone may be located on an extension arm or conveniently located near the user. If desired, the feedback microphone may communicate wirelessly with processing circuit 10 using Bluetooth or other wireless protocols. In this embodiment, amplitude adjustments for the various frequency band ranges determined by the calibration model may be significant (as in the headphone embodiment) due to the way walls and windows affect the original noise.

[0103] FIG. 13 illustrates another embodiment in which the components of FIG. 1 are utilized in a plenum-mounted package that fits within an HVAC system air duct or fan system (e.g., bathroom ceiling fan, kitchen exhaust fan, industrial exhaust fan, etc.). In this embodiment, sound generated by the HVAC system or fan system is sampled by input microphone 14, and an anti-noise signal is sent to the air duct system. In this embodiment, several speaker systems can be placed at ventilation ventilators throughout a house or building, if desired, to further reduce HVAC or fan system noise at each location. In such a multi-speaker embodiment, individual feedback microphones 26 may be used in each room, such as adjacent to each ventilator. Processing circuitry 10 can provide individual amplifier volume control signals to each amplified speaker to adjust the sound pressure level of the anti-noise signal for each room. Rooms closer to a noisy fan may require higher amplification of the anti-noise signal than rooms further away. Alternatively, individual devices can be attached to each ventilator to provide location-specific control.

[0104] The second class of airborne silencer devices includes high-powered, multi-unit systems designed to reduce noise coming from high-energy sources. These include systems that reduce noise generated by construction sites, busy streets and highways, and nearby airports. These same high-powered, multi-unit systems can also be used to reduce noise at school boundaries and stadiums. High-powered, multi-unit systems can also be used to reduce road noise in vehicle interiors (cars, trucks, military tanks, boats, airplanes, etc.).

[0105] Figure 14 shows an exemplary high-power multi-unit system utilized for highway noise mitigation. Individual silencer devices implemented as shown in Figure 1 are positioned to capture highway noise using their respective input microphones 14 and transmit anti-noise audio energy into the environment, thereby canceling highway noise in distant sections through destructive interference. Alternatively, silencer devices can be placed directly in a home's yard to provide more direct coverage.

[0106] In highway noise abatement embodiments, each silencer unit is preferably mounted on a vertical stand or other suitable structure so that the speaker is well above the head of a person standing nearby. A feedback microphone 26 may be located a significant distance from the silencer unit, using WiFi communication or other wireless communication protocols to transmit feedback information to the processing circuitry.

[0107] Additionally, if desired, individual silencer devices can wirelessly join a network, such as a mesh network or local area network, to share information about the local sound input and feedback signals available at each silencer device unit. In the case of highway noise, loud noise sources can be tracked by the input microphones of each silencer device. Thus, silencer device sound collection systems can communicate with each other, such as the air brakes on a tractor-trailer truck or a motorcycle with an ineffective muffler motor along a noise-protected highway, employing their respective anti-noise signals to enhance what would otherwise be possible using individual input and feedback microphones rather than anti-noise signals. This achieves a form of diversity noise cancellation made possible by the fact that two mathematically orthogonal sources of information are used: (a) a feedback microphone source and (b) a sound collection input microphone shared over a mesh network or local area network.

[0108] Figure 15 shows how a high-power multi-unit system can be used in a vehicle such as an automobile. Multiple input microphones are placed at noise entry locations within the vehicle. These input data are processed individually, either using multiple cores in a multi-core processor 10 or using multiple processors 10 (here shown as icons on the vehicle's infotainment screen, indicating that the system is factory-installed and integrated into the vehicle's electronics). Because each input signal is processed individually, it is not necessary to segment each signal in the same way. In fact, different types of noise signals typically have their own unique noise signatures (e.g., tire noise is quite different from muffler noise). Therefore, each input signal is segmented in a way that best suits the frequency spectrum and sound pressure level at each different noise location, to produce the desired results for each occupant location.

[0109] While dedicated anti-noise speakers can be used within the vehicle, it is also possible to use the vehicle's existing sound system. Thus, in the illustrated embodiment, processing circuitry 10 provides a stereo or surround sound audio signal that is mixed with audio coming from the vehicle's entertainment system. The mixing can be performed in the digital domain or the audio domain. In either case, however, processing circuitry 10 receives a data signal that provides the processing circuit with information about the volume level the user has selected for the entertainment system. The processor uses this information to adjust the volume of the anti-noise signal so that the noise source is properly neutralized regardless of the volume level the user has selected for the entertainment system. Thus, if the user increases the entertainment volume level, processing circuitry 10 reduces the anti-noise signal sent to the mixer to compensate. The processor is programmed to generate the correct anti-noise sound pressure level within the vehicle regardless of how the user has set the entertainment audio level.

