Noise cancellation using piecewise frequency-dependent phase cancellation

Abstract

Noise suppression in a signal stream having unwanted signals referred to as noise is performed by acquiring a digitized noise signal and using digital processor circuitry to subdivide the acquired noise signal into different frequency band segments, thereby producing a plurality of segmented noise signals. Then, for each segmented noise signal, the segmented noise signal is individually time shifted by a processor by an amount that depends on a selected frequency of the segmented noise signal, thereby producing a plurality of shifted segmented noise signals. The precise time shift applied to each noise segment takes into account the frequency content of that segment and system processing time. Amplitude scaling is separately applied to each segmented noise signal. The shifted and amplitude scaled segmented noise signals are then combined to form a composite anti-noise signal that is output into the signal stream to suppress the noise by destructive interference.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Utility Model Patent Application No. 15 / 497,417, filed April 26, 2017, and claims the benefit of U.S. Provisional Application No. 62 / 455,180, filed February 6, 2017. The entire disclosure of the above applications is incorporated herein by reference. Technical Field

[0003] This application generally relates to electronic and automatic noise cancellation techniques. More specifically, this application relates to a noise cancellation technique that generates frequency-dependent anti-noise components in multiple spectral bands for accurate calculation of systems and applications. Background Technology

[0004] For decades, scientists and engineers have been working on the problem of electronic automatic noise cancellation (ANC). The fundamental physics of wave propagation suggests that it is possible to generate "anti-noise" waves that are 180 degrees out of phase with the noise signal and completely eliminate the noise through destructive interference. This is quite effective for simple, repetitive, low-frequency sounds. However, it is less effective for dynamic, rapidly changing sounds, or sounds containing higher frequencies.

[0005] The current best-in-class system (using a hybrid design combining feedforward and feedback) can reduce repetitive noise (such as that from engines or fans) up to 2 kHz. It uses a variant of LMS (Least Mean Square) adaptive filtering to generate an anti-noise signal by repeatedly estimating the transfer function, thus producing minimal real-world noise at the output. While major companies continue to invest heavily in improving automatic noise cancellation, the focus appears to be on refining existing technologies. Furthermore, despite greater processing power, the upper frequency limit for automatic noise cancellation using various adaptive filters is below approximately 4 kHz, with signal attenuation capabilities ranging from 10 dB to 30 dB. Summary of the Invention

[0006] Compared to traditional methods, the disclosed system can eliminate almost any frequency range in offline mode in real time at commercially available processing speeds, and at least the entire audio spectrum, making it more effective than currently used methods.

[0007] Processing speeds and computing power continue to grow rapidly (e.g., Moore's Law has held true since 1965). Some commercial and military markets are less cost-sensitive (compared to most consumer applications) and therefore accept the higher costs of the current highest speed / power ratios. Furthermore, the significant advantages of quantum computing are becoming apparent. Therefore, we envision and present embodiments of the disclosed systems and methods, which are expected to become increasingly commercially viable over time. In this application, for simplicity, we minimize the number of embodiments, describing only five main embodiments defined by the minimum number of different hardware system architectures required to implement the numerous applications of the invention. The hardware system architectures are part of the invention because they are combined in a specific way with variations of our so-called "core engine" signal processing method. The basic elements of the five embodiments are as follows: Figure 1-5 As shown. In summary, the five embodiments can be described as: an airborne system; a communication and personal use system; an offline signal processing system; an encryption / decryption system; and a signal feature recognition, detection, and reception system.

[0008] The disclosed technology enables the real-time construction, utilization, and application of precise noise immunity across the entire signal spectrum. The system / algorithm is flexible, adaptable to higher or lower resolution and control requirements of applications (or, in practice, to product engineers deploying the invention given processing power, cost constraints, or other limitations). Integrating this versatile and effective technology into specific hardware and software system architectures is beneficial for a wide range of applications. We broadly categorize these into five areas based on the system architectures envisioned to date: airborne systems; communication and personal use systems; offline signal processing systems; encryption / decryption systems; and signal feature identification, detection, and reception systems. This list, representing the potential of these systems, is best considered as it is not intended to limit the scope of this application.

[0009] Besides its usefulness in the audio spectrum, the disclosed technique can also be used for electromagnetic signals. Therefore, the disclosed technique enables the real-time elimination of virtually any frequency range in offline mode, and at least the entire audio spectrum, using currently commercially available processors. It is anticipated that with increasing processor speeds, or by integrating the power of multiple processors, this invention can be used to process any electromagnetic signal in real time.

[0010] This algorithm significantly improves noise cancellation performance across the entire audio spectrum by processing discrete frequency bands individually by calculating the ideal noise immunity of the system or application. In fact, this algorithm can successfully cancel the entire audio spectrum in both offline and signal processing applications. It is particularly effective in the audio spectrum of headphones and air-based systems, handling higher frequencies than any other system being deployed. The ability to process discrete frequency bands (and allow frequency ranges or bands to be grouped together, as described below) allows the algorithm to be customized for good results for any specific application, whether in or outside the audio spectrum.

[0011] Processing discrete frequency bands can create noise immunity for dynamic noise sources that change rapidly over time. (Currently available methods are limited to periodic, steady-state sounds, such as engine noise.) Processing discrete frequency bands also reduces the need for multiple input microphones in headphones / in-ear headsets.

[0012] In audio applications, this algorithm also reduces the number of microphones required for microphone noise cancellation and the need for complex beamforming algorithms used to identify target speech from ambient noise. This is especially true for communication headsets, as the noise immunity created for the headset effectively cancels out unwanted signals when added to the microphone input signal with a small delay adjustment (perhaps utilizing passive components to provide the required delay). Feedback processing can be adjusted and stored in presets if needed.

[0013] Whether in the product development phase or in large-scale production, calibration modes reduce the need for costly system tuning of various physical systems.

[0014] Using frequency bands or ranges in algorithm versions has several advantages, including: i. Reduce the required processing power and memory; ii. Easily and quickly maximize system performance for specific applications; iii. Allows for the creation and deployment of pre-built configurations for various types of noise, environments, etc.; iv. Allow the algorithm to be used to enhance the clarity of specific signals in noisy environments. This enables the algorithm to be deployed for hearing aids (e.g., better speech recognition in noisy restaurants), audio monitoring applications (analyzing speech from ambient noise), identifying device features on networks or in noisy fields, or encryption / decryption applications.

[0015] Based on the description herein, other application areas will become apparent. The descriptions and specific examples in this invention are for illustrative purposes only and are not intended to limit the scope of this application. Attached Figure Description

[0016] The accompanying drawings described herein are for illustrative purposes only, representing selected embodiments and not all possible implementations, and are not intended to limit the scope of this application.

[0017] Figure 1 This is a block diagram of a first embodiment of a silencer device used to provide noise suppression or noise elimination in an air system.

[0018] Figure 2 This is a block diagram of a second embodiment of a silencer device used to provide noise suppression or noise cancellation in a communication microphone, communication headset, or headphone / in-ear headphone system.

[0019] Figure 3 This is a block diagram of a third embodiment of a silencer device used to provide noise suppression or noise cancellation in a signal processing system; Figure 4 This is a block diagram of a fourth embodiment of a muffler device used for encrypting and decrypting confidential communications.

[0020] Figure 5 This is a block diagram of a fifth embodiment of a silencer device for removing noise from electromagnetic transmissions and separating specific device features or communications from background noise of power lines (e.g., for power line communications and smart grid applications).

