Patent Publication Number: US-2018033451-A1

Title: Identification by sound data

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation application under 35 U.S.C. §120 of U.S. application Ser. No. 14/420,858, filed on Feb. 10, 2015, which is a U.S. National Stage filing under 35 U.S.C. §371 of International Application No. PCT/US14/33525, filed on Apr. 9, 2014. U.S. application Ser. No. 14/420,858 and International Application No. PCT/US14/33525 are hereby incorporated by reference in their entirety. 
    
    
     BACKGROUND 
     Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section. 
     Behaviors and activities may be monitored for the purpose of influencing and aiding groups or individuals. Monitoring may be performed by various methods. Some monitoring may include human observation and postal interception. Other monitoring may include observation from a distance by means of electronic devices such as cameras, or interception of electronically transmitted information such as internet traffic and phone calls. 
     SUMMARY 
     In an example, methods effective to identify an individual are generally described. The methods may include receiving sound data at a microphone. A processor may be configured to be in communication with the microphone. The methods may include the processor determining a name from the sound data. The methods may also include the processor generating a query based on the name. The methods may include the processor sending the query to a social network database. The methods may also include the processor receiving a response to the query from the social network database. The methods may include the processor identifying the individual based on the response. 
     In an example, methods effective to identify a group that includes at least a first and a second individual are generally described. The methods may include a processor generating first sound data at a first microphone at a first location. The first sound data may relate to a first voice and a second voice. The methods may include the processor identifying first voice data in the first sound data. The methods may include the processor identifying second voice data in the first sound data. The methods may include the processor generating second sound data at a second microphone at a second location. The second sound data may relate to the first voice and the second voice. The methods may include the processor identifying third voice data in the second sound data. The methods may include the processor identifying fourth voice data in the second sound data. The methods may include the processor grouping the first, second, third and fourth voice data into a group. The methods may include the processor determining a first name from the first sound data. The methods may include the processor determining a second name from the second sound data. The methods may include the processor generating a query based on the first and second name. The methods may include the processor sending the query to a social network database. The methods may include the processor receiving a response to the query from the social network database. The methods may include the processor identifying that the first individual is in the group based on the response. The methods may include the processor identifying that the second individual is in the group based on the response. 
     In an example, systems configured to identify an individual are generally described. The systems may include a microphone and a processor. The microphone may be configured to generate sound data. The processor may be configured to be in communication with the microphone, a memory, and a social network database. The processor may be further configured to receive the sound data from the microphone. The processor may be configured to determine a name related to the individual from the sound data. The processor may be configured to generate a query based on the name. The processor may be configured to send the query to a social network database. The processor may be configured to receive a response to the query from the social network database. The processor may be configured to identify the individual based on the response. 
     In an example, systems effective to identify a group that includes at least a first and a second individual are generally described. The systems may include a first microphone, a second microphone and a processor. The first microphone may be at a first location. The first microphone may be configured to generate first sound data. The second microphone may be at a second location. The second microphone may be configured to generate second sound data. The processor may be configured to be in communication with the first microphone, the second microphone, a memory, and a social network database. The processor may be configured to receive the first sound data from the first microphone. The processor may be configured to identify first voice data in the first sound data. The processor may be configured to identify second voice data in the first sound data. The processor may be configured to receive the second sound data from the second microphone. The processor may be configured to identify third voice data in the second source data. The processor may be configured to identify fourth voice data in the second source data. The processor may be configured to group the first, second, third and fourth voice data into a group. The processor may be configured to determine a first name from the first sound data. The processor may be configured to determine a second name from the second sound data. The processor may be configured to generate a query based on the first and second name. The processor may be configured to send the query to a social network database. The processor may be configured to receive a response to the query from the social network database. The processor may be configured to identify that the first individual is in the group based on the response. The processor may be configured to identify that the second individual is in the group based on the response. 
     The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  illustrates an example system effective to implement identification by sound data; 
         FIG. 2  illustrates the example system of  FIG. 1  with additional details relating to identifying individuals within a group using a query sent to a social network database; 
         FIG. 3  illustrates the example system of  FIG. 1  and  FIG. 2  with additional details relating to identifying a group; 
         FIG. 4  depicts a flow diagram for example processes for implementing identification by sound data; 
         FIG. 5  illustrates computer program products effective to implement identification by sound data; and 
         FIG. 6  is a block diagram illustrating an example computing device that is arranged to implement identification by sound data, 
     
