Patent Publication Number: US-9431001-B2

Title: Device, system and method of noise control

Description:
CROSS-REFERENCE 
     This application claims the benefit of and priority from U.S. Provisional Patent application No. 61/484,722, entitled “Device, System and Method of Noise Control”, filed May 11, 2011, the entire disclosures of which is incorporated herein by reference. 
    
    
     BACKGROUND 
     Noise in general, and tonal noise in particular is very annoying. Low-frequency noise is very penetrating, travels very long distances and is difficult to attenuate using traditional passive control measures. 
     Passive noise control technology, which usually involves using absorptive materials or noise partitions, enclosures, barriers and silencers, can be bulky, ineffective and rather expensive at low frequencies. Active Noise Control (ANC), on the other hand, can be very efficient and relatively cheaper in reducing low-frequency noise. 
     Active Noise Control (ANC) is a technology using noise to reduce noise. It is based on the principle of superposition of sound waves. Generally, sound is a wave, which is traveling in space. If another, second sound wave having the same amplitude but opposite phase to the first sound wave can be created, the first wave can be totally cancelled. The second sound wave is named “anti-noise”. 
     SUMMARY 
     Some demonstrative embodiments include devices, systems and methods of noise control. 
     In some demonstrative embodiments, an active noise control system may include a controller to control noise within a predefined noise-control zone, the controller is to receive a plurality of noise inputs representing acoustic noise at a plurality of predefined noise sensing locations, which are defined with respect to the predefined noise-control zone, to receive a plurality of residual-noise inputs representing acoustic residual-noise at a plurality of predefined residual-noise sensing locations, which are located within the predefined noise-control zone, to determine a noise control pattern, based on the plurality of noise inputs and the plurality of residual-noise inputs, and to output the noise control pattern to at least one acoustic transducer. 
     In some demonstrative embodiments, the controller may include an extractor to extract from the plurality of noise inputs a plurality of disjoint reference acoustic patterns, which are statistically independent, wherein the controller may determine the noise control pattern based on at least one disjoint reference acoustic pattern of the plurality of disjoint reference acoustic patterns. 
     In some demonstrative embodiments, the controller may select the at least one disjoint reference acoustic pattern from the plurality of disjoint reference acoustic patterns based on one or more predefined acoustic pattern attributes of at least one predefined noise pattern to be controlled within the noise-control zone. 
     In some demonstrative embodiments, the acoustic pattern attributes comprise at least one attribute selected from the group consisting of amplitude, energy, phase, frequency, direction, and statistical properties. 
     In some demonstrative embodiments, the controller may extract the plurality of disjoint reference acoustic patterns by applying a predefined extraction function to the plurality of noise inputs. 
     In some demonstrative embodiments, the controller is to determine the noise control pattern to reduce at least one noise parameter within the noise-control zone, the noise parameter including at least one parameter selected from the group consisting of energy and amplitude. 
     In some demonstrative embodiments, the controller is to determine the noise control pattern to selectively reduce one or more predefined first noise patterns within the noise-control zone, while not reducing one or more second noise patterns within the noise-control zone. 
     In some demonstrative embodiments, the noise-control zone is located within an interior of a vehicle, wherein the one or more first noise patterns include at least one pattern selected from the group consisting of a road noise pattern, a wind noise pattern, and an engine noise pattern, and wherein the one or more first noise patterns include at least one pattern selected from the group consisting of an audio noise pattern of an audio device located within the vehicle, a horn noise pattern, and a siren noise pattern Or any other functional/hazard signals. 
     In some demonstrative embodiments, the controller is to determine the noise control pattern without having information relating to one or more noise-source attributes of one or more actual noise sources generating the acoustic noise at the plurality of predefined noise sensing locations. 
     In some demonstrative embodiments, the noise-source attributes include at least one attribute selected from the group consisting of a number of the noise sources, a location of the noise sources, a type of the noise sources, and one or more attributes of one or more noise patterns generated by one or more of the noise sources. 
     In some demonstrative embodiments, the noise sensing locations are distributed on an enclosure surrounding the noise-control zone. 
     In some demonstrative embodiments, the system includes one or more first acoustic sensors to sense the acoustic noise at one or more of the plurality of noise sensing locations; and one or more second acoustic sensors to sense the acoustic residual-noise at one or more of the plurality of residual-noise sensing locations. 
     In some demonstrative embodiments, a method of active noise control may include determining acoustic noise at a plurality of predefined noise sensing locations, which are defined with respect to a predefined noise-control zone; determining acoustic residual-noise at a plurality of predefined residual-noise sensing locations, which are located within the predefined noise-control zone; determining a noise control pattern to control the acoustic noise within the noise-control zone, based on the acoustic noise at the plurality of predefined noise sensing locations and the acoustic residual-noise at the plurality of predefined residual-noise sensing locations; and outputting the control pattern to at least one acoustic transducer. 
     In some demonstrative embodiments, a method of noise control may include determining a noise control pattern to control acoustic noise within a predefined noise-control zone, based on acoustic noise at a plurality of predefined noise sensing locations, which are defined with respect to the predefined noise-control zone, and acoustic residual-noise at a plurality of predefined residual-noise sensing locations, which are located within the predefined noise-control zone; and outputting the control pattern to at least one acoustic transducer. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity of presentation. Furthermore, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. The figures are listed below. 
         FIG. 1  is a schematic block diagram illustration of an Active Noise Control (ANC) system, in accordance with some demonstrative embodiments. 
         FIG. 2  is a schematic illustration of a deployment scheme of components of the ANC system of  FIG. 1 , in accordance with some demonstrative embodiments. 
         FIG. 3  is a schematic block diagram illustration of a controller, in accordance with some demonstrative embodiments. 
         FIG. 4  is a schematic block diagram illustration of an extractor, in accordance with some demonstrative embodiments. 
         FIG. 5  is a schematic block diagram illustration of a multi-input-multi-output prediction unit, in accordance with some demonstrative embodiments. 
         FIG. 6  is a schematic block diagram illustration of a controller including a noise pattern selector, in accordance with some demonstrative embodiments. 
         FIG. 7  is a conceptual illustration of a headrest ANC system, in accordance with some demonstrative embodiments. 
         FIG. 8  is a schematic flow-chart illustration of a method of noise control, in accordance with some demonstrative embodiments. 
         FIG. 9  is a schematic block diagram illustration of an article of manufacture, in accordance with some demonstrative embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of some embodiments. However, it will be understood by persons of ordinary skill in the art that some embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, units and/or circuits have not been described in detail so as not to obscure the discussion. 