[0021] Figure 6 This is a block diagram illustrating how digital processor circuitry is programmed to execute the core engine algorithm used in the muffler device.

[0022] Figure 7 This is a flowchart further illustrating how digital processor circuitry is programmed to execute the core engine algorithm used in the muffler device.

[0023] Figure 8 It is shown by Figure 6 The core engine algorithm is implemented using signal processing technology.

[0024] Figure 9 This is a detailed signal processing diagram showing the calibration mode used in conjunction with the core engine algorithm.

[0025] Figure 10 This is a flowchart of the core engine process.

[0026] Figure 11 This is an exemplary low-power, single-component air silencer system configured as a desktop personal quiet zone system.

[0027] Figure 12 This is an exemplary low-power, single-component air silencer system configured as a window-type component.

[0028] Figure 13 This is an exemplary low-power, single-component air silencer system configured as an air-pressurized kit.

[0029] Figure 14 This is an exemplary high-power, multi-component airborne muffler system configured for highway noise suppression.

[0030] Figure 15 This is an exemplary high-power, multi-component air silencer system configured to suppress noise in a vehicle.

[0031] Figure 16 This is an exemplary high-power, multi-component air silencer system configured to create a quiet cone zone for protecting private conversations from being overheard by others.

[0032] Figure 17 This is an exemplary smartphone integration embodiment.

[0033] Figure 18 This is an exemplary embodiment of noise-canceling headphones.

[0034] Figure 19 This is another exemplary embodiment of noise-canceling headphones.

[0035] Figure 20 An exemplary processor implementation is shown.

[0036] Figure 21 An exemplary encryption-decryption embodiment is shown.

[0037] Figure 22 An exemplary feature detection concept is shown.

[0038] In the several views of the accompanying drawings, the corresponding reference numerals indicate the corresponding parts. Detailed Implementation

[0039] Example embodiments will now be described more fully with reference to the accompanying drawings.

[0040] The disclosed muffler device can be deployed in a variety of different applications. For illustrative purposes, five exemplary embodiments will be discussed in detail herein. It should be understood that these examples provide an understanding of some different uses of the muffler device. Other uses and applications are also possible within the scope of the appended claims.

[0041] Reference Figure 1This illustration shows a first exemplary embodiment of a muffler device. This embodiment is designed to provide noise cancellation for an airborne system, i.e., sensing incoming ambient noise within the airborne system, generating a noise cancellation signal, and broadcasting it to the surrounding area. As shown, this embodiment includes a digital signal processor circuit 10 with associated memory 12, in which configuration data, referred to herein as application presets, is stored. The digital signal processor circuit can be implemented using commercially available multimedia processor integrated circuits, such as the Broadcom BCM2837Quad Core ARM Cortex A53 processor, etc. Programming details of the digital signal processor circuit are as follows. In a preferred embodiment, the digital signal processor circuit can be implemented using a Raspberry Pi computer, such as a Raspberry Pi 3 model B or better. The device includes the signal processor circuit 10, a VideoCore IV GPU, onboard SDRAM, WiFi and Bluetooth transceiver circuitry, 802.11n wireless LAN circuitry, and support for Bluetooth 4.1 communication. It provides 26 GPIO ports, as well as 4 USB 2 ports, a 100Base-T Ethernet port, DSI and CSI ports, a 4-pole composite video / audio port, and an HDMI 1.4 port. These ports can be used to provide input and output connections to the signal processor circuitry 10, such as... Figure 1-4 The block diagram is shown below.

[0042] Figure 1 The airborne noise cancellation system includes one or more input microphones 14 deployed at physical locations capable of sensing the noise source to be cancelled. Each microphone 14 is connected to a digital audio converter or DAC 16, which converts the analog signal waveform from the connected microphone into digital data through sampling. While different sampling rates can be appropriately employed for this task, the illustrated embodiment uses a sampling rate of 48 kHz. The selection of the sampling rate is based on the frequency range occupied by most of the noise energy, the distance between the input and feedback microphones, and other factors relevant to the specific application and objective.

[0043] Connected between the DAC 16 and the digital signal processor circuitry 10 is an optional gate circuit 18 that passes noise energy above a predetermined threshold and blocks energy below that threshold. Gate circuit 18 can be implemented using software running on the processor or using a separate noise gate integrated circuit. The purpose of the gate circuit is to distinguish between unpleasant ambient background noise levels and higher noise levels associated with unpleasant noise. For example, if an airborne noise cancellation system is deployed to reduce intermittent road noise from a nearby highway, the gate threshold would be configured to open when the sound energy of vehicle traffic is detected, and close when only the rustling of nearby trees is detected. Thus, the gate helps reduce the load on the digital signal processor 10 while preventing unnecessary pumping.

[0044] The noise threshold can be configured by the user, allowing them to set a noise threshold that only considers sounds above that threshold as noise. For example, in a quiet office, the ambient noise level might be around 50 dB SPL. In this environment, the user could set the noise threshold to apply only to signals above 60 dB SPL.

[0045] A digital-to-analog converter (ADC) 20 is connected to the output of the digital signal processor circuit 10. The ADC, complementing the DAC 16, converts the output of the digital signal processor circuit 10 into an analog signal. This analog signal represents a specially constructed noise-cancelling signal designed to cancel noise detected by the input microphone 14. A suitable amplifier 22 and a loudspeaker or transducer system 24 project or broadcast this noise-cancelling signal into the air, where it mixes with and cancels out noise heard from a vantage point within the effective transmission area of ​​the loudspeaker or transducer system 24. Essentially, the loudspeaker system 24 is positioned between the noise source and the listener or receiver, allowing the listener / receiver to receive a signal arriving at his or her location, only the signal from the noise source is canceled out by the noise-cancelling signal from the loudspeaker or transducer system 24.

[0046] If needed, the circuit may also include a white or pink noise source fed to amplifier 22, which essentially mixes a predetermined amount of white or pink noise with an analog signal from digital signal processing circuitry 10 (via ADC 20). This noise source helps to mitigate the effects of the noise cancellation signal by masking auditory transients that may occur when the noise cancellation signal is combined with a noise source signal downstream of the speaker.

[0047] Feedback microphone 26 is located in front of (downstream of) speaker system 24. The feedback microphone is used to sample the signal stream after noise cancellation has been introduced into it. This microphone provides a feedback signal to digital signal processor circuitry 10, which is used to adjust the algorithm controlling how the digital signal processor circuitry generates an appropriate noise cancellation signal. Although Figure 1 Not shown, but the feedback microphone output can be processed by suitable amplification and / or analog-to-digital converter circuitry to provide a feedback signal used by the noise cancellation algorithm. In some applications that utilize only the amplitude of the noise versus noise cancellation signal, the feedback microphone output can be processed in the analog domain to derive an amplitude-voltage signal, which can be processed by averaging or other means if desired. In other applications requiring a more accurate evaluation of the noise versus noise cancellation signal, a phase comparison with the input microphone signal can also be performed. In most implementations of this system, the phase and amplitude of discrete segments of the feedback signal (the creation and processing of discrete segments will be discussed later in this document) are analyzed by comparing them with the input microphone signal or the desired result for the application. The feedback microphone output can be sampled and converted to the digital domain using an analog-to-digital converter. The feedback microphone can be connected to a microphone input or line input that is connected to the video / audio port of a digital signal processor circuit.

