Patent Publication Number: US-9412373-B2

Title: Adaptive environmental context sample and update for comparing speech recognition

Description:
FIELD OF THE INVENTION 
     The present invention relates to active sensor circuits, and in particular, to active sensor circuits that operate on low power and at a low duty cycle. 
     BACKGROUND OF THE INVENTION 
     With recent advancements in semiconductor manufacturing and sensor technologies, low power sensor networks, particularly those operating wirelessly, are providing new capabilities for monitoring various environments and controlling various processes associated with or within such environments. Applications, both civil and military, include transportation, manufacturing, biomedical, environmental management, and safety and security systems. Further, voice or sound controlled applications may be coupled with mobile telephony or other personal electronic devices and systems, automotive control and entertainment system, etc. 
     Particularly for wireless sensor networks, low power operation is critical to allow for maximum flexibility and battery life and minimum form factor. It has been found that typical wireless sensor assemblies use upwards of 90% of their power merely on environmental or channel monitoring while waiting for an anticipated event(s) to occur. In other words, simply monitoring for the occurrence of an anticipated event requires the expenditure of nearly all available power. This is particularly true for acoustic sensors, which often require significant amounts of power to perform voice or sound recognition. 
     This problem has been addressed thus far by having a low power, or “sleep,” mode of operation in which the back end of the sensor assembly, e.g., the signal transmitter, or “radio,” circuitry, is effectively shut down pending receipt of a signal indicating the occurrence of the anticipated event, such as a change in the local environmental conditions, such as acoustic noise or temperature, for example. This can reduce power consumption of the sensor assembly to levels in the range of 10 to 50 percent of normal or full power operation. However, for a low duty cycle system where each sensor assembly may only spend a very small amount of time (e.g., 1%) performing data transmission, the power being consumed during such an idle period can still constitute a major portion of the overall power budget. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Particular embodiments in accordance with the invention will now be described, by way of example only, and with reference to the accompanying drawings: 
         FIG. 1  is an illustration of a device in which detection of a unique sound may be used to cause the system to perform a task or operation; 
         FIGS. 2A and 2B  are plots illustrating performance evaluation metrics for a sound recognition system; 
         FIG. 3  is a functional diagram illustrating a typical prior art sound recognition system; 
         FIG. 4  is a functional diagram illustrating analog-to-information (A2I) operation of a sound recognition system that operates on sparse information extracted directly from an analog input signal; 
         FIGS. 5 and 6  are detailed block diagrams of another A2I logic block; 
         FIG. 7  is a plot illustrating a region of interest that may be initiated by a trigger signal; 
         FIGS. 8A-8E, 9A-9D, and 10A-10C  illustrate examples of robust A2I sound feature extraction; 
         FIGS. 11A-11B and 12A-12B  illustrate two approaches for using A2I sparse sound features to recognize a spoken word or phrase; 
         FIGS. 13 and 14  are block diagrams of the system of  FIG. 4  interacting with a cloud based sound recognition training system; 
         FIGS. 15A and 15B  are block diagrams illustrating examples of interfacing a microphone to a backend system in conjunction with A2I detection results; 
         FIGS. 16A-16E  are flow diagrams illustrating operation of a low power sound recognition system; and 
         FIG. 17  is a block diagram of a system that utilizes A2I sparse sound features for sound recognition. 
     
    
    
     Other features of the present embodiments will be apparent from the accompanying drawings and from the detailed description that follows. 
     DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION 
     Specific embodiments of the invention will now be described in detail with reference to the accompanying figures. Like elements in the various figures are denoted by like reference numerals for consistency. In the following detailed description of embodiments of the invention, numerous specific details are set forth in order to provide a more thorough understanding of the invention. However, it will be apparent to one of ordinary skill in the art that the invention may be practiced without these specific details. In other instances, well-known features have not been described in detail to avoid unnecessarily complicating the description. 
     As will be described in more detail below, a low power sound recognition sensor may be configured to receive an analog signal that may contain a signature sound. Sparse sound parameter information may be extracted from the analog signal. The extracted sound parameter information may be sampled in a periodic manner and a context value may be updated to indicate a current environmental condition. The sparse sound parameter information may be compared to both the context value and a signature sound parameter database stored locally with the sound recognition sensor to identify sounds or speech contained in the analog signal, such that identification of sound or speech is adaptive to the current environmental condition. 
     Voice command recognition has become a very important approach for hand-free operations on devices, such as: mobile phone, remote control, security system, automobile, etc. The objective of voice command recognition is to discriminate between utterances in which a given keyword is uttered to utterances in which the keyword is not uttered. Sound or command recognition may be used for various applications, such as:
         to wake up devices by sound (mobile phone, pad, PC);   to classify background sound conditions to assist device operations, such as office, restaurant, driving, on trains;   context awareness sensing to detect special sounds to trigger alarms or surveillance camera such as gunshot, glass break, talking, walking, car approaching;   to detect abnormal operation conditions by sounds such as motor, engine, electric arc, car crashing, glass break sound, animal chewing power cables, rain, wind, etc.       

     Current DSP based sound signature detection solutions typically digitally sample the raw data of the input signal at a Nyquist rate for frequencies of interest. All the complex signal segmentation, signal transformation and final pattern recognition are realized in the digital domain using the Nyquist rate digital samples. It requires both high-performance, high-accuracy analog-front-end (AFE) and analog to digital converter (ADC) to convert the analog signal to a digital one for the following complex digital processing. For example, for sound signal with 8K bandwidth, an ADC with 16-bit accuracy and at least 16 KSps is required. Since it records the raw data of the input signal, the input signal potentially could be reconstructed based on raw data, which increases the threat to the personal privacy. Problems for current DSP solutions are as followings:
         high hardware/algorithm complexity;   high accuracy and high bandwidth on the analog front-end and ADC;   high power consumption when it is running continuously;   potential threat to personal privacy by reconstructing the sampled raw data.       