     Discussions herein utilizing terms such as, for example, “processing”, “computing”, “calculating”, “determining”, “establishing”, “analyzing”, “checking”, or the like, may refer to operation(s) and/or process(es) of a computer, a computing platform, a computing system, or other electronic computing device, that manipulate and/or transform data represented as physical (e.g., electronic) quantities within the computer&#39;s registers and/or memories into other data similarly represented as physical quantities within the computer&#39;s registers and/or memories or other information storage medium that may store instructions to perform operations and/or processes. 
     The terms “plurality” and “a plurality” as used herein include, for example, “multiple” or “two or more”. For example, “a plurality of items” includes two or more items. 
     Some portions of the following detailed description are presented in terms of algorithms and symbolic representations of operations on data bits or binary digital signals within a computer memory. These algorithmic descriptions and representations may be the techniques used by those skilled in the data processing arts to convey the substance of their work to others skilled in the art. 
     An algorithm is here, and generally, considered to be a self-consistent sequence of acts or operations leading to a desired result. These include physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers or the like. It should be understood, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. 
     Some demonstrative embodiments include systems and methods, which may be efficiently implemented for controlling noise, for example, reducing or eliminating undesirable noise, e.g., at least noise of generally low frequencies, as described below. 
     Some demonstrative embodiments may include methods and/or systems of Active Noise Control (ANC) configured to control, reduce and/or eliminate the noise energy and/or wave amplitude of one or more acoustic patterns (“primary patterns”) produced by one or more noise sources, which may include known and/or unknown noise sources. 
     In some demonstrative embodiments, an ANC system may be configured to produce a noise control pattern (“secondary pattern”), e.g., including a destructive noise pattern, which may be based on one or more of the primary patterns, for example, such that a controlled noise zone, for example, a reduced noise zone, e.g., a quiet zone, may be created by a combination of the secondary and primary patterns. 
     In some demonstrative embodiments, the ANC system may be configured to control, reduce and/or eliminate noise within a predefined location, area or zone (“the noise-control zone”, also referred to as the “quiet zone” or “Quiet Bubble™”), without, for example, regardless of and/or without using a-priori information regarding the primary patterns and/or the one or more noise sources. 
     For example, the ANC system may be configured to control, reduce and/or eliminate noise within the noise control zone, e.g., independent of, regardless of and/or without knowing in advance one or more attributes of one or more of the noise sources and/or one or more of the primary patterns, for example, the number, type, location and/or other attributes of one or more of the primary patterns and/or one or more of the noise sources. 
     Some demonstrative embodiments are described herein with respect to ANC systems and/or methods configured to reduce and/or eliminate the noise energy and/or wave amplitude of one or more acoustic patterns within a quiet zone. 
     However, in other embodiments the ANC systems and/or methods may be configured to control in any other manner the noise energy and/or wave amplitude of one or more acoustic patterns within the noise control zone, for example, to affect, alter and/or modify the noise energy and/or wave amplitude of one or more acoustic patterns within a predefined zone. 
     In one example, the ANC systems and/or methods may be configured to selectively reduce and/or eliminate the noise energy and/or wave amplitude of one or more types of acoustic patterns within the noise control zone and/or to selectively increase and/or amplify the noise energy and/or wave amplitude of one or more other types of acoustic patterns within the noise control zone; and/or to selectively maintain and/or preserve the noise energy and/or wave amplitude of one or more other types of acoustic patterns within the noise control zone. 
     In some demonstrative embodiments, the ANC system may be configured to control reduce and/or eliminate the noise energy and/or wave amplitude of one or more of the primary patterns within the quite zone. 
     In some demonstrative embodiments, the ANC system may be configured to control, reduce and/or eliminate noise within the noise control zone in a selective and/or configurable manner, e.g., based on one or more predefined noise pattern attributes, such that, for example, the noise energy, wave amplitude, phase, frequency, direction and/or statistical properties of one or more first primary patterns may be affected by the secondary pattern, while the secondary pattern may have a reduced effect or even no effect on the noise energy, wave amplitude, phase, frequency, direction and/or statistical properties of one or more second primary patterns, e.g., as described below. 
     In some demonstrative embodiments, the ANC system may be configured to control, reduce and/or eliminate the noise energy and/or wave amplitude of the primary patterns on a predefined envelope or enclosure surrounding and/or enclosing the noise control zone. 
     In one example, the noise control zone may include a two-dimensional zone, e.g., defining an area in which the noise energy and/or wave amplitude of one or more of the primary patterns is to be controlled, reduced and/or eliminated. 
     According to this example, the ANC system may be configured to control, reduce and/or eliminate the noise energy and/or wave amplitude of the primary patterns along a perimeter surrounding the noise control zone. 
     In one example, the noise control zone may include a three-dimensional zone, e.g., defining a volume in which the noise energy and/or wave amplitude of one or more of the primary patterns is to be controlled, reduced and/or eliminated. According to this example, the ANC system may be configured to control, reduce and/or eliminate the noise energy and/or wave amplitude of the primary patterns on a surface enclosing the three-dimensional volume. 
     In one example, the noise control zone may include a spherical volume and the ANC system may be configured to control, reduce and/or eliminate the noise energy and/or wave amplitude of the primary patterns on a surface of the spherical volume. 
     In another example, the noise control zone may include a cubical volume and the ANC system may be configured to control, reduce and/or eliminate the noise energy and/or wave amplitude of the primary patterns on a surface of the cubical volume. 
     In other embodiments, the noise control zone may include any other suitable volume, which may be defined, for example, based on one or more attributes of a location at which the noise control zone is to be maintained. 
     Reference is now made to  FIG. 1 , which schematically illustrates an ANC system  100 , in accordance with some demonstrative embodiments. Reference is also made to  FIG. 2 , which schematically illustrates of a deployment scheme  200  of components of ANC system  100 , in accordance with some demonstrative embodiments. 
     In some demonstrative embodiments, ANC system  100  may include a controller  102  to control noise within a predefined noise-control zone  110 , e.g., as described in detail below. 
     In some demonstrative embodiments, noise control zone  110  may include a three-dimensional zone. For example, noise control zone  110  may include a spherical zone. 
     In some demonstrative embodiments, controller  102  may be configured to receive a plurality of noise inputs  104  representing acoustic noise at a plurality of predefined noise sensing locations  105 , which are defined with respect to noise-control zone  110 . 
     In some demonstrative embodiments, controller  102  may receive noise inputs  104  from one or more acoustic sensors, e.g., microphones, accelerometers, tachometers and the like, located at one or more of locations  105 , and/or from one or more virtual sensors configured to estimate the acoustic noise at one or more of locations  105 , e.g., as described in detail below. 
     In some demonstrative embodiments, controller  102  may be configured to receive a plurality of residual-noise inputs  106  representing acoustic residual-noise at a plurality of predefined residual-noise sensing locations  107 , which are located within noise-control zone  110 . 
     In some demonstrative embodiments, controller  102  may receive residual-noise inputs  106  from one or more acoustic sensors, e.g., microphones, accelerometers tachometers and the like, located at one or more of locations  107 , and/or from one or more virtual sensors configured to estimate the residual-noise at one or more of locations  107 , e.g., as described in detail below. 