[0048] about Figure 1 For other examples of noise suppression systems, please see the section below entitled “Different Use Case Implementations”.

[0049] Figure 2 A second embodiment of the mute device is shown. As will be explained, this embodiment has two signal paths: a receiving audio signal path that suppresses noise in the user's in-ear headphones, earphones, or speaker 24a; and a transmitting audio signal path in which sound captured by the microphone of telephone 34 is processed to suppress ambient noise also captured by the microphone of telephone 34. Therefore, the receiving audio signal path improves the sound heard by the user in the in-ear headphones, earphones, or speaker by reducing or eliminating ambient noise in the environment. This makes listening to music or making phone conversations easier. The transmitting audio signal path is used to partially or completely cancel ambient noise, such as wind noise, entering the microphone of the user's telephone 34. Of course, the same noise cancellation technology can also be used in other systems, not just telephones, including live recording microphones, broadcast microphones, etc.

[0050] refer to Figure 2 The exemplary embodiment illustrated is applicable to a headset system and has the same characteristics as... Figure 1The same components are used in the previous embodiment. In this embodiment, the input microphone is implemented using one or more noise-sensing microphones 14a disposed outside the headset or headphones / in-ear headphones. Analog-to-digital circuitry associated with or integrated into each noise-sensing microphone converts ambient noise into a digital signal, which is fed to digital signal processing circuitry 10. A feedback microphone 26 is disposed within the headphones / in-ear headphones and audio-coupled to the headphone speaker 24a, or the feedback microphone may be omitted entirely, as this is a more tightly controlled physical embodiment. In a system including a feedback microphone, the feedback microphone data includes components of a composite signal that may include desired entertainment content (e.g., music or audio / video audio tracks) and / or speech signals, plus noise and anti-noise, and can be compared with a noise and anti-noise signal or a composite of the input and desired signals.

[0051] Please note that this system differs significantly from traditional systems because it effectively eliminates sounds with frequency components above 2000 Hz, thus reducing the need for acoustic isolation methods deployed in traditional noise-canceling headphones. This results in a lighter, lower-priced product and facilitates efficient deployment within the form factor of "in-ear headphones."

[0052] In this embodiment, noise processing of the silencer device can be implemented independently for each headphone component. For lower-cost headphones / in-ear headphones / headsets, this processing can be implemented jointly for both headphones in a stereo system, or the processing can be implemented by an external processor in a smartphone or other device.

[0053] Another important feature of this embodiment is that the calibration mode will also be used to calculate the appropriate amplitude adjustment required for each frequency band range to compensate for the effects of the physical characteristics of the headphone or in-ear headphone structure on ambient / unwanted noise before the noise enters the ear (calibration mode and frequency band range will be discussed later in this document).

[0054] Similarly, in this embodiment, the noise immunity generated by the system is mixed with the desired music or voice signal (provided by a telephone 30, music player, communication device, etc.) via mixer 30, and both are typically output together by a single speaker. In this use case, the feedback microphone operates only in calibration mode and may be omitted in production units in some cases. Alternatively, the feedback microphone may operate continuously in some multi-speaker applications of this embodiment (e.g., in VR and gaming headsets).

[0055] As described above, in a headset system including a microphone, the output of the digital signal processor circuit 10 can be fed to both the microphone circuit and the headphone speaker circuit. This has already been implemented. Figure 2As shown, the first output signal is fed from the digital signal processor circuit 10 to the first mixer 30, and then to the audio playback system (amplifier 22 and headphone speaker 24a). The second output signal is fed from the digital signal processor circuit 10 to the second mixer 32, and then to the telephone 34 or other voice processing circuitry. Because the ambient noise signal is sampled at a location away from the communication / voice microphone, the desired voice signal is not canceled out. Depending on the application, this alternating noise immunity signal may or may not be adjusted in the calibration mode described above.

[0056] For critical communication applications, such as media broadcasting or warfighter communications, a separate signal processing circuit may be required to cancel microphone noise. This allows for precise elimination of known noise characteristics, avoids the elimination of certain critical information bands, and facilitates further customization for these mission-critical applications through user configuration or presets.

[0057] about Figure 2 For other examples of noise suppression systems, please see the section below entitled “Different Use Case Implementations”.

[0058] Figure 3 A third, more generalized embodiment is shown. In this embodiment, the input signal can come from any source, and the output signal can be connected to circuitry or a device (not shown) that typically processes the input signal. Therefore, Figure 3 The embodiments are designed to be inserted into or placed in line with signal processing or transmission equipment. In this embodiment, feedback is typically not used. Known noise characteristics can be compensated for using system parameters (set using the setting item labeled "Preset" 12). If these characteristics are unknown, the system can be calibrated to eliminate noise by processing material portions that have noise but do not contain signals (e.g., the system can be calibrated to noise characteristics using a "pre-roll" portion of a video clip). Applications of this embodiment include noise removal from event recordings or audio monitoring, or noise removal in "live" situations with an appropriate amount of "broadcast delay," such as the "7-second" delay currently used for language review during live broadcasts.

[0059] While a standalone digital signal processor circuit 10 is shown in the example above, it should be understood that a processor within a mobile device (such as a smartphone) can be used to execute signal processing algorithms; or the core engine can be implemented as a "plugin" to a software suite or integrated into another signal processing system. Therefore, the description herein will also use the term "core engine" to refer to signal processing algorithms, as described more fully below, that run on a processor, such as a standalone digital signal processor circuit or a processor embedded in a smartphone or other device. Figure 3As described in the embodiments, the core engine can be used to process noise in offline mode or in signal processing applications that may not require input microphones, output amplifier speakers, and feedback microphones.

[0060] about Figure 3 For other examples of noise suppression systems, please see the section below entitled “Different Use Case Implementations”.

[0061] Figure 4 A fourth embodiment is illustrated. In this embodiment, two parties wish to share information with each other via files, broadcasts, signal transmissions, or other means, and restrict access to the information. This embodiment requires that both the "encoder" and the "decoder" have access to the device containing the invention.

[0062] The encryption / decryption "key" is a recipe for setting the frequency band range used to encode information. Setting the encryption and decryption "keys" requires consideration of the characteristics of the noise or other signals containing the information; these frequency band range settings include frequency and amplitude information. This allows the encoded signal to be embedded in a very narrow segment of a transmission device that appears to be, for example, a harmless broadband transmission device made of some other material or a "white noise" signal. In one example, embedding a smart transmission device where it appears to be white noise requires a white noise record of appropriate length to carry the complete information during encryption. This invention processes white noise records only for a very narrow frequency range or a set of ranges (i.e., a very narrow noise slice will be "segmented" by the core engine); frequencies not included in this defined range group pass through the system untouched (for frequencies not included in the encryption-side frequency band definition, the default amplitude is set to 1); and the amplitude of the frequency range containing the smart signal can be adjusted to be hidden within the noise. The information to be shared can be encoded in a "carrier signal" within a narrow frequency range using frequency modulation or other techniques (providing another layer of encryption), which will be mixed with the system output (with the "segmented" noise slice). In this example, this will effectively embed information into a signal that appears to be random white noise (or, if needed, another type of broadband or signal transmission).

[0063] For "decryption," the frequency and amplitude settings of the frequency band range describing the location of the recorded information are needed as the "decryption key." During decryption, the default amplitude of frequencies not included in the frequency band definition must be set to 0. This means the system will not generate any output for frequencies not included in the frequency band definition, and therefore will only output the signal that needs to be decoded.