[0110] Yet another class of airborne silencer systems provides reverse airborne functionality. In this type of system, the silencer system is configured in reverse to create a "cone of silence" where private conversations can take place in public without others being able to clearly hear the content of the conversation. As shown in FIG. 16, the silencer device includes one or more outward-facing speakers. The input microphone is located in the center of the speaker arrangement, so that private conversations occur on the "noise input" side of the audio stream. In this embodiment, the feedback microphones 26 are located in a location where a third-party (uninvited) listener cannot easily block these microphones and alter the anti-noise signal being generated, thereby canceling out the conversation.

[0111] Telecommunication microphones, telecommunication headsets, personal headphones / earphones (audio) The telecommunications / headphone system can be implemented as shown in Figure 2 and described above. Several different implementations are possible.

[0112] One such embodiment, shown in FIG. 17, is a handheld smartphone application where the input microphone is on the back of the smartphone, the speaker is the smartphone's receiver speaker, the core engine is implemented using the smartphone's processor, and the feedback microphone is not used (due to its fixed geometry). In this embodiment, the same anti-noise can be added to the microphone transmission. An alternative to this embodiment is a passive headset that plugs into a microphone / headphone jack, Lightning connector, etc. For this alternative embodiment to be effective, the phone's microphone would need to be exposed to ambient noise rather than being in a pocket, purse, or backpack.

[0113] Another consumer embodiment would be a noise cancelling headset, headphones or earphones with the core engine processing performed using a processor and input microphone included as part of the headset, headphones or earphones, as shown in Figure 18. In this embodiment (described above), a lower cost / performance product could use a common processor for both ears in a stereo system, while a higher cost / performance product could use a separate processor for each ear.

[0114] Commercial and military products will likely utilize faster processors, individual processing per earpiece, and separate processors for microphone noise cancellation. In critical applications, additional input microphones will be used to capture intense ambient noise (from stadium crowds, wind, vehicles, weapons, etc.), with the microphone core engine processor in close proximity to the actual transmit microphone, as shown in Figure 19. For example, U.S. Navy SEALs aboard F470 Combat Rubber Raiding Craft will be able to dispense with their current throat microphones and benefit from better communication using this type of system. Similarly, sports announcers will enjoy smaller, lighter, less obtrusive, and more "camera-friendly" headset designs.

[0115] Offline signal processing (audio) An offline signal processing system can be implemented as shown in FIG. 3 and described above. This embodiment can reduce or remove known noise characteristics from recordings or in live situations with an appropriate delay between the action and the actual broadcast. In this embodiment, the core engine can be a "plug-in" for another editing or processing software system integrated into another signal processor, or it can be a standalone device as shown in FIG. 20. Given the characteristics of the noise to be removed (or the signal to be passed in an unknown noise environment: excluding those frequencies from the frequency band range definition and setting the amplitude scaling factor for frequencies not included in the frequency band range definition to 1 will pass only those frequencies), system parameters can be set manually (or via presets) to effectively remove the noise and pass the target signal. Alternatively, a calibration mode can be used to analyze the noise in a "pre-roll" portion and determine appropriate anti-noise settings. Use cases for this embodiment include removing noise from old recordings, reducing noise in "live" situations without adversely affecting the quality of the voice announcer, removing noise from surveillance recordings, and enhancing the audio of surveillance recordings.

[0116] Encryption / Decryption (Above Audio Band) The encryption / decryption system can be implemented as shown in FIG. 4 and described above. The primary use case here is the transmission of private information by covertly encoding it into a narrow segment of a wideband transmission or noise signal in a manner that does not significantly affect the wideband signal. Another use case for this embodiment is the inclusion of additional data or information related to the wideband transmission. As shown in FIG. 21, the encryption "key" would include frequency and amplitude information to "carry out" individual "channels" of the broadband signal that would not substantially affect the broadband content (e.g., white noise). The encoded signal is modulated onto a "carrier" of the appropriate frequency, so that when the "carrier" is added to the broadband content, it appears "normal" to an observer. The "decryption" key would specify amplitude and frequency settings for a frequency band range that result in all information except the "carrier" being canceled. This information could then be demodulated and decoded. This is expected to be most frequently achieved by excluding the "carrier" frequency from the frequency band definition and setting the default amplitude to zero from the excluded frequencies. Preservation of the "carrier" signal can also be further enhanced by appropriately scaling the amplitude of the anti-noise created immediately adjacent to the "carrier" frequency as part of the definition of the "decryption key."

[0117] In another embodiment, the encryption / decryption system can omit the step of "carving out" individual channels within the broadband signal. Rather, these individual channels are simply identified by a processor based on private, a priori knowledge of which portions of the frequency spectrum to select. Such a priori knowledge of these selected channels is stored in memory accessed by the processor and communicated to the intended message recipients by covert or private means. The message to be transmitted is then modulated onto appropriate carriers that carry the message on these individual channels but are mixed with any noise signals that would otherwise be present. The entire wideband signal (including the noise-masked individual channels) is then transmitted. Upon reception, the wideband signal is processed at the decoding end using a processor programmed to subdivide the broadband signal into segments, identify the individual channels carrying messages (based on a priori knowledge of the channel frequencies), and perform noise mitigation on the channels carrying the messages.