     In some demonstrative embodiments, ANC  100  may include at least one acoustic transducer  108 , e.g., a speaker. Controller  102  may control acoustic transducer  108  to generate an acoustic noise control pattern configured to control the noise within noise control zone  110 , e.g., as described in detail below. 
     In some demonstrative embodiments, controller  102  may be configured to determine a noise control signal  109 , based on noise inputs  104  and residual-noise inputs  106 , and to output noise control signal  109  to control acoustic transducer  108 , e.g., as described in detail below. 
     In some demonstrative embodiments, the at least one acoustic transducer  108  may include, for example, an array of one or more acoustic transducers, e.g., at least one suitable speaker, to produce the noise control pattern based on noise control signal  109 . 
     In some demonstrative embodiments, the at least one acoustic transducer  108  may be positioned at one or more locations, which may be determined based on one or more attributes of noise control zone  110 , e.g., a size and/or shape of zone  110 , one or more expected attributes inputs  104 , one or more expected attributes of one or more potential actual noise sources  202 , e.g., an expected location and/or directionality of noise sources  202  relative to noise control zone  110 , a number of noise sources  202 , and the like. 
     In one example, acoustic transducer  108  may include a speaker array including a predefined number, denoted M, of speakers or a multichannel acoustical source. For example, acoustic transducer  108  include speaker Part No. AI 4.0, available from Cerwin-Vega Inc., Chatsworth, Calif., and the like. 
     In some demonstrative embodiments, acoustic transducer  108  may include an array of speakers implemented using a suitable “compact acoustical source” positioned at a suitable location, e.g., external to zone  110 . In another example, the array of speakers may be implemented using a plurality of speakers distributed in space, e.g., around noise control zone  110 . 
     In some demonstrative embodiments, locations  105  may be distributed externally to noise control zone  110 . For example, one or more of locations  105  may be distributed on, or in proximity to, an envelope or enclosure surrounding noise control zone  110 . 
     For example, if noise control zone  110  is defined by a spherical volume, then one or more of locations  105  may be distributed on a surface of the spherical volume and/or external to the spherical volume. 
     In another example, one or more of locations  105  may be distributed in any combination of locations on and/or external to the spherical volume, e.g., one or more locations surrounding the spherical volume. 
     In some demonstrative embodiments, locations  107  may be distributed within noise control zone  110 , for example, in proximity to the envelope of noise control zone  110 . 
     For example, if quiet zone  110  is defined by a spherical volume, then locations  107  may be distributed on a spherical surface having a radius, which is lesser than a radius of noise control zone  110 . 
     In some demonstrative embodiments, ANC system  100  may include one or more first acoustic sensors (“primary sensors”) to sense the acoustic noise at one or more of the plurality of noise sensing locations  105 . 
     In some demonstrative embodiments, ANC system  100  may include one or more second acoustic sensors (“error sensors”) to sense the acoustic residual-noise at one or more of the plurality of residual-noise sensing locations  107 . 
     In some demonstrative embodiments, one or more of the error sensors and/or one or more of the primary sensors may be implemented using one or more “virtual sensors” (“virtual microphones”). A virtual microphone corresponding to a particular microphone location may be implemented by any suitable algorithm and/or method capable of evaluating an acoustic pattern, which would have be sensed by an actual acoustic sensor located at the particular microphone location. 
     In some demonstrative embodiments, controller  102  may be configured to simulate and/or perform the functionality of the virtual microphone, e.g., by estimating and/or evaluating the acoustic noise pattern at the particular location of the virtual microphone. 
     In some demonstrative embodiments, ANC system  100  may include a first array  219  of one or more primary sensors, e.g., microphones, accelerometers, tachometers and the like, configured to sense the primary patterns at one or more of locations  105 . For example, the primary sensors may include one or more sensors to sense the primary patterns on a spherical surface defining a spherical noise control zone  110 . 
     For example, array  219  may include microphone Part No. ECM6AP, available from ARIO Electronics Co. Ltd., Taoyuan, Taiwan. The microphone may output a noise signal  104  including, for example, a sequence of N samples per second. For example, N may be 41100 samples per second, e.g., if the microphone operates at a sampling rate of about 44.1 KHz. The noise signal  104  may include any other suitable signal having any other suitable sampling rate and/or any other suitable attributes. 
     In some demonstrative embodiments, one or more of the sensors of array  219  may be implemented using one or more “virtual sensors”. For example, array  219  may be implemented by a combination of at least one microphone and at least one virtual microphone. A virtual microphone corresponding to a particular microphone location of locations  105  may be implemented by any suitable algorithm and/or method, e.g., as part of controller  102  or any other element of system  100 , capable of evaluating an acoustic pattern, which would have be sensed by an acoustic sensor located at the particular microphone location. For example, controller  102  may be configured to evaluate the acoustic pattern of the virtual microphone based on at least one actual acoustic pattern sensed by the at least one microphone of array  219 . 
     In some demonstrative embodiments, ANC system  100  may include a second array  221  of one or more error sensors, e.g., microphones, configured to sense the acoustic residual-noise at one or more of locations  107 . For example, the error sensors may include one or more sensors to sense the acoustic residual-noise patterns on a spherical surface within spherical noise control zone  110 . 
     In some demonstrative embodiments, one or more of the sensors of array  221  may be implemented using one or more “virtual sensors”. For example, array  221  may include a combination of at least one microphone and at least one virtual microphone. A virtual microphone corresponding to a particular microphone location of locations  107  may be implemented by any suitable algorithm and/or method, e.g., as part of controller  102  or any other element of system  100 , capable of evaluating an acoustic pattern, which would have be sensed by an acoustic sensor located at the particular microphone location. For example, controller  102  may be configured to evaluate the acoustic pattern of the virtual microphone based on at least one actual acoustic pattern sensed by the at least one microphone of array  221 . 
     In some demonstrative embodiments, the number, location and/or distribution of the locations  105  and/or  107 , and/or the number, location and/or distribution of one or more acoustic sensors at one or more of locations  105  and  107  may be determined based on a size of noise control zone  110  or of an envelope of noise control zone  110 , a shape of noise control zone  110  or of the envelope of noise control zone  110 , one or more attributes of the acoustic sensors to be located at one or more of locations  105  and/or  107 , e.g., a sampling rate of the sensors, and the like. 
     In one example, one or more acoustic sensors, e.g., microphones, accelerometers, tachometers and the like, may be deployed at locations  105  and/or  107  according to the Spatial Sampling Theorem, e.g., as defined below by Equation 1. 
     For example, a number of the primary sensors, a distance between the primary sensors, a number of the error sensors and/or a distance between the error sensors may be determined in accordance with the Spatial Sampling Theorem, e.g., as defined below by Equation 1. 
     In one example, the primary sensors and/or the error sensors may be distributed, e.g., equally distributed, with a distance, denoted d, from one another. For example, the distance d may be determined as follows: 
                   d   ≤     c     2   ·   f               (   1   )               
wherein c denotes the speed of sound and f max  denotes a maximal frequency at which noise control is desired.
 