    
    
     all arranged in accordance with at least some embodiments described herein. 
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless context dictates otherwise. The illustrative embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the Figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are explicitly contemplated herein. 
     This disclosure is generally drawn, inter alia, to methods, apparatus, systems, devices, and computer program products related to identification by sound data. 
     Briefly stated, technologies are generally described for systems, devices and methods effective to identify an individual. In some examples, a microphone may receive sound data such as sound that may be present in a mall. A processor that may be in communication with the microphone, may determine a name from the sound data. Stated differently, the processor may determine that the name is part of or included in the sound data. The processor may generate a query based on the name and may send the query to a social network database. The processor may receive a response to the query from the social network database and may identify the individual based on the response. 
     As described in more detail below, in an example, an identification of a group, or an individual within a group, may be determined with use of sound data. Microphones may monitor sound, such as voices, that is generated or produced in an area. Individuals may be determined from the voices included in the sound detected in the area. Movement of the individuals may be tracked within the area by analyzing sound received by microphones at different locations within the area. Groups of individuals may be determined by tracking individuals that move together. Words spoken within a group may be recognized and analyzed to determine names of the individuals in the group. The names of the individuals may be used as queries to a social network database to identify individuals. 
       FIG. 1  illustrates an example system  100  effective to implement identification by sound data, arranged in accordance with at least some embodiments described herein. In some examples, system  100  may include one or more microphones  104 , a processor  106 , and a memory  110 . Microphone  104  may be a microphone in a mobile device such as a cell phone, or tablet device. Microphone  104  may include more than one microphone unit such as multiple microphones arranged in an array. Processor  106  may be in communication with microphones  104  over network  108 . Processor  106  may also be in communication with memory  110 . Memory  110  may include identity instructions  112 , diarization instructions  140  and speech recognition instructions  142 . Processor  106  may also be in communication with a social network  170  over network  108 . As discussed in more detail below, processor  106  may determine an identification of a group or individual within a group by analyzing sound data. 
     In one example, microphones  104  may detect and receive sound  102 . Sound  102  received by microphones  104  may be waves perceptible to humans such as waves of the frequencies from about 20 Hz to about 20,000 Hz. Microphones  104  may generate sound data  114  in response to sound  102 . Sound  102  may be related to voices, or individuals speaking, within a zone  150  that may be in a sound range of microphones  104 . Processor  106  may receive sound data  114  from microphones  104  over network  108 . Processor  106 , by executing diarization instructions  140 , may analyze sound data  114  to determine a number of voices present in sound data  102  and to determine voice data in sound data  114  related to each individual voice within sound  102 . Processor  106  may execute speaker diarization on sound data  114  to separate sound data  114  into voice data related to each individual voice in sound  102 . For example, processor  106 , executing diarization instructions  140  on sound data  114 , may determine there are four voices present in sound  102 . Processor  106  executing diarization instructions  140  may segregate sound data  114  into voice data associated with each of the four voices determined to be present in sound  102 . Processor  106  may relate each determined voice and the segregated voice data to an individual to generate set of individuals  116 . Processor  106  may assign an identification number to each individual in set of individuals  116 . Set of individuals  116  are represented in  FIG. 1  by profiles with identification numbers  131 ,  132 ,  133 ,  134 ,  135 ,  136 ,  137 ,  138 , and  139 . For example, if processor  106  determined nine voices were present in sound  102 , processor may segregate sound data  114  into voice data related to each of the nine determined voices in sound data  102 . Processor  106  may assign identification number  131  to an individual associated with voice data related to the first voice, assign identification number  132  to an individual associated with voice data related to the second voice, assign identification number  133  to an individual associated with voice data related to the third voice, and so on, until an identification number is assigned to each of the nine determined voices in sound  102 . 
     Processor  106  may assign or identify a location corresponding to each microphone  104  such that sound  102  monitored by each microphone  104  may be related to the corresponding location of the respective microphone. For example, as shown at  155 , four microphones  104  (shown as  104   a ,  104   b ,  104   c ,  104   d ) at four locations may be within zone  150  with corresponding monitored sounds  102  (including  102   a ,  102   b ,  102   c , and  102   d ) and sound data  114  (including  114   a ,  114   b ,  114   c , and  114   d ). 
     Processor  106  may determine tracking data  118  by analyzing sound data  114 . Processor  106  may determine a location for each individual, in set of individuals  116 , based on locations of microphones  104  and sound data  114  received by microphones  104  at each location. Processor  106  may track each individual represented by identification numbers  131 - 139 , as each individual moves around within zone  150  and determine tracking data  118 . Processor  106 , having assigned an identification number to each individual in set of individuals  116 , may store a record of tracking data  118  for each individual in memory  110 . Each identification number in set of individuals  116  may relate to voice data in sound data  114 . Tracking data  118  may be associated with voice data related to each identification number and the respective microphone  104  ( 104   a ,  104   b ,  104   c ,  104   d ) from which processor  106  receives sound data  114 . Tracking data  118  may be stored periodically in memory  110  and may include data related to a location of an individual at successive times. Tracking data  118  may be stored at successive time intervals of about 0.5 seconds to about 10 seconds for any time period. 