     Voice command recognition has progressed in paralleled with the development of automatic speech recognition. Most digital based solution using a high-speed and high resolution ADC extract the features in the frequency domain like Mel-frequency cepstrum coefficients (MFCC), Linear Predictive Coding (LPC), etc. Statistical Hidden Markov Models (HMM) are then used to model the phonemes or words. Disadvantages of this solution include high computation complexity and power consumption. 
       FIG. 1  is an illustration of a device in which detection of a unique sound may be used to cause the system to perform a task or operation. In this example, a person  100  may be interacting with a mobile phone  120 . System user  100  may be holding the cell phone and talking directly to it, or user  100  may be wearing an earpiece  110  that contains a microphone and detection circuitry and is in contact with mobile phone  120  via a Bluetooth wireless channel, for example. In either case, earphone  110  and/or phone  120  may often be placed in a low power state in order to conserve battery power. Occasionally, user  100  may wish to place a call or otherwise interact with phone  120  and may speak a known word or phrase, such as “hello”, or “call”, or any other word or phrase that device  110  or  120  has been configured to expect. Sound energy in the form of a sound wave  102  may be received by a microphone within earpiece  110  or phone  120  and converted to an analog electrical signal. However, in order for earpiece  110  or phone  120  to respond to a known voice command from user  100 , some portion of detection logic must be powered on to determine when an expected voice command is received. Since user  100  may speak a large number of words and phrases that have nothing to do with earpiece  110  and/or phone  120 , the detection logic must be able to recognize when an expected command is received without wasting a lot of battery power on non-expected words and phrases. 
     Embodiments of the invention, as described in more detail herein, may perform a process that extracts sparse sound information directly from an analog signal that may be processed using ultra low power analog or mixed signal circuitry. This process is referred to herein as “analog to information” (A2I). 
     Embodiments of the invention are not limited to mobile phones or ear pieces. Other embodiments may include various computer tablets and pads, personal computers, and other forms of personal digital assistants now known or later developed that may be controlled using spoken words or phrases. Other embodiments may be included in control, access, and/or entertainment systems for automobiles, homes, offices, etc. 
     Other embodiments may be included in sensors, such as wireless sensors, that may be monitoring a physical or environmental condition. For example, a water meter that may respond to the sound or vibration of running water, or a rain sensor that may respond to the sound or vibration caused by falling rain, etc. 
       FIGS. 2A and 2B  are plots illustrating performance evaluation metrics for a sound recognition system. Various terms illustrated in these figures will be used during the descriptions that follow. For any sound recognition system, there will be occasions when the recognizer incorrectly rejects a genuine command or incorrectly accepts an imposter based on a recognition score.  FIG. 2A  is a plot illustrating a typical distribution of recognition scores for imposter commands  202  and recognition scores for genuine commands  204 . A threshold  206  may be selected to define when a score will be interpreted as a genuine command or an imposter command. 
       FIG. 2B  illustrates an operating curve  210  for an example sound recognizer system. A false rejection rate (FRR) is the frequency at which a system will inaccurately reject an genuine command. A false acceptance rate (FAR) is the frequency at which a system will inaccurately accept an imposter. The equal error rate (EER) is the rate at which FAR and FRR are equal, which is indicated by line  212 . (1-FRR) is a measure of convenience, as indicated at  214 . (1-FAR) is a measure of security, as indicated at  216 . It is desirable to maximize both measures. Typically, the system detection threshold  206  may be selected so that an operating region  220  of the sound recognizer is located along EER line  212 . 
     A user or system designer may select the operating point (threshold) in real applications based on their security or convenience requirements. For example, if the user or system designer sets the threshold too high, there are may not be any false alarms, but some genuine users will be rejected also. Likewise, if the threshold is set too low, maybe all the genuine users will be accepted, but the false alarm rates may be too high. 
       FIG. 3  is a functional diagram illustrating a typical prior art sound recognition system  300 . Sounds  310  arrive at recognition system  300  via the surrounding environment, which is typically through air. For typical human speech recognition systems, sound signals in the frequency range of a few cycles up to perhaps 20 kHz are of interest. A microphone  312 , or other type of transducer, converts the sound signals to an analog signal. In an analog front end (AFE) stage  320 , the analog signal is converted to a digital signal (A2D) by an analog to digital converter (ADC)  322  that produces a sequence of digital samples  324 . Typically, the sampling rate the Nyquist rate, which is twice the maximum frequency of interest; therefore, for a system that operates on received signals of up to 20 kHz, the sample rate may be 40 kHz. 
     Digital logic  330  includes a portion  332  that converts digital samples  324  to sound information (D2I) by partitioning the samples into frames  340  and then transforming  342  the framed samples into information features using a defined transform function  344 . 
     A next portion  333  then maps the information features to sound signatures (I2S) using pattern recognition and tracking logic  350 . Pattern recognition logic  350  typically operates in a periodic manner as represented by time points t( 0 )  360 , t( 1 )  361 , t( 2 )  362 , etc. For example, as each information feature, as indicated by  346  for example, is produced, it is compared to a database  370  that includes multiple features as indicated generally at  370 . At each time step, recognition logic  350  attempts to find match between a sequence of information features produced by transformation logic  342  and a sequence of sound signatures stored in data base  370 . A degree of match for one or more candidate signatures  352  is indicated by a score value. When the score for a particular signature exceeds a threshold value, recognizer  300  may then indicate a match for the selected signature. 
     Recognition logic  350  may implement one or more types of known pattern recognition techniques, such as a Neural Network, a Classification Tree, Hidden Markov models, Conditional Random Fields, Support Vector Machine, etc. These techniques are all well known and will not be described in further detail herein. 
     Digital domain logic  330  may perform signal processing using various types of general purpose microcontroller units (MCU), a specialty digital signal processor (DSP), an application specific integrated circuit (ASIC), etc. 
     For battery powered system, a significant problem with recognition system  300  is that all the complex signal segmentation, signal transformation and final pattern recognition operations are performed in the digital domain. It requires both a high-performance, high-accuracy analog-front-end (AFE) and ADC to convert the analog signal to a digital signal for the following complex digital processing. For example, for a sound signal with an 8 kHz bandwidth, an ADC with 16-bit accuracy operating at least 16 KSps (samples per second) is required. Since the recognizer records the raw data of input signal  310 , the input signal potentially could be reconstructed based on stored raw data, which poses a threat to the personal privacy of a user. 
     To mitigate the problem of high power consumption, system  300  may be configured to duty-cycle operation modes between normal detection on and standby. For example, from time to time the whole system may be turned on and run in full-power mode for detection. The rest of time it may be in low-power standby mode. However, duty cycled operation increases the possibility of missing an event. 