     For example, in case the maximal frequency of interest is f max =100 [Hz], the distance d may be determined as 
     
       
         
           
             
               d 
               ≤ 
               
                 343 
                 
                   2 
                   · 
                   100 
                 
               
             
             = 
             
               
                 1.7 
                 ⁡ 
                 
                   [ 
                   m 
                   ] 
                 
               
               . 
             
           
         
       
     
     As shown in  FIG. 2  deployment scheme  200  is configured with respect to a circular or spherical noise control zone  110 . For example, locations  105  are distributed, e.g., substantially evenly distributed, in a spherical or circular manner around noise control zone  110 , and locations  107  are distributed, e.g., substantially evenly distributed, in a spherical or circular manner within noise control zone  110 . 
     However in other embodiments, components of ANC system  100  may be deployed according to any other deployment scheme including any suitable distribution of locations  105  and/or  107 , e.g., configured with respect a noise control zone of any other suitable form and/or shape. 
     In some demonstrative embodiments, controller  102  may be configured to determine the noise control pattern to be reduced according to at least one noise parameter, e.g., energy, amplitude, phase, frequency, direction, and/or statistical properties within noise-control zone  110 , e.g., as described in detail below. 
     In some demonstrative embodiments, controller  102  may determine the noise control pattern to selectively reduce one or more predefined first noise patterns within noise-control zone  110 , while not reducing one or more second noise patterns within noise-control zone  110 , e.g., as described below. 
     In one demonstrative embodiment, noise-control zone  110  may be located within an interior of a vehicle, and controller  102  may determine the noise control pattern to selectively reduce one or more first noise patterns, e.g., including a road noise pattern, a wind noise pattern, and/or an engine noise pattern, while not reducing one or more second noise patterns, e.g., including an audio noise pattern of an audio device located within the vehicle, a horn noise pattern, a siren noise pattern, a hazard noise pattern of a hazard, an alarm noise pattern of an alarm signal, a noise pattern of an informational signal, and the like. 
     In some demonstrative embodiments, controller  102  may determine the noise control pattern without having information relating to one or more noise-source attributes of one or more of actual noise sources  202  generating the acoustic noise at the noise sensing locations  105 . 
     For example, the noise-source attributes may include a number of noise sources  202 , a location of noise sources  202 , a type of noise sources  202  and/or one or more attributes of one or more noise patterns generated by one or more of noise sources  202 . 
     In some demonstrative embodiments, controller  102  may be configured to extract from the plurality of noise inputs  104  a plurality of disjoint reference acoustic patterns, which are statistically independent. 
     For example, controller  102  may include an extractor to extract the plurality of disjoint reference acoustic patterns, e.g., as described below with reference to  FIG. 4 . 
     The phrase “disjoint acoustic patterns” as used herein may refer to a plurality of acoustic patterns, which are independent with respect to at least one feature and/or attribute, e.g., energy, amplitude, phase, frequency, direction, one or more statistical signal properties, and the like. 
     In some demonstrative embodiments, controller  102  may extract the plurality of disjoint reference acoustic patterns by applying a predefined extraction function to the plurality of noise inputs  104 , e.g., as described below with reference to  FIG. 4 . 
     In some demonstrative embodiments, the extraction of the disjoint acoustic patterns may be used, for example, to model the primary pattern of inputs  104  as a combination of the predefined number of disjoint acoustic patterns, e.g., corresponding to a respective number of disjoint modeled acoustic sources. 
     This modeling may be useful, for example, in order to increase an efficiency, e.g., a computational efficiency, reduce a complexity, e.g., a mathematical and/or computational complexity, which may result from processing the primary pattern, without, having, for example, a-priori information regarding the primary pattern and/or the one or more actual noise sources  202 . 
     Additionally or alternatively, the extraction of the disjoint acoustic patterns may enable selectively controlling noise within noise control zone  110 , e.g., according to one or more predefined noise attributes and/or types, e.g., as described below. 
     In some demonstrative embodiments, controller  102  may determine the noise control signal  109  for generating the noise control pattern based on at least one disjoint reference acoustic pattern of the plurality of disjoint reference acoustic patterns. 
     In some demonstrative embodiments, controller  102  may select the at least one disjoint reference acoustic pattern (“the selected reference acoustic pattern”) from the plurality of disjoint reference acoustic patterns based, for example, on one or more predefined acoustic pattern attributes of at least one predefined noise pattern to be controlled within noise-control zone  110 . 
     In some demonstrative embodiments, the acoustic pattern attributes may include an amplitude, energy, phase, frequency, direction, and/or one or more statistical signal properties of the predefined noise pattern. 
     In some demonstrative embodiments, the predefined acoustic pattern attributes may relate to expected and/or estimated attributes of an expected noise pattern to be affecting noise control zone  110 . 
     In one example, ANC system  100  may be implemented in an environment, e.g., a room, in which a user may want to hear acoustic signals of a first type, e.g., a television located within the room, while reducing and/or eliminating acoustic signals of a second type, e.g., noise from outside of the room. According to this example, controller  102  may be configured to model inputs  104  into first and second disjoint acoustic patterns corresponding to the first and second types of acoustic signals, respectively. Controller  102  may then generate noise control signal  109  to selectively reduce and/or cancel the acoustic signals of the second type, e.g., while substantially not affecting the acoustic signals of the first type. 
     In another example, if ANC system  100  is deployed within the interior of a vehicle, it may be expected that one or more expected noise patterns, which are expected to affect noise-control zone  110 , may be generated by one or more of road noise, wind noise, engine noise and the like. Accordingly, controller  102  may be configured to select one or more reference acoustic patterns based on one or more attributes of the road noise pattern, the wind noise pattern, and/or the engine noise pattern. 
     Reference is now made to  FIG. 3 , which schematically illustrates a controller  300 , in accordance with some demonstrative embodiments. In some embodiments, controller  300  may perform, for example, the functionality of controller  102  ( FIG. 1 ). 
     In some demonstrative embodiments, controller  300  may receive a plurality of inputs  304 , e.g., including inputs  104  ( FIG. 1 ), representing acoustic noise at a plurality of predefined noise sensing locations, e.g., locations  105  ( FIG. 2 ), which are defined with respect to a noise-control zone, e.g., noise control zone  110  ( FIG. 2 ). Controller  300  may generate a noise control signal  312  to control at least one acoustic transducer  314 , e.g., acoustic transducer  108  ( FIG. 1 ). 