[0064] The ability to select a default amplitude for frequencies not included in the defined frequency band is one of the defining features of this embodiment.

[0065] If the encryption / decryption "key" is shared between the parties through alternative means, the security of information transmission is greatly enhanced, but it can be included in the "calibration header" in the transmission or file, calculated based on timestamps or other settings.

[0066] about Figure 4 For other examples of noise suppression systems, please see the section below entitled “Different Use Case Implementations”.

[0067] Figure 5 A fifth embodiment is shown. In this embodiment, the invention is used to help identify, detect, or receive transmission or equipment characteristics in a noise domain, which can be a noise field existing in the electromagnetic field density of major cities and other areas, and is inherent to transmission lines, etc.

[0068] This invention can aid in the detection, identification, or reception of signals within noise by creating presets for frequency band settings designed to allow only target signals to pass through. These frequency band settings include the frequency and amplitude information needed to identify the “fingerprint” or “characteristic” of the target signal based on the properties of the background noise, as determined in previous analyses. These settings are implemented by excluding the target signal frequency components from the frequency band settings and using a default amplitude of 0 for frequencies not included in the frequency band settings, effectively allowing only the target signal to pass through the system; and by appropriately adjusting the amplitude and frequency of adjacent frequencies or harmonics to further enhance the target signal. This will help detect weak signals that would otherwise go unnoticed in a noisy field.

[0069] For example, when an air conditioning system's compressor starts, a unique pulse is transmitted to the power grid. Power company substations can deploy this invention within a system to help predict peak loads by counting pulses from various sources. In power line communication applications, the characteristics of "normal" noise and fluctuations can be minimized, and the desired communication signal can be enhanced by deploying presets designed for this task. Presets can also be designed to detect or enhance long-distance or weak electromagnetic communications. Similarly, presets can be designed to detect interference in a noise field identified by specific types of objects or other potential threats.

[0070] In this embodiment, multiple instances of the core engine can be deployed on a server (or other multi-core or multi-signaling device) to enable the identification, detection, or reception of various signal or feature types on a single node.

[0071] about Figure 5 For other examples of noise suppression systems, please see the section below entitled “Different Use Case Implementations”.

[0072] Overview of Core Engine Noise Cancellation Algorithms The core engine's noise cancellation algorithm is essentially about creating perfect noise immunity for many small, discrete segments of a noisy signal. The digital signal processing circuit 10 (whether implemented as a standalone circuit or using another device, such as a smartphone processor) is programmed to execute a signal processing algorithm that precisely generates a set of custom noise cancellation signals for each of a set of discrete frequency bands containing the target noise signal or a portion thereof.

[0073] Figure 6 The basic architecture of the core engine noise cancellation algorithm is illustrated. As shown, the acquired noise signal 40 is subdivided into different frequency bands. In the currently preferred embodiment, the width of those segments in each frequency range (and in some embodiments, an amplitude scaling factor applied to noise reduction) can be set differently for each of the different frequency bands 42. In various embodiments, these parameters of the frequency bands can be set via a user interface, preset, or dynamically set based on standards. Each frequency band range is then further subdivided into frequency bands of selected widths. Then, for each frequency band, the digital signal processing circuitry shifts the phase of that segment by an amount that depends on a selected frequency of the segmented noise signal. For example, the selected frequency could be the center frequency of the frequency band. Thus, if a particular frequency band is 100 Hz to 200 Hz, the selected center frequency could be 150 Hz.

[0074] By segmenting the input noisy signal into multiple distinct frequency bands, digital signal processing circuitry enables noise cancellation algorithms to adapt to the specific requirements of a given application. This is achieved by selectively controlling the size of each segment to suit the specific application. For example, each segment across the entire frequency range of the incoming noisy signal can be very small (e.g., 1 Hz). Alternatively, different portions of the frequency range can be subdivided into larger or smaller segments, using smaller (higher resolution) segments at frequencies containing the most important information, or segments needed for shorter wavelengths; while using larger (lower resolution) segments at frequencies carrying less information or having 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 individually based on settings within the frequency band.

[0075] When extremely high noise cancellation accuracy is required, the noise signal is appropriately segmented across the entire spectrum, or, depending on the situation, into smaller segments (e.g., 1 Hz or other sizes). This fine-grained segmentation requires significant processing power. Therefore, in applications requiring low-power, low-cost processors, the core engine noise cancellation algorithm is configured to divide the signal into different frequency bands or ranges. The number of frequency bands can be adjusted in the core engine software code to suit the application's needs. If desired, the digital processor can be programmed to further subdivide the acquired noise signal into different frequency bands by applying wavelet decomposition, thereby generating multiple segmented noise signals to further refine the acquired noise signal.

[0076] For each given application, the segment size and how / whether the size varies across the entire spectrum are initial conditions of the system, determined by defining parameters for different frequency ranges. These parameters can be set via a user interface and then stored as presets for each application in memory 12.

[0077] Once the noise signal has been segmented according to a segmentation plan (automatically and / or based on user configuration) established by the digital signal processing circuitry, phase correction is selectively applied to each segment to generate a segment waveform that substantially cancels out the noise signal within that segment's frequency band through destructive interference. Specifically, the processing circuitry calculates and applies a frequency-dependent delay time 46, taking into account the segment's frequency and any system propagation or delay time. Because this frequency-dependent delay time is calculated and applied individually to each segment, the processing circuitry 10 calculates and applies these phase correction values ​​in parallel or very quickly serially. Subsequently, at 48, the phase-corrected (phase-shifted) segmented noise signals are combined to generate a composite noise immunity signal 50, which is then output to the signal stream to suppress noise through destructive interference. Figure 6 As shown, the noise immunity signal can be introduced into the signal stream through an amplified loudspeaker system or other transducer 24. Alternatively, in some applications, a suitable digital or analog mixer can be used to introduce the noise immunity signal into the signal stream.

[0078] In some embodiments, noise suppression can be further enhanced by using a feedback signal. Therefore, as... Figure 6 As shown, the feedback microphone 26 can be located within the signal stream, downstream of the point where the noise-resistant signal is introduced. In this way, the feedback microphone senses the result of destructive interference between the noise signal and the noise-resistant signal. The feedback signal derived from the feedback microphone is then provided to the processing circuit 10 for adjusting the amplitude and / or phase of the noise-resistant signal. This feedback processing generally... Figure 6As shown at point 52. Feedback processing 52 includes converting the feedback microphone signal into a suitable 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 noise immunity signal to achieve maximum noise suppression. When the noise immunity signal and the noise signal interfere in an optimal manner, the feedback microphone signal will detect zero because the noise energy and the noise immunity energy optimally cancel each other out.

[0079] In one embodiment, the amplitude of the combined noise immunity signal 50 can be adjusted based on the feedback microphone signal. Alternatively, the amplitude and phase of each frequency band can be adjusted individually. This can be achieved by comparing the amplitude and phase of the signal stream at the feedback point with the input signal and adjusting the noise immunity parameters. Optionally, the frequency content and amplitude of the feedback signal itself can be examined to indicate the necessary adjustments to the noise immunity parameters, thereby improving the results by fine-tuning the frequency-dependent delay time 46 and amplitude of each band.

[0080] Determination of frequency-dependent delay time The signal processing circuit 10 calculates the frequency-dependent time delay for each segment by taking into account several factors. One of these factors is the calculated 180-degree phase shift time, which is associated with a predetermined frequency (e.g., the segment center frequency) for each individual signal segment.