       FIG. 4  is a functional diagram illustrating analog-to-information (A2I) operation of a sound recognition system  400  that operates on sparse information  424  extracted directly from an analog input signal. Recognition system  400  sparsely extracts the frame-based features of the input sounds in the analog domain. Instead of digitizing all the raw data, recognizer  400  only digitizes the extracted features. In another words, recognizer  400  only digitizes information features. Pattern recognition based on these features is performed in the digital domain. Since the input sound is processed and framed in the analog domain, the framing removes most of the noise and interference that may be present on a sound signal. This in turn reduces the precision needed within on AFE. An ADC samples the frame-based features, therefore, the speed and performance requirement on the ADC are greatly reduced. For frames as large as 20 ms, the sound features may be digitized at a rate as slow as 50 Hz, much lower than the input signal Nyquist rate (typically 40 KHz for 20 KHz sound bandwidth). With such a moderate requirement on the performance of the AFE and ADC, extreme low power operation may be accomplished for the AFE and ADC design. 
     Due to its ultralow power consumption, system  400  to be operated in a continuous manner so that the possibility of missing a targeted event is reduced. Also, since system  400  only sparsely extracts sound features, these features are not sufficient to be used to reconstruct the original input sound, thereby assuring privacy to a user of the system. 
     Referring still to  FIG. 4 , a same analog signal  310  may be received by microphone  312  and converted to an analog signal. Analog signal processing logic  424  within analog front end  420  may perform various forms of analog signal processing. For example, one or more instances of low pass, high pass, band pass, band block, etc filters may be included to produce one or more filtered output channels, as illustrated at  425 . The processed analog channel signals may then be framed by analog frame logic  426 . The length of each frame may be selected for a given application; typical frame values may be in the range of 1-20 ms, for example. 
     After framing, a resultant value for each channel may then be digitized by ADC  422  to produce a sparse set of digital feature information as indicated generally at  424 . Due to the low digitalization rate that is used, a low cost, low power sigma-delta analog to digital converter may be used. The general operation of ΣΔ ADCs is well known, e.g. see: “The Design of Sigma-Delta Modulation Analog-to-Digital Converters,” Bernard Boser and Bruce Wooley, 1988, which is incorporated by reference herein. The general operation of an ΣΔ ADC will now be described to aid in understanding the operation of embodiments of the invention. While the use of a ΣΔ ADC is described herein, other implementations may use other types of known or later developed ADCs. 
     The rudimentary delta sigma converter is a 1-bit sampling system. An analog signal applied to the input of the converter needs to be relatively slow so the converter can sample it multiple times, a technique known as oversampling. The sampling rate is typically hundreds of times faster than the digital results at the output ports. Each individual sample is accumulated over time and “averaged” with the other input-signal samples through a digital/decimation filter. 
     The ΣΔ converter&#39;s primary internal cells are the ΣΔ modulator and the digital filter and decimator. While Nyquist A/D converters have one sample rate, the ΣΔ converter has two—the input sampling rate (fS) and the output data rate (fD). The ratio of these two rates is the decimation ratio and defines the oversampling rate. A ΣΔ modulator coarsely samples the input signal at a very high fS rate into a 1-bit stream. The digital/decimation filter then takes this sampled data and converts it into a high-resolution, slower fD rate digital code. 
     These digital features from ΣΔ ADC  422  may then be provided to pattern recognition logic  450  in the digital domain. Recognition logic  450  then maps the information features to sound signatures (I2S) using pattern recognition and tracking logic. Pattern recognition logic  450  typically operates in a periodic manner as represented by time points t( 0 )  460 , t( 1 )  461 , t( 2 )  462 , etc. For example, as each information feature, as indicated by  430  for example, is produced, it is compared to a database  470  that includes multiple features as indicated generally at  470 . At each time step, recognition logic  450  attempts to find match between a sequence of information features produced by ADC  422  and a sequence of sound signatures stored in data base  470 . A degree of match for one or more candidate signatures  452  is indicated by a score value. When the score for a particular signature exceeds a threshold value, recognizer  400  may then indicate a match for the selected signature. 
     Recognition logic  450  may implement one or more type of known pattern recognition techniques, such as a Neural Network, a Classification Tree, Hidden Markov models, Conditional Random Fields, Support Vector Machine, etc. These techniques are all well known and will not be described in further detail herein. 
     Digital domain logic  450  may perform signal processing using various types of general purpose microcontroller units (MCU), a specialty digital signal processor (DSP), an application specific integrated circuit (ASIC), etc. 
     In this manner, recognition system  400  may operate continuously, but only use a small amount of power. It may continually monitor for one or more expected types of sounds, such as gun-shot sound, glass break sound, voice commands, speech phrase, a music melody, ultrasound emission for electric discharge such as an electrical arc in a piece of equipment, etc. 
     As will now be described in more detail, various implementations of AFE  420  may be used to wake up devices based on the receipt of an expected sound; for example, a mobile phone, pad, PC, etc, may be woken from a low power mode in response to detecting a particular word or phrase spoken by a user of the system. AFE  420  may be used to classify background sound conditions to provide context awareness sensing to assist in device operations. For example, speech recognition operation may be adjusted based on AFE  420  detecting that it is in an office, in a restaurant, driving in a vehicle or on train or plane, etc. 
     AFE  420  may also be used to detect special sounds to trigger alarms or surveillance camera, such as: a gunshot, glass break, talking, walking, car approaching, etc. It may detect abnormal operation conditions by sounds, such as: motor or engine operation, electric arc, car crashing, breaking sound, animal chewing power cables, rain, wind, etc. 
       FIG. 5  is a detailed block diagram of another A2I feature extraction system  502  in which analog front end channel  520  is illustrated. A2I logic block  502  also includes signal trigger logic  580 . Signal trigger logic  580  evaluates the signal condition versus background noise to decide whether the following signal chain needs to be awakened. This may allow the AFE channel  520  logic to be placed in a power off state most of the time. When signal trigger logic  580  detects a certain amount of signal energy, then it may assert a “sound detected” trigger (S-trigger) control signal that turns on power for the AFE channel  520 . Microcontroller  550  performs pattern recognition using digital signal processing techniques as described in more detail above. 
     A 1 , A 2  are input gain blocks. The analog input  512  from a microphone may be compared with an analog threshold “Vref”. Once it is higher than “Vref,” an output of comparator  581  is switched from “0” to “1” to generate a trigger signal S-trigger indicating that a large input signal has been received. This is useful when the environment is very quiet. While the MIC input signal is below “vref”, the whole AFE  520  may be placed in a power down mode until some larger sound comes in. 