     In some demonstrative embodiments, controller  300  may include an estimator (“prediction unit”)  310  to estimate noise signal  312  by applying an estimation function to an input  308  corresponding to inputs  304 . 
     In some demonstrative embodiments, e.g., as shown in  FIG. 3 , controller may include an extractor  306  to extract a plurality of disjoint reference acoustic patterns from inputs  304 , e.g., as described below. According to these embodiments, input  308  may include the plurality of disjoint reference acoustic patterns. 
     In some demonstrative embodiments, controller  300  may use the extraction of the disjoint acoustic patterns to model the noise represented by inputs  304  as a combination of a predefined number of disjoint modeled acoustic sources generating the predefined number of disjoint acoustic patterns, respectively. This modeling may be useful, for example, in order to increase an efficiency, e.g., a computational efficiency, reduce a complexity, e.g., a mathematical and/or computational complexity, of controller  300 , which may result, for example, from processing inputs  304 , without, having, for example, a-priori information regarding attributes of inputs  304  and/or attributes of one or more noise sources generating and/or affecting inputs  304 . 
     Additionally or alternatively, controller  300  may utilize the disjoint acoustic patterns  308  to reduce and/or eliminate noise within the noise control zone  110  ( FIG. 2 ) in a selective and/or configurable manner, e.g., based on one or more predefined noise pattern attributes. 
     For example, controller  300  may be configured to generate noise control signal  312  based on the disjoint acoustic patterns such that, for example, the noise control signal  312  may affect the noise energy and/or wave amplitude of one or more first primary patterns in a first manner, while the noise energy and/or wave amplitude of one or more second primary patterns may be affected in a second, different manner. 
     In one example, controller  300  may generate noise control signal  312  configured to reduce and/or eliminate the noise energy and/or wave amplitude of the first primary patterns within the noise control zone, while the noise energy and/or wave amplitude of the first primary patterns may not be affected within the noise control zone. 
     In some demonstrative embodiments, extractor  306  may be configured to extract noise patterns related to one or more “unwanted” noise sources and/or patterns, which may be predefined based on any suitable attributes. Controller  300  may generate noise control signal  312  such that, for example, only a specific portion of the unwanted noise will be destructed by the pattern produced by the transducer  314 . 
     Reference is now made to  FIG. 4 , which schematically illustrates an extractor  400 , in accordance with some demonstrative embodiments. In some demonstrative embodiments, extractor  400  may perform the functionality of extractor  306  ( FIG. 3 ). 
     In some demonstrative embodiments, extractor  400  may receive a plurality of inputs  408 , e.g., including inputs  104  ( FIG. 1 ), representing acoustic noise at a plurality of predefined noise sensing locations, e.g., locations  105  ( FIG. 2 ), which are defined with respect to a noise-control zone, e.g., noise control zone  110  ( FIG. 2 ). Extractor  400  may extract from inputs  408  a plurality of disjoint reference acoustic patterns  410 , e.g., as described in detail below. 
     In some demonstrative embodiments, extractor  400  may apply an extraction algorithm  402  to inputs  408 . 
     In some demonstrative embodiments, extraction algorithm  402  may represent, for example, noise sources disaggregated by a suitable statistical approach, e.g., Independent Component Analysis (ICA) also known in the art as Blind Source Separation (BSS), and the like. 
     In some demonstrative embodiments, extractor  400  may include an adaptation algorithm  404  to adapt one or more parameters of extraction algorithm  402 , e.g., based on at least one predetermined criterion. For example, adaptation algorithm  404  may be able to minimize, a statistical dependence between disjoint reference acoustic patterns  410 , e.g., Mutual Information (MI), as discussed below. 
     In some demonstrative embodiments, the plurality of inputs  408  may include a predefined number, denoted K′, of inputs corresponding to a respective plurality of K′ noise sensing locations, e.g., locations  105  ( FIG. 2 ). 
     In some demonstrative embodiments, extraction algorithm  402  may generate disjoint reference acoustic patterns  410  including a predefined number, denoted K, of disjoint reference acoustic patterns  410 . 
     In some demonstrative embodiments, extraction algorithm  402  may determine the K disjoint reference acoustic patterns  410  corresponding to a current sample of the noise at the K′ noise sensing locations. 
     In some demonstrative embodiments, extraction algorithm  402  may determine the K disjoint reference acoustic patterns  410  corresponding to the current sample, based on the current sample of the noise at the K′ noise sensing locations, and taking into account one or more successive previous samples of the noise at the K′ noise sensing locations, e.g., a predefined number, denoted I, of the noise at the K′ noise sensing locations. 
     For example, inputs  408  corresponding to an n-th sample, may be represented by a matrix, denoted X[n], which includes the n-th sample of the noise at the K′ noise sensing locations, and I successive previous samples of the noise at the K′ noise sensing locations. For example, inputs  408  may be represented as follows: 
     
       
         
           
             
               
                 
                   
                     X 
                     ⁡ 
                     
                       [ 
                       n 
                       ] 
                     
                   
                   = 
                   
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                               ] 
                             
                           
                         
                       
                     
                     ) 
                   
                 
               
               
                 
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                   2 
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     In some demonstrative embodiments, extraction algorithm  402  may generate disjoint reference acoustic patterns  410 , by applying an extraction function to the inputs  408 , e.g. as follows:
 
 Ŝ[n]=F   −1 ( X[n ])  (3)
 
wherein F −1  denotes the extraction function, and wherein Ŝ[n] denotes a vector of the K disjoint reference acoustic patterns  410  corresponding to the n-th sample. For example, the vector Ŝ[n] may be represented as follows:
 
     
       
         
           
             
               
                 
                   
                     S 
                     ⁡ 
                     
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                       n 
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                   = 
                   
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                     ) 
                   
                 
               
               
                 
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                   4 
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     In some demonstrative embodiments, the function F −1  may include a memory-less function, e.g., with respect to previous samples, or a function having an element of memory. 
     For example, the vector Ŝ[n] may be represented as follows, e.g., using a memoryless function: 
     