[0118] Enhanced recognition, detection, or reception of signal signatures (audio band and above) Recognition, detection, or enhanced reception of signal signatures can be achieved as shown in FIG. 5 and described above. This embodiment of the core engine facilitates recognition, detection, or enhancement of specific types of transmission or device signatures within a noise field. FIG. 22 illustrates the possibility of recognizing or detecting various signatures in a single noise or data transmission field by utilizing multiple instances of the core engine to examine that field. Unlike other embodiments that use microphones to capture incoming noise signals, in this embodiment, noise generated by a specific device is captured over a predetermined period of time, and the captured data is processed by a computer to calculate a moving average or perform other statistical smoothing operations to develop a noise signature for that device. Depending on the nature of the device, this noise signature may be an audio frequency signature (e.g., representing the sound of a blower motor fan) or an electromagnetic frequency signature (e.g., representing radio frequency interference generated by a commutated or electronically switched motor). The noise signature of the device is then used to generate an anti-noise signal. Noise signatures developed in this manner can be stored in memory (or in a database for access by other systems) and accessed by signal processing circuitry as needed to mitigate noise in a particular device or class of devices.

[0119] In addition to being useful for noise mitigation in specific devices, the stored database of noise signatures could also help identify devices by their generated noise signatures by configuring the core engine to pass only those signatures. One use case would allow utilities to detect the energization of non-smart grid products to help predict grid loads from legacy products (e.g., HVAC systems, refrigerators). Another use case would be detecting or enhancing remote or weak electromagnetic communications with known characteristics. Alternatively, the frequency and amplitude parameters of a frequency band range could be configured to detect transmission or noise field disruptions associated with specific occurrences, such as electromagnetic interference that may be caused by unmanned drones or other objects crossing the transmission or noise field, the activation of surveillance or counter-surveillance equipment, tampering with the transmission or field source, or celestial or terrestrial events.

[0120] The foregoing description of the embodiments has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure. Individual elements or features of a particular embodiment are generally not limited to that particular embodiment, but may be interchangeable where applicable and may be used in selected embodiments even if not specifically shown or described. The same may differ in many respects. Such variations are not to be considered departures from the present disclosure, and all such modifications are intended to be within the scope of the present disclosure.

Claims

1. 1. A method for calibrating a noise reduction system incorporated in an audio device, comprising: Disabling the noise reduction system; obtaining, by the audio device, a digitized noise signal from an environment surrounding the audio device; determining a time of flight of the digitized noise signal while the noise abatement system is disabled; enabling the noise reduction system; determining a system propagation time of the digitized noise signal through the noise abatement system while the noise abatement system is enabled; the air propagation time is the transit time for a signal to travel through air from an input microphone to a feedback microphone of the audio device; The method, wherein the system propagation time is a throughput rate of a digital processor circuit that includes the noise abatement system.

2. calculating a system offset time by subtracting the system propagation time from the air propagation time; storing the system offset time in a computer memory of the audio device; The method of claim 1 , wherein the computer memory is accessible to the noise abatement system.

3. outputting, by the noise reduction system, an anti-noise signal into the signal stream output by the audio device; The method of claim 1 , wherein the anti-noise signal is configured using a system offset time.

4. Determining the time of flight of the digitized noise signal over the air comprises: determining a time when the digitized noise signal is captured by the input microphone; determining a time when the digitized noise signal is captured by the feedback microphone positioned proximate to a speaker of the audio device; Calculating the time-of-flight by subtracting the time at which the digitized noise signal is captured by the input microphone from the time at which the digitized noise signal is captured by the feedback microphone. The method of claim 1 further comprising:

5. Determining the system propagation time comprises: determining a time when the digitized noise signal is captured by the input microphone; determining a time when the digitized noise signal is captured by the feedback microphone positioned proximate to a speaker of the audio device; The time during which the digitized noise signal is captured by the feedback microphone Calculate the system propagation time by subtracting the time at which the digitized noise signal is captured by the input microphone from The method of claim 1 further comprising:

6. 6. The method of claim 5, further comprising modifying the digitized noise signal after it is acquired by the audio device and before it is input to the noise abatement system.

7. segmenting the digitized noise signal into a plurality of signal segments, each correlated to a different frequency range; For each signal segment, calculate a segment time delay that causes a 180 degree phase shift at a center frequency of the frequency range associated with the signal segment; Calculating for each signal segment an applied time delay as the absolute value of the segment time delay minus a system offset time; storing the applied time delay for each signal segment in a computer memory of the audio device; The method of claim 3 further comprising:

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