     After S-trigger is switched to high, it will power-up AFE  520  to start collecting the input signal and do the frame-based feature extraction using ADC  522 . However, to save power, trigger control block  582  may cause microcontroller  550  to remain off for a period of time while AFE  520  processes an initial set of frames. For example, AFE  520  may buffer an initial truncated set of several frames of sound features in buffer  523  and do a pre-screen by itself using feature pre-screen logic block  524 . This allows pre-screen logic  524  to make sure the first few frames of features are very likely the targeted sound signature before waking up MCU  550  to collect the features and do a more complicated and accurate classification. For example, buffer  522  may buffer five frames that each represent 20 ms of analog signal. 
     Event trigger logic  582  may decide whether classifier  550  needs to wake up to run full signature detection, as discussed above. Event trigger  582  may be designed to rely on one AFE channel feature identified by pre-screen logic  524  or a combination of several channel features to signal a starting point. Pre-screen logic  524  may include memory that stores a database of one or more truncated sound signatures that may be compared against the truncated feature samples stored in buffer  523 . When a match is detected, then an event trigger signal E-trigger is asserted to trigger control logic  582  that then causes MCU  550  to wake up and begin performing a rigorous sound recognition process on the sparse sound features being extracted from the analog signal provided by microphone  512 . 
     In these blocks, MCU  550  is the most power hungry block, AFE  520  is second most power hungry block, comparator  581  used to generate S-trigger is a very low power design. Using this triggering scheme, the frequency of waking up the power hungry blocks is minimized and the power efficiency of the whole system is thereby maximized. 
       FIG. 6  is a detailed block diagram of another AFE A2I logic block  602  in which multiple analog channels  520 ( 1 )- 520 ( n ) are illustrated. Each AFE channel may operate on the input analog signal from microphone  512  to extract a different analog feature. For example, AFE channel  520 ( 1 ) may extract zero-crossing information from the raw analog signal or from a filtered version of the analog input signal. AFE channel  520 ( 2 ) may extract a total energy value from the analog signal. AFE channels  520 ( 3 )- 520 ( n ) may each contain a band pass, low pass, high pass or other type of filter and thereby extract an energy value based on a particular band of frequencies, for example. 
     Each AFE channel extract features directly using analog or low power mixed signal processing. Each feature may be sampled at very low rate, for example, the feature interval may be in the range of 5-20 ms. Typically, a spoken command word or other sound event may be approximately one second in duration; therefore features for a one second event may be extracted from only 200-50 frames, depending on frame length. The sparse features cannot be used to reconstruct speech content so privacy is protected; therefore, no eavesdropping is possible. 
     Other embodiments may include other types of analog signal processing circuits that may be tailored to extraction of sound information that may be useful for detecting a particular type of sound, such as motor or engine operation, electric arc, car crashing, breaking sound, animal chewing power cables, rain, wind, etc. 
       FIG. 7  is a plot illustrating a region of interest that may be initiated by a trigger signal from signal trigger logic  580 . Trigger logic  580  may compare an energy value for a current frame against an average or cumulative energy value for a few preceding frames. For example, trigger logic  580  may be designed to compare an energy value for a current frame to an energy value from the two prior frames. When the current energy value of the current frame exceeds the energy values of the two preceding frames, then it asserts the signal trigger control signal to cause the AFE channels to be turned on. 
     For example, at the frame indicated at  702 , frame energy exceeds the prior two frame energy values. The AFE channels are turned on for a sample window period of time indicated at  703 , such as for one second. During sample window  703 , the AFE channels extract sound information from the analog signal, as discussed above. After one second, the AFE channels are again turned off. At the frame indicated at  704 , frame energy again exceeds the frame energy of the prior two frames and the AFE channels are again turned on for a one second period  705  to again allow feature extraction. After one second, the AFE channels are again turned off. 
     In this manner, power efficient feature extraction is only performed during a region of interest (ROI) that follows a spike in signal energy. 
     Referring again to  FIGS. 5 and 6 , an adjustable preamplifier  584  may be provided that allows the amplitude of the analog signal from microphone  512  to be normalized during operation to prevent saturation during periods of high background noise, for example. The gain of preamp  584  may be adjusted by context sensing circuit  686 , for example. Context sensing circuit  686  typically operates periodically and may cause one or more of the AFE channels to be periodically turned on to sample the background noise level. Context sensing circuit  686  will be described in more detail later in this disclosure. 
     An adjustable bias circuit  683  may be provided to allow low power operation of MIC  512 . Bias circuit  683  may be adjusted to vary the noise floor and sensitivity of the MIC based on different sound environments, as determined by context sensing module  686 , for example. When MIC  512  is biased with a low current to reduce power consumption, it may exhibit a high noise floor and low sensitivity. Similarly, when biased with a higher bias current value from bias circuit  683 , it may exhibit a lower noise floor and a higher sensitivity. Typically, a microphone consumes a large amount of power when biased at a default high current. Its power consumption may be comparable or larger than fully-power running AFE. To optimize the power of the whole system, MIC current bias may be adjusted with the low power triggering scheme discussed in above. Before S-trigger is switched to high, MIC  512  is low current biased and AFE  520  is powered off for power saving. When S-trigger goes to high, AFE  520  is powered up and MIC  512  is high-current biased to generate more accurate analog signal for feature extraction. 
       FIGS. 8-10  illustrate examples of robust A2I sound feature extraction. Frame based zero-crossing (ZC) count is a unique feature for pattern detection applications, such as voice command recognition or sound signature detection. It is typically easy to implement; however it may be vulnerable to circuit non-idealities and interference. These common-mode interference and circuit non-idealities may be removed or mitigated by extracting the ZC counts differentially thereby making ZC a very robust feature for signature detection. Several different schemes for extracting the differential ZC counts will now be described. 
     Differential ZC rate may be extracted in several different manners, such as: determining a difference in ZC rate between adjacent sound frames (time-domain), determining ZC rate difference by using different threshold voltage instead of only one reference threshold (amplitude-domain); determining ZC rate difference by using different sampling clock frequencies (frequency-domain), for example. These ZC rate difference may be used individually or be combined for pattern recognition. 
       FIGS. 8A-8D  illustrate extraction of time-wise differential ZC rate. Extracting differential ZC rate is a very power and cost efficient solution; it doesn&#39;t needs extra filtering to clean an input signal or more power for better circuit performance. Typically, the implementation is very easy. There is almost no extra hardware complexity to produce a ZC rate difference. For example, for time-wise ZC difference, one ZC counter may be used for ZC counting. By recording the total ZC counts of a current and a previous frame a ZC difference between two frames may then be calculated. 
     In essence, time-wise differential ZC rate provides coherence information about the analog signal between adjacent frames. The more coherent time-wise, the less ZC rate difference. In human speech, vowel sounds produce a low differential ZC rate, while consonant sounds produce a large differential ZC rate. 