       
         
           
             
               
                 
                   
                     
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     The vector Ŝ[n] may be represented, for example, as follows, e.g., using a function with memory: 
     
       
         
           
             
               
                 
                   
                     
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     In some demonstrative embodiments, the function F −1  may include a linear function, e.g., such that each of the elements of the vector S is a linear combination of elements of the matrix X, or a non-linear function. 
     For example, an i-th element of the vector Ŝ[n] may be determined, e.g., as follows: 
     
       
         
           
             
               
                 
                   
                     
                       s 
                       i 
                     
                     ⁡ 
                     
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                       n 
                       ] 
                     
                   
                   = 
                   
                     
                       b 
                       i 
                     
                     + 
                     
                       
                         ∑ 
                         
                           k 
                           = 
                           1 
                         
                         K 
                       
                       ⁢ 
                       
                         
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                             i 
                             , 
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                         · 
                         
                           x 
                           k 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     In some demonstrative embodiments, the function F −1  may be defined based on one or more predefined required attributes of the K disjoint reference acoustic patterns  410 , e.g., based on the one or more predefined noise pattern attributes to be controlled within the noise control zone, as described above. 
     In some demonstrative embodiments, the function F −1  may include, for example, a linear mapping function with memory. For example, the operation F −1 (•) may denote an operation of convolution, e.g., such that the vector Ŝ[n] may be determined according to Equation 3 by convolving the function F −1  with the matrix X[n]. 
     For example, the vector Ŝ[n] may be determined by transforming Equation 3 to a Z-domain, e.g., as follows:
 
{circumflex over ( S )}( z )= B ( z )· X ( z )  (8)
 
wherein B(z) denotes a separation matrix.
 
     For example, extraction algorithm  402  may determine the vector Ŝ(z) in the z-domain based on a contrast function, denoted φ[Ŝ(z)]. For example, the contrast function φ[Ŝ(z)] may be defined as a Mutual Information (MI) between the outputs Ŝ(z) of extraction algorithm  402 , e.g., as follows: 
                     ϕ   ⁡     [       S   ^     ⁡     (   Z   )       ]       =       I   ⁡     (         s   ^     1     ,   …   ⁢           ,       s   ^     k       )       =         ∑     k   =   1     K     ⁢     H   ⁡     (       s   ^     k     )         -     H   ⁡     (     S   ^     )                   (   9   )               
wherein I denotes an information function, and H denotes Shannon&#39;s Entropy. The information function I(X,Y) corresponding to two variables X, Y may be defined, for example, as follows:
 
                     I   ⁡     (     x   ,   y     )       =       ∑     y   ∈   Y       ⁢       ∑     x   ∈   X       ⁢       p   ⁡     (     x   ,   y     )       ⁢     log   ⁡     (       p   ⁡     (     x   ,   y     )           p   ⁡     (   x   )       ⁢     p   ⁡     (   y   )           )                     (   10   )               
where p(x,y) denotes a joint probability distribution function of X and Y, and p(x) and p(y) denote the marginal probability distribution functions of X and Y, respectively.
 
     For example, extractor  400  may include a contrast function estimator  406  to estimate the contrast function φ[Ŝ(z)] based on the output of extractor  402 , e.g., in accordance with Equation 9. The contrast function φ[Ŝ(z)] may reach a minimum, for example, when extraction/separation is achieved, for example, since the separation process may be a minimization of mutual information (contrast function) between the outputs of a separation unit. For example, adaptation algorithm  404  may adapt the function F −1  by detecting the minimum of the function φ[Ŝ(z)]. 
     In one example, the separation matrix B(z) may be determined using a natural gradient iterative algorithm, e.g., as follows: 
                       B   n     ⁡     (   z   )       =       (     I   -     μ   ⁢     ∂     ∂       B   n     ⁡     (   z   )           ⁢     ϕ   ⁡     [       S   ^     ⁡     (   z   )       ]           )     ⁢       B   n     ⁡     (   z   )                 (   11   )               
wherein μ denotes a learning rate, e.g., an iteration step.
 
     Referring back to  FIG. 3 , in other embodiments, controller  300  may not include extractor  306 . Accordingly, input  308  may include inputs  304  and/or any other input based on inputs  304 . 
     In some demonstrative embodiments, estimator  310  may apply any suitable linear and/or non-linear function to input  308 . For example, the estimation function may include a non-linear estimation function, e.g., a radial basis function. 
     In some demonstrative embodiments, estimator  310  may be able to adapt one or more parameters of the estimation function based on a plurality of residual-noise inputs  316  representing acoustic residual-noise at a plurality of predefined residual-noise sensing locations, which are located within the noise-control zone. For example, inputs  316  may include inputs  106  ( FIG. 1 ) representing acoustic residual-noise at residual-noise sensing locations  107  ( FIG. 2 ), which are located within noise-control zone  110  ( FIG. 2 ). 
     In some demonstrative embodiments, one or more of inputs  316  may include at least one virtual microphone input corresponding to a residual noise (“noise error”) sensed by at least one virtual error sensor at at least one particular residual-noise sensor location of locations  107  ( FIG. 2 ). For example, controller  300  may evaluate the noise error at the particular residual-noise sensor location based on inputs  308  and the predicted noise signal  312 , e.g., as described below. 
     In one example, controller  300  may utilize a speaker transfer function to produce an estimation of a noise control pattern generated by transducer  314 , e.g., by applying the speaker transfer function to predicted noise signal  312 . Controller  300  may also utilize a modulation transfer function to produce an estimation of the noise pattern at the particular residual-noise sensor location, e.g., by applying the modulation transfer function to the noise signal represented by input  308 . Controller  300  may determine the estimated residual noise at the particular residual-noise sensor location, for example, by subtracting the estimation of the noise control pattern from the estimation of the noise pattern. 
     In some demonstrative embodiments, controller  300  may estimate a sample (“the succeeding sample”) of the noise pattern succeeding a current sample of the noise pattern, for example, based on the current sample and/or one or more previous samples of the noise pattern. Controller  300  may provide noise control signal  312 , such that transducer  312  may produce the noise control pattern based on the estimated succeeding sample, e.g., such that the noise control pattern may reach the particular residual-noise sensor location substantially at the same time the noise pattern reaches the same particular residual-noise sensor location. 
     In some demonstrative embodiments, estimator  310  may include a multi-input-multi-output (MIMO) prediction unit configured, for example, to generate a plurality of noise control patterns corresponding to the n-th sample, e.g., including M control patterns, denoted y 1 (n) . . . y M (n), to drive a plurality of M respective acoustic transducers, e.g., based on the inputs  308 . 
     Reference is now made to  FIG. 5 , which schematically illustrates a MIMO prediction unit  500 , in accordance with some demonstrative embodiments. In some demonstrative embodiments, MIMO prediction unit  500  may perform the functionality of estimator  310  ( FIG. 3 ). 
     As shown in  FIG. 4 , prediction unit  500  may be configured to receive an input  502  including the vector Ŝ[n], e.g., as output from extractor  306  ( FIG. 3 ), and to drive loudspeaker array  502  including M acoustic transducers. For example, prediction unit  500  may generate a controller output  501  including the M noise control patterns y 1 (n) . . . y M (n), to drive a plurality of M respective acoustic transducers, e.g., based on the inputs  308 . 
     In some demonstrative embodiments, interference (cross-talk) between two or more of the M acoustic transducers of array  502  may occur, for example, when two or more, e.g., all of, the M acoustic transducers generate the control noise pattern, e.g., simultaneously. 
     In some demonstrative embodiments, prediction unit  500  may generate output  501  configured to control array  502  to generate a substantially optimal noise control pattern, e.g., while simultaneously optimizing the input signals to each speaker in array  502 . For example, prediction unit  500  may control the multi-channel speakers of array  502 , e.g., while cancelling the interface between the speakers. 
     In one example, prediction unit  500  may utilize a linear function with memory. For example, prediction unit  500  may determine a noise control pattern, denoted y m [n], corresponding to an m-th speaker of array  502  with respect to the n-th sample of the primary pattern, e.g., as follows: 
                       y   m     ⁡     [   n   ]       =       ∑     k   =   1     K     ⁢       ∑     i   =   1       I   -   1       ⁢         w     k   ⁢           ⁢   m       ⁡     [   i   ]       ⁢       s   k     ⁡     [     n   -   i     ]                     (   12   )               
wherein s k [n] denotes the k-th disjoint reference acoustic pattern, e.g., received from extractor  306  ( FIG. 3 ), and w km [i] denotes a prediction filter coefficient configured to drive the m-th speaker based on the k-th disjoint reference acoustic pattern, e.g., as described below.
 