[0081] Depending on the application and available processing power, this calculation can be performed in calibration mode and stored in a table in memory 12, or it can be continuously recalculated in real time. The exact time delay required to create appropriate noise immunity for each frequency "f" is calculated by the following formula: (1 / f) / 2, where

[0082] f is the predetermined frequency of the segment (e.g., the center frequency).

[0083] Another factor used in signal processing circuitry is the system offset time, which depends on two factors: the propagation time in the air and the system propagation time.

[0084] To generate an accurate noise cancellation signal, the processing circuitry relies on prior knowledge of the airborne sound propagation speed, which is measured as the time it takes for the signal to travel from the input microphone to the feedback microphone. This propagation time is referred to herein as the "airborne propagation time." The processing circuitry also relies on prior knowledge of the time taken for the processor 10 and associated input and output components (e.g., 14, 16, 18, 20, 22, 24) to generate the noise cancellation signal; this time is referred to herein as the "system propagation time." These data are needed to ensure that the noise cancellation signal is precisely phase-matched to the noise signal, resulting in perfect cancellation. The speed at which the noise signal propagates in the air depends on various physical factors, such as air temperature, pressure, density, and humidity. In various embodiments, the processor computation time and circuit throughput time depend on the processor speed, the speed of bus access to memory 12, and the signal delays through the input / output circuitry associated with the processor 10.

[0085] In a preferred embodiment, these airborne and system propagation times are measured in calibration mode and stored in memory 12. Calibration mode can be manually requested by the user through a user interface, or processor 10 can be programmed to perform calibration periodically or automatically in response to measured air temperature, pressure, density, and humidity conditions.

[0086] Therefore, in the preferred embodiment, the airborne propagation time from the detection of a noise signal at the input microphone 14 to the subsequent detection of a noise signal at the feedback microphone 26 is measured. Depending on the application, the two microphones can be positioned at a constant separation distance (e.g., at...). Figure 2 In the headset embodiment, or the separation distance between the two microphones may depend on their positions within the field. The time delay resulting from the processing of the input signal, its output to the speaker system 24 (24a), and its reception at the feedback microphone 26 corresponds to the system propagation time.

[0087] Once the airborne time and system propagation time are measured and stored in calibration mode, signal processing circuit 10 calculates the "system offset time," which is the arithmetic difference between the airborne time and the system propagation time. This difference can also be calculated in real time or stored in memory 12. In some fixed applications, such as headphones, an onboard calibration mode may not be necessary because calibration can be performed on the production line or based on the known fixed geometry of the headphones. The system offset time can be stored as a constant (or dynamically calculated in some applications) for use in the noise reduction calculations described herein.

[0088] For each discrete frequency band to be processed, an anti-noise signal is created by delaying the processed signal by an absolute value equal to the time of the "180-degree phase shift time" minus the "system offset time" for that discrete frequency band. This value is referred to herein as the "application time delay". The "application time delay" for each frequency band can be stored in a table or calculated continuously in various implementations of the algorithm.

[0089] Figure 7 The method by which the signal processing circuitry can be programmed to implement the core engine noise cancellation algorithm is shown in more detail. The programming process begins with a series of steps that fill a set of data structures 59 within memory 12, where the parameters used by the algorithm are stored for access when needed. Further details of the core engine process will be provided below. Figure 10 Let's have a discussion.

[0090] refer to Figure 7 First, records containing the selected block size are stored in data structure 59. The block size is the length of the time slice to be processed in each iteration executed by the core engine, represented by the number of samples processed as a data set or "block". The block size is primarily based on the application (the frequency range to be processed and the required resolution), the system propagation time, and the travel time between the input and output of noise signals transmitted in air or other media (processing and noise-injecting into the signal stream must be completed before the original signal passes through the noise-resistant output point).

[0091] For example, for an airborne system processing the entire audio spectrum, a distance of 5.0" between the input and output microphones, a sampling rate of 48 kHz, a system propagation time of 0.2 ms, and a block size of 16 would be appropriate (at a sampling rate of 48 kHz, 16 samples equate to approximately 0.3333 ms in time; under standard temperature and pressure, sound travels approximately 4.5" in air during that time period). By limiting system calls and state changes to once per block, processor operation can be optimized to efficiently handle the required block size.

[0092] When a noise cancellation device is configured for a given application, this block size record is typically stored at the beginning. In most cases, it is neither necessary nor desirable to change the block size record while the core engine's noise cancellation algorithm is running.

[0093] The frequency band range, the segment size within each frequency band, and the output scaling factor for each frequency band are set as initial conditions according to the application and stored in data structure 59. These parameters can be set in the user interface, included as presets in the system, or calculated dynamically.

[0094] Next, at point 62, the processing circuit measures and stores in data structure 59 the “system propagation time” corresponding to the time consumed by the processing circuit and its associated input and output circuits in performing the noise cancellation process. This is accomplished by operating the processing circuit in a calibration mode as described below, where a noise signal is provided to the processing circuit, which acts to generate an anti-noise signal and an output. The time elapsed from the input noise signal to the output anti-noise signal represents the system propagation time. This value is stored in data structure 59.

[0095] Furthermore, at position 64, the processing circuitry measures the airborne time and stores it in data structure 59. This operation is also performed by the processing circuitry in the calibration mode discussed below. In this case, the processing circuitry is switched to a mode that does not produce any output. The time elapsed between the input microphone received signal and the feedback microphone received signal is measured and stored as the airborne time.

[0096] Next, at position 66, the processing circuitry calculates the "system offset time" and stores it in data structure 59. This system offset time is defined as the air propagation time minus the system propagation time. This value will be used later by the processing circuitry when calculating the application delay time.

[0097] Using the aforementioned calibration parameters calculated and stored in this way, the core engine noise cancellation algorithm can now be pre-computed for specific segments (or, if there is sufficient processing power, these calculations can be performed in real time).

[0098] As shown in the figure, steps 68 and subsequent steps 70 and 72 are executed in parallel (or quickly serially) for each segment, depending on the frequency band settings. If a given application has 1000 segments, steps 68-70 are executed 1000 times, preferably in parallel, and the data is stored in data structure 59.

[0099] In step 70, the "180-degree phase shift time" is adjusted by subtracting the previously stored "system offset time" for each segment. The processor circuitry calculates and stores the absolute value of this value as the "application delay time," which is a positive number indicating the amount of phase shift to be applied to the corresponding segment.

[0100] The core engine uses stored data to process frequency bands faster (by pre-applying a pre-calculated time offset to all frequency bands). In step 72, the processor circuitry performs a phase shift of the segment noise signal by storing the segment noise signal time shift as the amount of the segment's "application delay time". Furthermore, if the frequency range is set or feedback processing 52 ( Figure 6If amplitude adjustment (or fine-tuning of phase adjustment) is required, then that adjustment is also applied here (in some embodiments, phase shift and amplitude adjustment can be applied simultaneously by storing the information as a vector). Depending on the system architecture, all segments can be processed in parallel or in a fast serial manner.

[0101] Once all segments of a particular block have been properly adjusted, at 74, the processing circuit reassembles all the processed segments to generate an anti-noise waveform for output to the signal stream.

[0102] To further understand the process executed by the processing circuitry, refer now to Figure 8 , Figure 8 A more intuitive representation of how to process noise signals is provided. Starting from step 80, noise signal 82 is acquired. In... Figure 8 In this context, noise signals are described as time-varying signals that include many different frequency components or harmonics.