       FIG. 8A  illustrates a portion of an analog signal received from a microphone.  FIG. 8B  is a schematic for a channel circuit  820  to extract a time-wise differential ZC. Channel circuit  820  may be included within AFE  600  as another one of AFE channels  520 ( n ), for example. As described above, sound features are extracted by slicing the analog signal into frames, as illustrated by frames  801 ,  802 . Typically, for a given system or application, a frame size will be used that provides good results for the application. Typically, a frame size in the range of 1-40 ms will be selected. During each frame, the number of times the amplitude of the signal  804  crosses a threshold voltage Vth, as determined by comparator  810 , may be counted in counter  812 . In this example, a counting clock  814  is used to catch these crossings. A sampling circuit  813  latches the value of comparator output  811  on each period of counting clock  814 . An exclusive-OR type circuit then indicates when the current value of comparator output  811  is different from the last sample in response to a zero crossing. Counter  812  is incremented each time sampling circuit  813  indicates a zero crossing has occurred. The frequency of the counting clock may affect final count value, since a slow counting clock may miss catching some of the zero crossings. At the end of each frame period, a count value is transferred to buffer  815  and counter  812  is reset by frame clock  822 . Prior to each reset, subtractor  816  produces differential ZC value  818 . 
       FIG. 8C  illustrates an example analog signal  830  that may be received by ZC channel circuit  820 . Frame clock  822  defines a frame length, while counting clock  814  counts the number of times the output  811  of comparator  810  crosses a threshold value. The threshold value may be zero volts for example, or it may be set at a higher or lower value. As mentioned above, the frequency of the counting clock may affect final count value, since a slow counting clock may miss catching some of the zero crossings, as illustrated during region  840  of this example. 
       FIG. 8D  illustrates raw analog input signal  804  and time-wise differential ZC  818 , while  FIG. 8E  illustrates and expanded portion of  FIG. 8C . 
       FIGS. 9A-9D  illustrate amplitude-wise differential ZC extraction.  FIG. 9A  illustrates how two different levels of threshold may be set, Vth 1  and Vth 2 , for example. The zero crossing counts detected for each threshold may then be subtracted to produce a differential ZC rate feature. The information about how the analog signal amplitude change affects the ZC counting provides a reliable metric for sound recognition. Typically, a larger threshold produces a smaller ZC rate, while a smaller threshold produces a larger ZC rate. The threshold values may be dynamically setup based on a previous frames&#39; energy, or they may simply be static levels, for example. Two counter circuits similar to the circuit shown in  FIG. 8B  may be used to count the two ZC values.  FIG. 9B  is a plot illustrating ZC rate  902  at a Vth 1  and another ZC rate  904  at an offset (ZOOS) threshold Vth 2  for a sound signal  906  illustrated in  FIG. 9D . In  FIG. 9C , amplitude-wise differential ZC rate  908  illustrates the result of ZC  902 -ZCOS  904 . Amplitude-wise ZC extraction is insensitive to device non-idealities such as noise, offset, mismatch and command interference. 
       FIGS. 10A-10C  are plots illustrating frequency-wise differential ZC rate extraction. ZCs that are counted by different clock frequencies will get different counts. A frequency-wise differential ZC may indicate a dominant frequency variation of an input signal. White noise, and ZC count is proportional to the frequency of the frame counting clock. In the human voice, a low frequency vowel produces a smaller ZC count using a differential clock. A high-frequency consonant may produce a larger ZC count. 
     A frequency-wise differential ZC rate extraction may be performed using two counters similar to  FIG. 8B  that are counted by different counting clocks but reset by the same frame clock. A frequency-wise differential ZC count provides a robust metric that is insensitive to device non-idealities such as noises, offset, mismatch and command interference. 
       FIG. 10A  illustrates an example analog sound signal.  FIG. 10B  illustrates plots of ZC counts produced by several different counting clock frequencies.  FIG. 10C  illustrates differential ZC counts produced from different pairs of ZC counts from  FIG. 10B . 
       FIGS. 11 and 12  illustrate two approaches for using A2I sparse robust sound features to recognize a spoken word or phrase. Rather than short phoneme or word recognition, an entire command sound signature, typically spanning 1-2 seconds, may be treated as a unique pattern. Good recognition results may be obtained for a whole sound signature pattern that typically contains one to five words. 
     Based on the sparse A2I features, a universal algorithm or a command specific algorithm may be defined based on each command to reach the best recognition performance. For example, a region of interest sampling window from A2I analog circuits may be for five seconds, during which average energy and/or zero-crossing features may be extracted based on every four frames (20 ms) or eight frames (40 ms) for 5 ms frames. In order to reduce the amount of memory that is required for storing features, in this example an average is taken across a set of four or eight 5 ms extraction frames. Simulation results indicate that the recognition accuracy is similar for both cases. Of course, other extraction frame lengths and/or averaging set sizes may be chosen 
     In another example, the region of interest sampling window may only be one or two seconds long. These time-series features may then be fed into a standard pattern recognition classifier, such as: Neural Network, Classification Tree, Hidden Markov models, Conditional Random Fields, Support Vector Machine, etc, for example. 
     As illustrated in  FIG. 7 , each sample window may be initiated by detecting a starting point and then robust features are extracted directly from the analog sound signal during the following region of interest at a rate that is significantly lower than the Nyquist sampling rate. 
       FIGS. 11A-11B  illustrate a time series sound signature detection example. In this example, one channel of total energy, three channels of low pass energy (0.5 kHz, 1 kHz, 1.5 kHz) and one channel of differential ZC rate are extracted from an analog signal as illustrated in  FIG. 11A . In the example, the phrase “get directions” is being spoken. 
       FIG. 11B  illustrates one channel  1102  corresponding to a low pass filter with a cutoff frequency of 500 Hz and one channel of differential ZC rate  1104 . As described above, the robust features are then provided to a classifier that uses currently known or later developed pattern matching techniques to select the most likely command word signature from a database of word and phrase signatures that has been developed using known training techniques. 
     The sampling window may be slid along the stream of robust features in the digital domain using known signal processing techniques. For example, an attempt is made to match a pattern to features extracted from frame  1  to frame  50  for a one second pattern. If no match is found, then an attempt is made to match the pattern again to features for frame  2  to frame  51 , etc, in case a correct starting point was not detected. 
     Test results show performance is good for short command recognition, with an equal error rate (EER)&lt;0.5%. For example: “Take A Picture”, “Open the Trunk”, “Get Directions”, etc. A user may speak at normal speed and tone. The commands should be said within the sampling window time period for a given application, such as within one second, for example. The technique is robust in noisy environments: &gt;10 dB SNR is good enough. The technique may be configured to be speaker independent or speaker dependent. Table 1 summarizes simulation results for a command “Get directions.” The neural network (NN) score has a range of −1 to +1, where more positive indicates a higher degree of similarity between a stored signature and a pattern being recognized. 
     