     In another example, prediction unit  500  may implement any other suitable prediction algorithm, e.g., linear, or non-linear, having or not having memory, and the like, to determine the output  501 . 
     In some demonstrative embodiments, prediction unit  500  may optimize the prediction filter coefficients w km [i], for example, based on a plurality of a plurality of residual-noise inputs  504 , e.g., including a plurality of residual-noise inputs  316 . For example, prediction unit  500  may optimize the prediction filter coefficients w km [i] to achieve maximal destructive interference at the residual-error sensing locations  107  ( FIG. 2 ). For example, locations  107  may include L locations, and inputs  504  may include L residual noise components, denoted e 1 [n], e 2 [n], . . . , e L [n]. 
     In some demonstrative embodiments, prediction unit  500  may optimize the prediction filter coefficients w km [i] based, for example, on a minimum mean square error (MMSE) criterion, or any other suitable criteria. For example, a cost function, denoted J, for optimization prediction filter coefficients w km [i] may be defined, for example, as a total energy of the residual noise components e 1 [n], e 2 [n], . . . , e L [n] at locations  107  ( FIG. 2 ), e.g., as follows: 
     
       
         
           
             
               
                 
                   J 
                   = 
                   
                     E 
                     ⁢ 
                     
                       { 
                       
                         
                           ∑ 
                           
                             l 
                             = 
                             1 
                           
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                         ⁢ 
                         
                           
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                             2 
                           
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                             n 
                             ] 
                           
                         
                       
                       } 
                     
                   
                 
               
               
                 
                   ( 
                   13 
                   ) 
                 
               
             
           
         
       
     
     In some demonstrative embodiments, a residual noise pattern, denoted e l [n], at an l-th location may be expressed, for example, as follows: 
                       e   l     ⁡     [   n   ]       =           d   l     ⁡     [   n   ]       -       ∑     m   =   1     M     ⁢       ∑     j   =   0       J   -   1       ⁢         stf     l   ⁢           ⁢   m       ⁡     [   j   ]       ·       y   m     ⁡     [     n   -   j     ]               =         d   l     ⁡     [   n   ]       -       ∑     m   =   1     M     ⁢       ∑     j   =   0       J   -   1       ⁢         stf   lmj     ⁡     [   j   ]       ·       ∑     k   =   1     K     ⁢       ∑     i   =   1       I   -   1       ⁢         w     k   ⁢           ⁢   m       ⁡     [   i   ]       ⁢       s   k     ⁡     [     n   -   i     ]                               (   14   )               
wherein stf lm [j] denotes a path transfer function having J coefficients from the m-th speaker of the array  502  at a l-th location; and w km [n] denotes an adaptive weight vector of the prediction filter with I coefficients representing the relationship between the k-th reference acoustic pattern s k [n] and the control signal of the m-th speaker.
 
     In some demonstrative embodiments, prediction unit  500  may optimize the adaptive weights vector w km [n], e.g., to reach an optimal point, e.g., a maximal noise reduction. For example, prediction unit  500  may implement a gradient based adaption method, when at each step the weight vector w km [n] is updated in a negative direction of a gradient of the cost function J, e.g., as follows: 
     
       
         
           
             
               
                 
                   
                     
                       
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                   ( 
                   15 
                   ) 
                 
               
             
           
         
       
     