[0103] In step 84, according to the combination Figure 7 The parameter 59 discussed is that the noise signal spectrum is subdivided into segments 86. For illustrative purposes, it is assumed that... Figure 7 In the spectrum, the time-varying noise signal 82 has already been represented in the frequency domain, with the lowest frequency component assigned to the far left of the spectrum 86, and the highest frequency component, or harmonics, assigned to the far right. For example, the spectrum 86 may include 20 Hz to 20,000 Hz, covering the entire range of generally acceptable human hearing. Of course, the spectrum can be allocated differently depending on the application.

[0104] It should be recognized that although the noise signal has been expressed in the frequency domain of spectrum 86, the noise signal is inherently a time-varying signal. Therefore, the energy in each frequency band will fluctuate over time. To illustrate this fluctuation, a waterfall plot 88 is also depicted, showing how the energy within each frequency band changes along the vertical axis over time.

[0105] As in step 90, for each segment, a frequency-dependent phase shift (i.e., "application delay time") is applied individually. To illustrate this, waveform 92 represents the noise frequency within the segment before the shift. Waveform 94 represents the same noise frequency after applying the "system offset time." Finally, waveform 96 represents the composite noise frequency after applying the "180-degree phase shift time" (note that this is for illustrative purposes only; in actual processing, only the "application delay time," i.e., the absolute value of the 180-degree phase shift time minus the system offset time, is applied). For this illustration, it is also assumed that the segments being processed do not require amplitude scaling.

[0106] By combining the time-shift 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 in step 102, the anti-noise signal 100 is mixed with the original noise signal 104, causing them to interfere destructively, effectively eliminating or suppressing the noise signal. What remains is any information-carrying signal 108 that can be retrieved in step 106.

[0107] Calibration mode Figure 9 The diagram illustrates how the processing circuitry 10, input microphone 14, amplifying speaker 24, and feedback microphone 26 achieve calibration by selectively enabling and disabling the core engine algorithm during measurement.

[0108] In the current preferred embodiment, the airborne propagation time is calculated with the core engine noise immunity system and output disabled. In this state, the airborne propagation time is calculated as the time difference between the time it takes to capture input noise at the input microphone 14 and the time it takes to capture noise at the feedback microphone 26. The system propagation time is measured with the core engine noise immunity system enabled. The same input is again introduced into 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) located in front of the feedback microphone 26. While processing the input signal in the core engine, its frequency can be changed to distinguish the output pulse from the input pulse noise (or the system output can be distinguished from the original noise using the timing / phase of the two signals). Both the airborne and system-generated signals will reach the feedback microphone. The system propagation time can then be calculated using the time it takes for the input pulse signal to reach the feedback microphone and the time it takes for the output signal to reach the feedback microphone.

[0109] Please note that this calibration mode effectively eliminates the significant engineering time required to "tune" a system, explaining minute differences or physical geometries between microphones used in noise-cancelling headphones or in-ear headphone systems. This can result in substantial savings in product development costs. The calibration mode also addresses the physical challenges of tuning individual devices on the production line due to manufacturing tolerances and variations in individual components, particularly microphones, by providing an automated initial tuning method. This also significantly reduces production costs.

[0110] Based on the system offset time, the processor calculates a specific "segment time delay" applied to each segment to create the precise noise immunity required for that segment. To calculate the precise segment time delay, the processor determines the time required to produce a 180-degree phase shift at the center frequency of a specific frequency segment and adjusts it using the system offset time. Specifically, the segment time delay is calculated as the 180-degree phase shift time minus the absolute value of the system offset time.

[0111] After calculating the time delay of all noise immunity segments, the digital signal of each segment is time-delayed by the computational amount of that segment, and then all the noise immunity segments generated in this way are assembled into a single noise immunity signal and output (e.g., to a speaker system).

[0112] In embodiments employing a feedback microphone or other feedback signal source, the processor 10 compares the feedback microphone input signal and the input microphone input signal in terms of both phase and amplitude. The processor uses the phase comparison to adjust the "application delay time" and uses the noise immunity amplitude to adjust the amplitude of the generated noise cancellation signal. When adjusting the amplitude, the processor can manipulate the amplitude of each segment individually within a frequency band (thus effectively controlling the amplitude of each segment). Alternatively, the necessary adjustments to the amplitude and phase of each segment can be determined by the frequency composition and amplitude of the feedback signal itself.

[0113] Detailed information about the core engine process Now for reference Figure 10 , Figure 10 This is a detailed explanation of how the signal processing circuit 10 implements the core engine process. Specifically, Figure 10 The software architecture implemented by signal processing circuitry in the preferred embodiment is described in detail. Users can interact with the processor running the core engine process in various ways. If needed, the user can initiate the configuration mode of the silencing device, as described in 120. By doing so, the user can also optionally configure the frequency band and corresponding segment width and amplitude, as well as the noise threshold gate parameters (part of data structure 59), see [link to relevant documentation]. Figure 7 The block size can also be set as a user interface parameter.

[0114] Alternatively, the user can simply activate the muffler device as described in 132. Then, in 134, the user can command the core engine process to calibrate the device. The calibration process is implemented by the core engine software 124 calling the calibration engine 126, which is part of the core engine software 124 running on the signal processing circuitry. The calibration engine 126 performs the calibration process described above, thereby filling data structure 59 with propagation time in air, system propagation time, and other calculated parameters. These stored parameters are then used by the noise immunity generation engine 128, which is also part of the core engine software 124. As shown, the noise immunity generation engine 128 provides a signal to the speaker, which then introduces the noise immunity signal into the signal stream, as described in 130.

[0115] Whether as part of the calibration process or as part of the noise cancellation process during use, the core engine receives signals from the input and feedback microphones, as described on 136. To achieve noise suppression during use, at 138, the user commands the device to suppress noise via the user interface. As shown, this results in an anti-noise signal being introduced into the signal stream (e.g., air 140). White noise or pink noise may also be introduced into the signal stream if used, as described on 142.

[0116] To further illustrate how the signal processing circuitry running the core engine algorithm operates on the exemplary input data, please refer to the table below. The table specifies exemplary user-defined frequency ranges. It can be seen that the application time delay can be expressed as a floating-point number corresponding to the delay time (in seconds). The data shows that typical application time delays may be very small, but each application time delay is precisely calculated for each given frequency band.

[0117]

[0118]

[0119] Compared to traditional noise cancellation techniques, the core engine's noise cancellation algorithm delivers excellent results at frequencies above 2000 Hz and helps achieve good results across the entire audio spectrum (up to 20,000 Hz) and even higher frequencies (provided there is sufficient processing speed). Currently used traditional techniques are only effective at around 2000 Hz and are largely ineffective above 3000 Hz.

[0120] Different use case examples The basic techniques disclosed above can be applied to a variety of different uses. Some of these use cases will be described below.

[0121] Air-based silencer system (audio) examples Air-based silencer systems can be like Figure 1 This is achieved as shown and described above. Several different embodiments may exist. These include low-power, single-component systems optimized for providing a personal quiet area. Figure 11 As shown, Figure 1The components described herein are mounted in a desktop or desktop box containing an amplifying speaker, which is pointed towards the user. Note that the feedback microphone 26 is positioned within the sound field of the speaker; this can be a fixed configuration, or a detachable extension arm can be deployed, which also serves as a microphone stand to allow the user to place the feedback microphone closer to the user. Another possible embodiment is to encode the mute system into a smartphone, or add it as a smartphone application. In this embodiment, the system can be created using the phone's built-in microphone and speaker, as well as the microphone of a headset.