       
         
           
               
             
               
                 TABLE 1 
               
             
            
               
                   
               
               
                 Simulation results for “Get Directions” 
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Threshold 
                 NN Score 
                 0.2 
                 0.3 
                 0.4 
                 0.5 
                 0.6 
                 0.7 
                 0.8 
               
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 Correct 
                 Genuine 
                 99.8 
                 99.73 
                 99.54 
                 99.18 
                 98.35 
                 96.87 
                 93.61 
               
               
                 Recognition 
                 command 
               
               
                 Rate 
                 (162k) 
               
               
                   
                 Imposter 
                 99.03 
                 99.63 
                 99.82 
                 99.91 
                 99.96 
                 99.98 
                 100 
               
               
                   
                 commands 
               
               
                   
                 (1000k) 
               
               
                   
               
            
           
         
       
     
       FIGS. 12A-12B  illustrate a time-series, multi-stage approach for using A2I sparse sound features to recognize a spoken word or phrase. In this approach, a word or phrase is broken into smaller portions, such as phonemes, and a signature is trained for each portion. Recognition of a complete word or phrase may involve several sequential stages of recognition. During recognition, end point detection may be performed to determine the extent of each stage.  FIG. 12A  is a sound plot illustrating a spoken phrase: “hello Siri”. In this example, the work “hello” is recognized in a first stage of pattern matching, “Si” is recognized in a second stage, and “ri” is recognized in a third stage. A fourth stage detects silence indicating an end to the command. The pattern recognition may be performed using known or later developed techniques, such as: a Hidden Markov model, a neural network, etc, for example. The multi-stage technique may be sensitive to correct end point detection, which is not an issue in the whole phrase detection technique illustrated in  FIGS. 11A-11B . 
       FIG. 12B  illustrates a plot of the robust differential ZC rates extracted from the analog signal for this speech sequence. As discussed above, additional energy and ZC feature channels may also be extracted to assist in pattern recognition. 
     Cloud-Based Training 
       FIG. 13  is a block diagram illustrating recognition system  400  interacting with a cloud based sound recognition training system  1300 . Training server  1300  includes signature databases  1302 ,  1304  that may be collected from a large number of distributed recognition systems  400 ( n ). Each remote recognition system  400 ( n ) may be part of a mobile phone, tablet, pad or other entertainment or communication system, for example. Recognition system  400  may be included in vehicles for driving or entertainment control, for example. Each recognition system  400  may be in communication with cloud based training server  1300  using known means of communication, such as a cellular data channel, WIFI, or other type of wireless channel, for example, that is supported by link circuitry  1320 . 
     Training server  1300  maintains a database  1302  of sound signatures that have been developed using known or later developed training schemes  1308  based on sparse sound features as described herein. Typically, voice samples taken from a number of people are used to create a signature that is then speaker independent. Training server  1300  may also maintain a database  1304  of imposter sound signatures. These may be signatures of non-voice events, such as: noise, tapping, doors opening/closing, music, etc, for example. Training server  1300  may be configured to create application specific signature databases that are provided to various recognizers  400 ( n ). An application specific signature database may be downloaded to a recognizer  400 ( n ) when it is initially configured, for example. Additional or different application specific database portions may be downloaded to a recognizer  400 ( n ) when it starts a new application, for example. In this manner, each recognizer  400 ( n ) may have a signature database that is configured to be compatible with a specific recognition application. 
     Each recognition system  400 ( n ) maintains a local database of sound signatures that it uses to recognize words and commands. During the course of operation, occasionally a spoken word or command may be falsely accepted or falsely rejected. The false acceptance rate (FAR) and false rejection rate (FRR) are metrics that measure these errors. An application may be configured to try to determine when a recognition error occurs. For example, if the system falsely accepts a command that the user did not intend, then a following inconsistent action by the user may be used by the system to infer that a recognition error was made. Similarly, if the system falsely rejects a command, a user will typically repeat the command. The system may be able to keep track of repeated sounds and infer that a recognition error was made. 
     When recognizer  400 ( n ) determines that a recognition error has occurred, it may send a copy of the extracted sound features from a sample window along with an indication of which sound signature was incorrectly selected and the type of error to training server  1300  via a wireless link. This may be done immediately in some implementations; other implementations may collect several sets of errors and only send them periodically or in response to a query from training server  1300 , for example. 
     As training server  1300  receives sample packets from various recognition systems  400 ( n ), it may use the received sample packets to perform online training  1306  to improve the signature database. The training may be performed using known or later developed training techniques using the sparse sound samples collected from multiple diverse recognition systems  400 ( n ). 
     Periodically, training server  1300  may send a revised signature database to a recognition system  400 ( n ) after a training session has been performed. A signature database update may be done based on a periodic schedule, for example, during which a request for recent sound parameter info may be sent to one or more remote recognition systems  400 ( n ). In some implementations, a signature database update may be provided in response to a request from a recognition system  400 ( n ), or in response to a recognition system  400 ( n ) sending an error sample, for example. 
     In this manner, each recognition system may be minimized to store only a portion of a signature database that is needed for an active application. Furthermore, recognition system  400 ( n ) does not need to be capable of performing local training, which may thereby reduce complexity and power consumption. 
       FIG. 14  is a flow diagram illustrating interaction of recognition system  400  with cloud based training system  1300 . Most of the time, recognition system  400  may be in a low power sleep mode. As described above in more detail, when a sound is detected that exceeds a background threshold, a portion of recognizer  400  is awoken to perform further analysis of the received sound. If an initial analysis indicates a possible command word is being received, then additional classification circuitry  550  may be awoken. An application processor  1400  may be activated  1410  in response to a recognized command word or phrase. As long as a successful recognition occurs, recognizer  400  and application processor  1400  may operate autonomously from training server  1300 . Typically, application processor  1400  is activated only infrequently in response to a command recognized by recognition system  400 . 
     Application processor  1400  may be coupled to link hardware  1320 , referring again to  FIG. 13 , to initiate and control information exchanges with cloud based training server  1300 . Application processor  1400  may be one of various types of microprocessors, microcontrollers, digital signal processor, etc, for example. 
     Interaction  1420  with training server  1300  needs to occur only when a recognition error occurs. In this manner, the distributed recognition systems  400 ( n ) may operate in low power modes, but have the power of the cloud based training server  1300  available when needed. 
     The cloud based training server  1300  may routinely request sound parameters from recognizer  400  to calibrate sound sensor  400  to the environment. For example, for an application where the environment is expected to be relatively stable, calibration may be performed on a weekly basis. However, when a more dynamic environment is expected, training may be performed more frequently, such as hourly or even more often, for example. 
     Local sound sensor  400  may routinely send sound parameters extracted from a current environmental background sounds to remote training server  1300  even without an occurrence of an error in order to refine the local database of sensor  400 . In response to a request or query from sensor  400  that includes background sound parameters, cloud based training server  1300  may provide a retrained sound parameter database. 
     Context Awareness 
     A mobile or stationary device may further adjust its performance and status to get the best communication performance and maximum power saving by being aware of its current context. It is very difficult to reliably and accurately sense a devices&#39; location based on GPS or cell tower triangulation, for example; however, sounds detected by the device&#39;s microphone may provide very cost effective information to assist in this function. For example, when a mobile device is in a pocket or a bag, the false alarm rate of keyword detection needs to be reduced to a minimum, the display should be turn off and buttons should be less sensitive to pressure. When mobile device is in a user&#39;s hands or in open air, the detection hit rate may need to be increased, even if the false alarm rate is increased as well. 
     By making use of the multi-stage triggering mechanisms described above, a device may be always listening and checking the environment without dissipating much power. As described above, this is done by waking various portions of the device in different working modes in response to detected sounds. A signature detection threshold may be automatically tuned based on user security and convenience requirements and according to environment changes. 