     Referring back to  FIG. 3 , in some demonstrative embodiments, controller  300   100  may be implemented to alter, modify and/or control one or more acoustic attributes within a noise control zone, e.g., zone  110  ( FIG. 2 ). Following are only some demonstrative implementations of controller  300 . 
     In some demonstrative embodiments, controller  300  may be implemented to reduce unwanted noise in a vehicle. In one example, a plurality of acoustic pattern “types” may be present within the vehicle, e.g., an audio system signal, a horn, a siren, road noise and/or wind noise, which may take place simultaneously. 
     Extractor  306  may be configured to disaggregate the different noise patterns and to provide to estimator  310  input  308  including only one or more of the unwanted noise patterns, e.g., the road noise pattern and/or the wind noise pattern. Accordingly, estimator  310  may control transducer  314  to generate a noise control pattern, which will affect other “wanted” noise patterns, e.g., the audio pattern, while reducing and/or eliminating the one or more unwanted noise patterns. 
     In some demonstrative embodiments, controller  300  may be implemented in a bedroom scenario, e.g., such that a snoring acoustic “pattern” may be disaggregated form an acoustic pattern of an alarm. Accordingly, controller  300  may allow reducing and/or eliminating the snoring acoustic patent, while not affecting the clock acoustic pattern. 
     In some demonstrative embodiments, controller  300  may be configured to select one or more of the noise patterns to be controlled by controller  300 . For example, as shown in  FIG. 6 , a controller  600  may include a selection unit  609 , e.g., between an extractor  606  and a prediction unit  610 . Selection unit  609  may selectively provide to prediction unit  610  only signals with special interest, e.g., based on a predefined criterion. For example, a possible criterion could be specific spectral or temporal behavior. For example, if the signal to be controlled does not overlap with the signal that is not to be controlled in a frequency domain, then selector  609  may separate the signals, e.g., by using a filter bank approach utilizing a plurality of filters to filter the frequencies of the signal to be controlled. 
       FIG. 7  illustrates front and back views of a conceptual deployment of a headrest ANC system  700  configured to maintain a Quiet Bubble  702  around a head of a user of headrest  700 , in accordance with some demonstrative embodiments. As shown in  FIG. 7 , headrest ANC system  700  may include two or more acoustic transducers, e.g., two transducers  704  positioned on both sides of the headrest, one or more reference sensors, e.g., three reference sensors  706  positioned outside the Quiet Bubble  702 , e.g., on a back of the headrest, and two or more residual-noise sensors, e.g., four sensors  708  positioned inside the Quiet Bubble  700 . 
       FIG. 8  is a schematic flow-chart illustration of a method of noise control, in accordance with some demonstrative embodiments. In some demonstrative embodiments, one or more operations of the method of  FIG. 8  may be performed by an ANC system e.g., system  100  ( FIG. 1 ), a controller, e.g., controller  300  ( FIG. 3 ) and/or any other component of an ANC system. 
     As indicated at block  800 , the method may include determining acoustic noise at a plurality of predefined noise sensing locations, which are defined with respect to a predefined noise-control zone. For example, controller  102  ( FIG. 1 ) may receive noise inputs  104  ( FIG. 1 ) corresponding to locations  105  ( FIG. 2 ) with respect to noise control zone  110  ( FIG. 2 ). For example, inputs  104  ( FIG. 1 ) may be determined based on inputs from one or more real and/or virtual noise sensors, e.g., as described above. 
     As indicated at block  802 , the method may include determining acoustic residual-noise at a plurality of predefined residual-noise sensing locations, which are located within the predefined noise-control zone. For example, controller  102  ( FIG. 1 ) may receive residual noise inputs  106  ( FIG. 1 ) corresponding to locations  107  ( FIG. 2 ) with respect to noise control zone  110  ( FIG. 2 ). For example, inputs  106  ( FIG. 1 ) may be determined based on inputs from one or more real and/or virtual noise sensors, e.g., as described above. 
     As indicated at block  804 , the method may include determining a noise control pattern to control the acoustic noise within the noise-control zone, based on the acoustic noise at the plurality of predefined noise sensing locations and the acoustic residual-noise at the plurality of predefined residual-noise sensing locations. For example, controller  102  ( FIG. 1 ) may determine noise control signal  109  ( FIG. 1 ), based on noise inputs  104  ( FIG. 1 ) and residual-noise inputs  106  ( FIG. 1 ), e.g., as described above. 
     As indicated at block  806 , the method may include outputting the noise control pattern to at least one acoustic transducer. For example, controller  102  ( FIG. 1 ) may output signal  109  to control acoustic transducer  108 , e.g., as described above. 
     As indicated at block  803 , the method may include extracting from the plurality of noise inputs a plurality of disjoint reference acoustic patterns, which are statistically independent with respect to at least one predefined attribute. For example, extractor  306  ( FIG. 3 ) may extract the plurality of disjoint reference acoustic patterns, e.g., as described above. For example, determining the noise control pattern may include determining the noise control pattern based on at least one disjoint reference acoustic pattern of the plurality of disjoint reference acoustic patterns, e.g., as described above. 
     Reference is made to  FIG. 9 , which schematically illustrates an article of manufacture  900 , in accordance with some demonstrative embodiments. Article  900  may include a non-transitory machine-readable storage medium  902  to store logic  904 , which may be used, for example, to perform at least part of the functionality of controller  102  ( FIG. 1 ) and/or to perform one or more operations of the method of  FIG. 8 . The phrase “non-transitory machine-readable medium” is directed to include all computer-readable media, with the sole exception being a transitory propagating signal. 
     In some demonstrative embodiments, article  900  and/or machine-readable storage medium  902  may include one or more types of computer-readable storage media capable of storing data, including volatile memory, non-volatile memory, removable or non-removable memory, erasable or non-erasable memory, writeable or re-writeable memory, and the like. For example, machine-readable storage medium  902  may include, RAM, DRAM, Double-Data-Rate DRAM (DDR-DRAM), SDRAM, static RAM (SRAM), ROM, programmable ROM (PROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), Compact Disk ROM (CD-ROM), Compact Disk Recordable (CD-R), Compact Disk Rewriteable (CD-RW), flash memory (e.g., NOR or NAND flash memory), content addressable memory (CAM), polymer memory, phase-change memory, ferroelectric memory, silicon-oxide-nitride-oxide-silicon (SONOS) memory, a disk, a floppy disk, a hard drive, an optical disk, a magnetic disk, a card, a magnetic card, an optical card, a tape, a cassette, and the like. The computer-readable storage media may include any suitable media involved with downloading or transferring a computer program from a remote computer to a requesting computer carried by data signals embodied in a carrier wave or other propagation medium through a communication link, e.g., a modem, radio or network connection. 
     In some demonstrative embodiments, logic  904  may include instructions, data, and/or code, which, if executed by a machine, may cause the machine to perform a method, process and/or operations as described herein. The machine may include, for example, any suitable processing platform, computing platform, computing device, processing device, computing system, processing system, computer, processor, or the like, and may be implemented using any suitable combination of hardware, software, firmware, and the like. 
     In some demonstrative embodiments, logic  904  may include, or may be implemented as, software, a software module, an application, a program, a subroutine, instructions, an instruction set, computing code, words, values, symbols, and the like. The instructions may include any suitable type of code, such as source code, compiled code, interpreted code, executable code, static code, dynamic code, and the like. The instructions may be implemented according to a predefined computer language, manner or syntax, for instructing a processor to perform a certain function. The instructions may be implemented using any suitable high-level, low-level, object-oriented, visual, compiled and/or interpreted programming language, such as C, C++, Java, BASIC, Matlab, Pascal, Visual BASIC, assembly language, machine code, and the like. 
     Functions, operations, components and/or features described herein with reference to one or more embodiments, may be combined with, or may be utilized in combination with, one or more other functions, operations, components and/or features described herein with reference to one or more other embodiments, or vice versa. 
     While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents may occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.