[0122] Figure 12 An alternative embodiment is shown, wherein Figure 1 The components are deployed in a mounting frame suitable for installation within a room window, shown as W. In this embodiment, the input microphone 14 captures sound from outside the window, and the amplifying speaker 24 introduces noise-resistant audio into the room. The feedback microphone can be located on an extension arm or conveniently placed close to the user. If desired, the feedback microphone can wirelessly communicate with the processing circuitry 10 using Bluetooth or other wireless protocols. In this embodiment, due to the way walls and windows affect the original noise, amplitude adjustments to various frequency bands determined in the calibration model may be important (as in the headphone embodiment).

[0123] Figure 13 Another embodiment is shown, in which Figure 1 The components are deployed in an air-pressurized kit suitable for installation within the air ducts of an HVAC system or a fan system (e.g., bathroom ceiling fans, kitchen ventilation fans, industrial ventilation fans, etc.). In this embodiment, sound generated by the HVAC or fan system is sampled by input microphone 14, and an anti-noise signal is injected into the air duct system. In this embodiment, if desired, several speaker systems can be deployed at vents throughout the residence or building to further reduce HVAC or fan system noise at various locations. This multi-speaker embodiment can use a separate feedback microphone 26 in each room, for example, adjacent to each vent. Processing circuitry 10 can provide amplifier volume control signals to each amplifying speaker individually to customize the sound pressure level of the anti-noise signal for each room. Rooms closer to the noisy blower may require higher anti-noise signal amplification than rooms further away. Alternatively, a separate device can be installed at each vent to provide location-specific control.

[0124] The second category of airborne noise suppressors includes high-power, multi-component systems designed to suppress noise from high-energy sources, including systems for suppressing noise from construction sites, busy streets or highways, and nearby airports. These same high-power, multi-component systems can also be used for noise suppression around schools and stadiums. Furthermore, high-power, multi-component systems can be used to suppress road noise in vehicle compartments (e.g., cars, trucks, military tanks, ships, aircraft, etc.).

[0125] Figure 14 An exemplary high-power, multi-component system for highway noise suppression is shown. For example... Figure 1 Each silencer device shown is positioned to intercept road noise using its respective input microphone 14 and inject noise-resistant audio energy into the environment, causing the road noise to be eliminated by destructive interference at a distant subdivision. Alternatively, the silencer devices can be located directly in the yard of a house to provide more direct coverage.

[0126] In the highway noise suppression embodiment, a single muffler device is preferably mounted on a vertical bracket or other suitable structure, such that the speaker is positioned well above the head of anyone standing nearby. A feedback microphone 26 can be placed at a considerable distance from the muffler device to send feedback information to the processing circuitry using WiFi or other wireless communication protocols.

[0127] Furthermore, if needed, individual muffler devices can be wirelessly connected to a network, such as a mesh network or LAN, allowing the muffler devices to share information about the local sound input and feedback signals obtained by each muffler device unit. In the case of highway noise, large noise sources can be tracked through the input microphones of the respective muffler devices. Thus, a collective system of muffler devices, such as a semi-truck with air brakes along a noise-protected highway or a motorcycle with an ineffective muffler motor, can communicate with each other and adjust their respective noise immunity signals to increase the possibility of using individual input and feedback microphones. This achieves a form of diversity noise cancellation due to the use of two different mathematically orthogonal information sources: (a) a feedback microphone source and (b) a collective input microphone shared via a mesh network or LAN.

[0128] Figure 15This illustrates how a high-power, multi-component system can be deployed in a vehicle such as a motor vehicle. Multiple input microphones are placed at noise entry points within the cabin. These input data are processed individually, either through multiple cores of a multi-core processor 10 or through multiple processors 10 (shown here as icons on the car's infotainment screen, representing the system being factory-installed and embedded in the car's electronics). Because each input signal is processed individually, it is not necessary to segment each signal in the same way. In fact, each different type of noise signal typically has its own noise characteristics (e.g., tire noise is very different from muffler noise). Therefore, each input signal is segmented in a manner best suited to the spectrum and sound pressure level of each different noise point to provide the desired results at each passenger location.

[0129] While dedicated noise-canceling speakers can be used inside the vehicle cabin, existing sound systems within the vehicle can also be used. Therefore, in the illustrated embodiment, processing circuitry 10 provides a stereo or optional surround sound audio signal, which is mixed with audio from the in-cabin entertainment system. Mixing can be performed in the digital or audio domain. However, in either case, processing circuitry 10 receives a data signal that provides information about the user-selected entertainment system volume level. The processor uses this information to adjust the volume of the noise-canceling signal so that noise sources are properly canceled regardless of how high the user-selected entertainment system volume level is. Thus, when the user increases the entertainment volume, processing circuitry 10 reduces the noise-canceling signal injected into the mixer to compensate. The processor is programmed to ensure that the correct noise-canceling sound pressure level is generated inside the cabin, regardless of how the user sets the entertainment audio level.

[0130] Another type of airborne silencer system offers a reverse airborne function. In this type of system, the silencer system is configured in reverse to create a "silent cone," allowing private conversations to be heard openly while remaining inaudible to others. Figure 16 As shown, the mute device is configured with one or more outward-facing speakers. An input microphone is positioned at the center of the speaker assembly, thus placing the parties engaged in a private conversation on the "noise input" side of the audio stream. In this embodiment, a feedback microphone 26 is deployed in a location where a third-party listener (uninvited listener) cannot easily block the microphone to alter the noise-canceling signal being generated to cancel out the conversation.

[0131] Communication microphones, communication headsets, and personal headphones / in-ear headphones (audio) Communication / headphone systems can be like Figure 2 As shown and as described above, this can be implemented. Several different embodiments may exist.

[0132] Figure 17One such embodiment is a smartphone handheld 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 no feedback microphone is used (due to its fixed geometry). In this embodiment, the same noise immunity can be added to the microphone transmission device. Alternatives to this embodiment include passive headsets that plug into the microphone / headphone jack, Lightning connectors, etc. For this alternative embodiment to be effective, the microphone on the phone needs to be exposed to ambient noise, rather than being in a pocket, purse, or backpack.

[0133] Another consumer-grade implementation is a noise-cancelling headset, headphones, or in-ear headphones, which uses a processor and input microphone integrated as part of the headset, headphones, or in-ear headphones to perform core engine processing, such as Figure 18 As shown. In this embodiment (as previously described), for lower-cost / performance products, both ears of the stereo system can share a single processor, while for higher-cost / performance products, each ear needs to use a different processor.

[0134] Commercial and military-grade products may use faster processors, processing each headphone unit individually and using separate processors to cancel microphone noise. For the most critical applications, additional input microphones will be used to capture severe ambient noise (noise from stadium crowds, wind, vehicles, ordnance, etc.), and the microphone core engine processor will be very close to that of an actual transmission microphone, such as... Figure 19 As shown. For example, SEALs on F470 combat reconnaissance rubber boats could replace their current throat microphones with this type of system for better communication. Similarly, sports broadcasters would prefer smaller, lighter, less conspicuous, and more "photogenic" headset designs.