     As described previously, an ultra low power analog front end section may continuously compare an incoming analog signal from a microphone with long-term background conditions to decide whether to wake up a following A2I logic module that may extract sound features directly from the analog circuit. When a significant change from the long-term background level occurs, a signal trigger may be asserted to awaken the A2I logic module. Once it is awakened, the A2I logic module may remain on for a relatively long period of time, such as one second, for example. While it is awake, the A2I logic module will begin extracting sound features from each frame of the analog signal and analyze extracted features from a small number of frames, such as five 20 ms frames, for example. If the A2I logic detects a pattern that might be an expected command word or phrase, it may then assert an event trigger to awaken a next stage of logic that is equipped to perform full sound or speech recognition using a long sequence of the A2I sound features. 
     A context awareness logic module may regularly sample A2I features and buffer a representative portion of them locally. Once the context awareness logic module collects enough information, or when an abruptly changing condition occurs, it may either update a context indicator locally or assert a context trigger to cause a following digital classifier to update environment conditions. 
     A sound signature detection threshold may then be adjusted based on the current detected environment. For example, in a time series detection process as described in more detail with regard to  FIGS. 11 and 12  a detection threshold, such a neural net (NN) score may be changed according to environment changes. 
     By using context awareness sound signature detection solution, the mobile device can further adjust its performance and status to get the best communication performance and maximum power saving. The device may be always listening or checking the environment using an ultra low power analog front end stage, while the multi-stage trigger will wake the device in different working modes to conserve power. 
     For example, various types of background sound conditions may be classified to assist device operations, such as: home, office, restaurant, driving, trains, plane, bus, in a purse or bag, in a pocket, in open air, etc. Recognition performance may be improved by using a portion of a signature database that has been trained under similar background noise conditions, for example. 
     Context awareness sensing may also be applied to sensors used to detect special sounds to trigger alarms or surveillance cameras, for example. By continually being aware of current environmental background sounds, a sensor may be better able to detect sounds of interest, such as: gunshot, glass break, talking, walking, car approaching, etc, for example. Similarly, by continually being aware of current environmental background sounds, a sensor may be better able to detect abnormal operation conditions, such as: motor or engine problems, electrical arcing, car crashing, breaking sounds, animal chewing power cables, rain, wind, etc, for example. 
       FIGS. 15A and 15B  are block diagrams illustrating examples of interfacing a microphone  1510  to a backend system  1550  in conjunction with A2I detection results. An A2I chip may be designed to connect to multiple types of microphones (MIC), such as either analog or digital types of microphones. 
     A universal connection may be provided that will accept either a digital MIC (DMIC) or analog MIC (AMIC) using a same configuration of signal pin inputs. An internal circuit may auto-detect the input type (analog/digital) of the MICs using known or later developed techniques. 
     An A2I module  1530  may use a dedicated MIC, not illustrated, or share MIC  1510  with other modules, such as backend unit  1550  that may include a CODEC (coder/decoder) that may perform various known types of signal processing to the audio signal. Backend unit  1550  may also include a microcontroller (MCU) that may control the operation of the CODEC, for example. 
     For a digital MIC, its output may be a digital pulse density modulated (PDM) stream that has to be filtered to get the final decimated digital output, which is digitized raw data of input sound. In that case, the features will be still extracted based on frames, but need not to be quantized, since it happens in the digital domain instead of analog. 
       FIG. 15A  illustrates a parallel connection between MIC  1510 , A2I signature detection module  1530  and backend unit  1550 . Signal cleaning logic  1520  may filter the signal received from MIC  1550  using known filter techniques to remove or enhance various frequencies. The raw sound stream from MIC  1510  or the filtered signal from filter  1520  may be provided directly to CODEC  1550  via mux  1532 . A2I signature detection module  1530  operates in a manner as described in more detail above to continually monitor an input signal from MIC  1510  and detect when a sound, word or phrase, or event of interest is heard by MIC  1510 . When an event is detected, A2I module  1530  may then provide the detection results to the MCU in backend unit  1550 . In this manner, a single microphone may be used with light loading on the MIC output, and the CODEC will not see the existence of A2I unit  1530 . 
       FIG. 15B  illustrates a serial connection between MIC  1510  and the CODEC in backend unit  1550 . Based on detection decisions made by A2I module  1530 , selector  1534  may be controlled to enable/disable MIC signal to backend system  1550 . In this manner, a CODEC module in backend system  1550  can be selectively connected to MIC  1550  only when an event of interest has been detected by A2I module  1530 . 
       FIGS. 16A-16E  are flow diagrams illustrating various aspects of the operation of a low power sound recognition module. As discussed above, the sound recognition module may provide command recognition for various types of systems, such as: mobile phone, remote control, security system, automobile, etc. Initially, the sound recognition module along with all or a portion of the rest of the system may be placed  1602  in a low power sleep mode, in which only a very small amount of analog detection logic in the sound recognition module remains active. The active analog detection logic may then monitor an analog signal received from a microphone that is connected to the sound recognition module. 
     Most of the time, a user may not be actively interacting with the system, in which case the microphone may pick up background noise and sounds. During this period, the active analog detection logic will be receiving  1604  an analog signal that normally contains background noises and sounds; however, when the user does speak a command, the analog may contain an expected sound. Since the expected sound is a command word or phrase, the expected sound has a defined length in time. A region of interest (ROI) time period may be defined based on the expected length of time of any valid word or phrase that the sound recognition module is configured to recognize. 
     As described earlier in more detail, the analog detection logic may compare sound level energy during a current time frame with sound levels during several prior time frames in which only background noise was occurring. When the signal exceeds  1606  the background noise level, then a sound trigger (s-trigger) signal may be asserted  1608  to awaken and trigger an analog feature extraction portion of the sound recognition module. As described above in more detail, the analog feature extraction circuitry may contain multiple channels of filters, zero crossing detectors, etc that are configured to extract  1610  sparse A2I sound parameter information from the analog signal. The low power analog feature extraction circuitry may be completely analog, or may be a mix of analog and low power digital circuitry, for example. As described above in more detail, the analog feature extraction circuitry may operate at a low sampling rate, such as 500 samples per second or lower, even as low as 50 samples per second, for example. 
     As described in more detail above, the sparse A2I sound parameter features may be sound energy levels from the entire analog signal or from various filtered bands of the analog signal. Sparse sound parameters may also include time frame based differential zero crossing rates, for example. As described in more detail above, a differential ZC rate may be extracted in several different manners, such as: determining a difference in ZC rate between adjacent sound frames (time-domain), determining ZC rate difference by using different threshold voltage instead of only one reference threshold (amplitude-domain); determining ZC rate difference by using different sampling clock frequencies (frequency-domain), for example. These ZC rate difference may be used individually or be combined for pattern recognition. 
     Each time the feature extraction circuitry is triggered, an initial truncated portion of the sound parameter information is compared  1612  to a truncated sound parameter database stored locally with the sound recognition sensor to detect when there is a likelihood that the expected sound is being received in the analog signal. As described above in more detail, the truncated portion may cover a span of just five time frames that each represent 20 ms of the analog sound signal, for example. Various implantations may use longer or shorter time frames and fewer or more frames during this event detection activity. 
     When a likely match between the truncated portion of the sound parameter information and the truncated signature database exceeds a threshold value  1614 , then an event trigger (e-trigger) signal may be asserted  1618  to awaken and trigger digital classification logic. 
     If no likely match is detected  1616  during the ROI time period, then the feature extraction circuitry is again placed  1602  into a sleep state. 
     The extracted A2I sparse sound parameter information is processed  1620  using the digital classification portion to identify expected sounds or speech contained in the analog signal after the trigger signal is generated. As described above in more detail, these sparse time-series features may be processed by comparing them to a local sound signature database of whole words or phrases using a standard pattern recognition classifier, such as: Neural Network, Classification Tree, Hidden Markov models, Conditional Random Fields, Support Vector Machine, etc, for example. 
     When a classification score exceeds a threshold value  1622 , then the spoken word or phrase is accepted  1626 . When a detection score does not exceed the threshold  1622  during the ROI  1624 , then the spoken command is rejected  1627 . As described above in more detail, a user or a designer of the system may set or adjust the threshold value to balance the false rejection rate (FRR) and false acceptance rate (FAR). 
     When a command word or phrase is accepted  1626 , additional portions of the mobile or stationary system may be powered on to respond to the recognized command word. 