[0135] Offline signal processing (audio) Offline signal processing systems can, for example, Figure 3 This is achieved as shown and as described above. In this embodiment, known noise characteristics can be reduced or eliminated from the recording, or, in field conditions, a suitable delay can be achieved between the action and the actual broadcast. In this embodiment, the core engine can be a "plug-in" to another editing or processing software system, incorporated into another signal processor, or used as... Figure 20The standalone device is shown. Based on the characteristics of the noise to be removed (or, alternatively, the characteristics of the signal to be transmitted in an unknown noise environment: excluding those frequencies from the frequency band range setting, and setting the amplitude scaling factor to 1 for frequencies not included in the frequency band range definition, thus allowing only those frequencies to pass), system parameters can be manually (or via presets) to effectively remove noise and transmit the target signal. Alternatively, a calibration mode can be used to analyze noise in the "pre-broadcast" section to determine appropriate noise immunity settings. Use cases for this embodiment include removing noise from old recordings, reducing noise in "real-time" situations without negatively impacting voice broadcast quality, removing noise from surveillance recordings, enhancing audio in surveillance recordings, etc.

[0136] Encrypt / decrypt (audio tape or more) Encryption / decryption systems can be like Figure 4 This is implemented as shown and described above. The primary use case here is to transmit private information by secretly encoding it into a narrow segment of broadband transmission or noise signal in a way that does not substantially affect the broadband signal. Another use case of this embodiment is to include additional data or information about the broadband transmission. Figure 21 As shown, the encryption "key" will include frequency and amplitude information for "cutting out" "discrete" channels in the broadband signal that do not substantially affect the broadband content (e.g., white noise). The encoded signal will be modulated onto a "carrier" at an appropriate frequency so that when the "carrier" is added to the broadband content, it appears "normal" to the observer. The "decryption" key will specify the amplitude and frequency settings for the bandwidth range, thereby eliminating all information outside those "carriers," which can then be demodulated and decoded. This is typically achieved by excluding "carrier" frequencies from the bandwidth definition and setting the default amplitude of the excluded frequencies to 0. As part of the "decryption key" definition, the preservation of the "carrier" signal can be further enhanced by appropriately scaling the noise-resistant amplitude generated by frequencies immediately adjacent to the "carrier" frequency.

[0137] In an alternative embodiment, the encryption / decryption system can omit the step of "cutting out" discrete channels in the broadband signal. Instead, these discrete channels are simply identified by the processor based on proprietary prior knowledge of which portions of the spectrum to select. This prior knowledge of the selected channels is stored in processor-accessible memory and is known to the intended message recipient in a secret or private manner. The message to be transmitted is then modulated onto a suitable carrier that places the message in these discrete channels, but mixes it with any noise that might otherwise be present. The entire broadband signal (including the noise-masked discrete channels) is then transmitted. Once the broadband signal is received, it is processed on the decoding side by a processor programmed to subdivide the broadband signal into segments; identify the discrete channels carrying the message (based on prior knowledge of the channel frequencies); and perform noise cancellation on the message-carrying channels.

[0138] Signal feature identification, detection, or reception enhancement (audio band and beyond). Signal feature recognition, detection, or reception enhancement can be achieved as follows: Figure 5 This is achieved as shown and as described above. This embodiment of the core engine facilitates the identification, detection, or enhancement of specific types of transmission or device characteristics within a noise field. Figure 22 This demonstrates the possibility of identifying or detecting various features in a single noise or data transmission field by deploying multiple instances of the core engine. Unlike other embodiments using microphones to capture input noise signals, in this embodiment, noise generated by a specific device is captured over a predetermined time period, and the captured data is then processed by calculating a moving average or other statistical smoothing to generate a noise characteristic of the device. Depending on the nature of the device, this noise characteristic can be an audio characteristic (e.g., representing the sound of a blower motor fan) or an electromagnetic frequency characteristic (e.g., representing radio frequency interference generated by a commutator motor or electronically switched motor). The noise characteristic of the device is then used to generate an anti-noise signal. The noise characteristic thus generated can be stored in memory (or in a database for access by other systems) and accessed as needed by signal processing circuitry to reduce noise from a specific device or device class.

[0139] Besides being useful for noise suppression of specific devices, the database of stored noise characteristics can also identify devices by the characteristics of the generated noise, and by setting the core engine to use only those characteristics. One use case is to enable power companies to detect the supply of power from non-smart grid products to help predict grid load caused by conventional products (HVAC systems, refrigerators, etc.). Another use case is to detect or enhance long-distance or weak electromagnetic communications with known characteristics. Alternatively, frequency and amplitude parameters of the bandwidth can be set to detect interference in transmission or noise fields associated with specific events, such as electromagnetic interference that may be caused by drones or other objects passing through the transmission or noise field, activation of surveillance or counter-surveillance equipment, interference with transmission or field sources, celestial or ground events, etc.

[0140] The foregoing description of embodiments is provided for illustrative and descriptive purposes. It is not intended to be exhaustive or limiting of the present application. Various elements or features of a particular embodiment are generally not limited to that particular embodiment, but are interchangeable where applicable and can be used in selected embodiments, even if not specifically shown or described. Differences may also occur in many aspects. Such changes are not considered to depart from the present application, and all such modifications are intended to be included within the scope of this application.

Claims

1. A calibration method for a noise suppression system embedded in an audio device, comprising: Disable the noise suppression system; The audio device acquires known signals from the environment surrounding the audio device; When the noise suppression system is disabled, the air propagation time of the known signal is determined, wherein the air propagation time is the time it takes for the signal to travel through the air from the input microphone of the audio device to the speaker of the audio device; Activate the noise suppression system; as well as When the noise suppression system is activated, the system propagation time of the known signal through the noise suppression system is determined, wherein the system propagation time is the throughput speed of the digital processor circuitry including the noise suppression system. The system offset time is calculated by subtracting the system propagation time from the air propagation time. The noise suppression system outputs an anti-noise signal to the signal stream output by the audio device, wherein the anti-noise signal is constructed using the system offset time.

2. The method of claim 1, further comprising: The system offset time is stored in the computer memory of the audio device, wherein the noise suppression system has access to the computer memory.

3. The method of claim 1, wherein determining the airborne time of the known signal further comprises: Determine the time at which the input microphone captures the known signal; Determine the time at which the feedback microphone, arranged adjacent to the speaker of the audio device, captures the known signal; and The airborne time is calculated by subtracting the time it takes for the input microphone to capture the known signal from the time it takes for the known signal to be captured from the feedback microphone.

4. The method of claim 1, wherein determining the system propagation time further includes Determine the time at which the input microphone captures the known signal; Determine the time at which the feedback microphone, arranged adjacent to the speaker of the audio device, captures the known signal; and The system propagation time is calculated by subtracting the time it takes for the input microphone to capture the known signal from the time it takes for the known signal to be captured from the feedback microphone.

5. The method of claim 4, further comprising altering the known signal after the audio device acquires the known signal but before inputting the known signal into the noise suppression system.

6. The method of claim 1, further comprising: The known signal is divided into multiple signal segments, each of which is associated with a different frequency range; For each signal segment, a segment time delay is calculated, wherein the segment time delay causes a 180-degree phase shift at the center frequency of the frequency range associated with the given signal segment; For each signal segment, the applied time delay is calculated as the segment time delay minus the absolute value of the system offset time; as well as For each signal segment, the application time delay is stored in the computer memory of the audio device.

Images