     As described above in more detail, in some implementations a cloud based training server may be utilized to improve recognition rates. In this case, when a command word detection error occurs  1628 , the extended set of sparse sound parameters that were collected over the ROI time period may be transmitted  1630  to the cloud based training server. As described in more detail above, a detection error may be inferred by the system based on subsequent input from a user after the acceptance or rejection of a spoken word. 
     As described in more detail above, the cloud based training server may then use the sound parameter information received from the remote sound recognition system to improve the signature database. An updated signature database may then be returned  1632  to the remote recognition system. As the training server receives sample packets from various recognition systems, it may use the received sample packets to perform online training to improve the signature database. The training may be performed using known or later developed training techniques using the sparse sound samples collected from multiple diverse recognition systems. 
     Periodically  1640 , the cloud based training server may send  1646  a revised signature database to the recognition system after a training session has been performed. The cloud based training system may send a request  1642  for recent sound parameter info to one or more remote recognition systems. The received recent sound parameter info may then be used by the cloud based training server to perform training. A signature database update may be done based on a periodic schedule  1640 , for example. In some implementations, a signature database update may be provided in response to a periodic request  1642  from a recognition system, or in response to a recognition system sending an error sample  1632 , for example. 
     As described above in more detail, a mobile or stationary device may further adjust its performance and status to get the best communication performance and maximum power saving by being aware of its current context. By making use of the multi-stage triggering mechanisms described above, a device may be always listening and checking the environment without dissipating much power. 
     A context awareness logic module may regularly  1650  sample and extract  1652  A2I features and buffer a representative portion of them locally. Once the context awareness logic module collects enough information, or when an abruptly changing condition occurs, it may either update  1654  a context indicator locally or assert a context trigger to cause a following digital classifier to update environment conditions. 
     A sound signature detection threshold may then be adjusted  1656  based on the current detected environment. For example, in a time series detection process as described in more detail with regard to  FIGS. 11 and 12  a detection threshold, such a neural net (NN) score may be changed according to environment changes. In this manner, the sound parameter information is compared to both the context value and a signature sound parameter database stored locally with the sound recognition sensor to identify sounds or speech contained in the analog signal, such that identification of sound or speech is adaptive to the current environmental condition. 
     System Example 
       FIG. 17  is a block diagram of example mobile cellular phone  1000  that utilizes A2I sparse sound features for command recognition. Digital baseband (DBB) unit  1002  may include a digital processing processor system (DSP) that includes embedded memory and security features. Stimulus Processing (SP) unit  1004  receives a voice data stream from handset microphone  1013   a  and sends a voice data stream to handset mono speaker  1013   b . SP unit  1004  also receives a voice data stream from microphone  1014   a  and sends a voice data stream to mono headset  1014   b . Usually, SP and DBB are separate ICs. In most embodiments, SP does not embed a programmable processor core, but performs processing based on configuration of audio paths, filters, gains, etc being setup by software running on the DBB. In an alternate embodiment, SP processing is performed on the same processor that performs DBB processing. In another embodiment, a separate DSP or other type of processor performs SP processing. 
     SP unit  1004  may include an A2I sound extraction module with multiple triggering levels as described above in more detail that allows mobile phone  1000  to operate in an ultralow power consumption mode while continuously monitoring for a spoken word command or other sounds that may be configured to wake up mobile phone  1000 . Robust sound features may be extracted and provided to digital baseband module  1002  for use in classification and recognition of a vocabulary of command words that then invoke various operating features of mobile phone  1000 . For example, voice dialing to contacts in an address book may be performed. Robust sound features may be sent to a cloud based training server via RF transceiver  1006 , as described in more detail above. 
     RF transceiver  1006  is a digital radio processor and includes a receiver for receiving a stream of coded data frames from a cellular base station via antenna  1007  and a transmitter for transmitting a stream of coded data frames to the cellular base station via antenna  1007 . RF transceiver  1006  is coupled to DBB  1002  which provides processing of the frames of encoded data being received and transmitted by cell phone  1000 . 
     DBB unit  1002  may send or receive data to various devices connected to universal serial bus (USB) port  1026 . DBB  1002  can be connected to subscriber identity module (SIM) card  1010  and stores and retrieves information used for making calls via the cellular system. DBB  1002  can also connected to memory  1012  that augments the onboard memory and is used for various processing needs. DBB  1002  can be connected to Bluetooth baseband unit  1030  for wireless connection to a microphone  1032   a  and headset  1032   b  for sending and receiving voice data. DBB  1002  can also be connected to display  1020  and can send information to it for interaction with a user of the mobile UE  1000  during a call process. Touch screen  1021  may be connected to DBB  1002  for haptic feedback. Display  1020  may also display pictures received from the network, from a local camera  1028 , or from other sources such as USB  1026 . DBB  1002  may also send a video stream to display  1020  that is received from various sources such as the cellular network via RF transceiver  1006  or camera  1028 . DBB  1002  may also send a video stream to an external video display unit via encoder  1022  over composite output terminal  1024 . Encoder unit  1022  can provide encoding according to PAL/SECAM/NTSC video standards. In some embodiments, audio codec  1009  receives an audio stream from FM Radio tuner  1008  and sends an audio stream to stereo headset  1016  and/or stereo speakers  1018 . In other embodiments, there may be other sources of an audio stream, such a compact disc (CD) player, a solid state memory module, etc. 
     OTHER EMBODIMENTS 
     While the invention has been described with reference to illustrative embodiments, this description is not intended to be construed in a limiting sense. Various other embodiments of the invention will be apparent to persons skilled in the art upon reference to this description. For example, while a two level (S-trigger, E-trigger) triggering scheme was described herein, in other embodiments a single level may be used, or additional levels may be included by further subdividing operation of the digital domain, for example. 
     In another embodiment, no power triggering is used and all analog and digital logic is powered up all of the time. Extraction of sparse sound features may be used to reduce the size of signature databases, even for a system that is not sensitive to power usage. 
     While use of low power sigma-delta ADC was described herein, other embodiments may use other currently known or later developed ADC technology. 
     Various aspects described herein may be applicable to all manner of sound or voice activated systems, including simple metering or security systems to complex word or phrase activated systems. 
     Some embodiments may include many, or all of the aspects described herein, while other embodiments may include only one or a few aspects. 
     The techniques described in this disclosure may be implemented in analog or mixed signal hardware in which some digital logic is combined with low power analog logic. As used herein, the term “analog logic” may also refer to mixed signal analog circuits that include some amount of digital logic. 
     Some aspects of the techniques described herein may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the software may be executed in one or more processors, such as a microprocessor, application specific integrated circuit (ASIC), field programmable gate array (FPGA), or digital signal processor (DSP). The software that executes the techniques may be initially stored in a computer-readable medium such as compact disc (CD), a diskette, a tape, a file, memory, or any other computer readable storage device and loaded and executed in the processor. In some cases, the software may also be sold in a computer program product, which includes the computer-readable medium and packaging materials for the computer-readable medium. In some cases, the software instructions may be distributed via removable computer readable media (e.g., floppy disk, optical disk, flash memory, USB key), via a transmission path from computer readable media on another digital system, etc. 
     Certain terms are used throughout the description and the claims to refer to particular system components. As one skilled in the art will appreciate, components in digital systems may be referred to by different names and/or may be combined in ways not shown herein without departing from the described functionality. This document does not intend to distinguish between components that differ in name but not function. In the following discussion and in the claims, the terms “including” and “comprising” are used in an open-ended fashion, and thus should be interpreted to mean “including, but not limited to . . . . ” Also, the term “couple” and derivatives thereof are intended to mean an indirect, direct, optical, and/or wireless electrical connection. Thus, if a first device couples to a second device, that connection may be through a direct electrical connection, through an indirect electrical connection via other devices and connections, through an optical electrical connection, and/or through a wireless electrical connection. 
     Although method steps may be presented and described herein in a sequential fashion, one or more of the steps shown and described may be omitted, repeated, performed concurrently, and/or performed in a different order than the order shown in the figures and/or described herein. Accordingly, embodiments of the invention should not be considered limited to the specific ordering of steps shown in the figures and/or described herein. 
     It is therefore contemplated that the appended claims will cover any such modifications of the embodiments as fall within the true scope and spirit of the invention.