     Processor  106  may group voice data of individuals into groups  120 ,  122 ,  124  who may be travelling together. Processor  106  may determine groups  120 ,  122 ,  124  by tracking individuals and analyzing tracking data  118  to determine individuals that move together in a group within zone  150  of microphones  104 . 
     In an example, as depicted at  156 , individuals with identification numbers  131 - 139  may all be detected as speaking at a first time (0 sec) and location corresponding to microphone  104   a . Identification numbers  131 ,  133 ,  134 ,  136 ,  137 , and  139  may be detected as speaking at a second location corresponding to microphone  104   b  at a successive time (3 sec). Tracking data for identification numbers  133 ,  134 ,  136 , and  139  may be detected to be speaking at a third location corresponding to microphone  104   c  at a further successive time (6 sec). Processor  106  may determine identification numbers  133 ,  134 ,  136 , and  139  have the same tracking data. Processor  106  may group identification numbers  133 ,  134 ,  136 , and  139  and corresponding voice data into group  122  due to tracking data  118  for each identification number  133 ,  134 ,  136 , and  139  including the same location and changes in location for three subsequent times. In another example, a group may be determined with only one individual when the individual is monitored while speaking on a phone. In one example, all individuals in set of individuals  116  may be determined as groups of one if no tracking data  118  is determined. 
     Upon grouping voice data associated with individuals and tracking data  118 , processor  106  may analyze sound data  114  related to the group. Sound data  114  related to the group may include sound data  114  related to each individual determined to be in the group. In an example, processor  106  may determine individuals (e.g., identification numbers profiles  133 ,  134 ,  136 ,  139 ) form group  122 . Processor  106  may analyze sound data  114  related to group  122  and identification numbers  133 ,  134 ,  136 , and  139 . Processor  106  may exclude sound data related to individuals not in group  122  such as identification numbers  131 ,  132 ,  135 ,  137 , and  138  in groups  120 ,  124 . Processor  106  may execute speech recognition instructions  142  on sound data  114  related to group  122  to identify names within sound data  114 . In an example, speech recognition instructions  142  may convert sound data  114  related to group  122  into text, separate the text into words, and separate names from the words. 
     Identified names from sound data  114  may include first names of individuals within group  122 . Identified names from sound data  114  related to group  122  may also include names of people not present in the group, for example, famous people such as politicians, actors, sports stars etc. Processor  106  may filter identified names to remove names of famous people and produce a set of filtered identified names  160 . Filtering may be accomplished by comparing identified names in sound data  114  to a list of names where the list may include famous names. 
     Speech recognition instructions  142  may include natural language processing (NPL) algorithms which may use grammar rules to extract meaning from words in sound data  114 . In an example, if sound data  114  relates to the words “Riley did you” spoken by an individual corresponding to identification number  133  of group  122 , then processor  106  may determine that one of identification numbers  134 ,  136 , or  139  may correspond to an individual named Riley. 
     In some examples, processor  106  may identify a name of an individual by identifying names spoken by individuals in sound data  114 . For example, sound data  114  may relate to names spoken by identification number  133  in group  122  and include “Tom”, “Mike”, and “Riley”. Set of filtered identified names  160  may include “Tom”, Mike”, “Riley” and “Sally”. Processor  106  may identify identification number  133  as “Sally” based on sound data  144  of identification number  133  including all names in set of filtered identified names  160  except one—“Sally.” 
     In some examples, processor  106  may not determine a specific name for each individual within group  122  due to insufficient sound data  114 . Processor  106  may identify set of filtered identified names  160  related to all the individuals in group  122  rather than identify a specific name for each identification number  133 ,  134 ,  136 , and  139 . For example, processor  106  may determine that set of filtered identified names  160  may include “Tom”, Mike”, “Riley” and “Sally”. Sound data  114  may not include enough other information so that one name in set of filtered identified names  160  may be determined for one identification number in group  122 . As described in more detail below, upon determining set of filtered identified names  160  related to individuals in group  122 , processor  106  may send a query to a social network database  170  with set of filtered identified names  160  to identify one or more individuals in group  122 . 
       FIG. 2  illustrates example system  100  of  FIG. 1  with additional details relating to identifying individuals within a group using a query sent to a social network database, arranged in accordance with at least some embodiments described herein.  FIG. 2  is substantially similar to  FIG. 1 , with additional details. Those components in  FIG. 2  that are labeled identically to components of  FIG. 1  will not be described again for the purposes of clarity. 
     After processor  106  determines set of filtered identified names  160  related to individuals  116  in group  122 , processor  106  may generate and send a query  230  to social network database  170 . For example, processor  106  may use an application programming interface (API) of social network database  170  to generate query  230 . Social network database  170  may be a database related to any social or professional network such as FACEBOOK, LINKED-IN, etc. Social network database  170  may include social network data related to names of social network members. Processor  106  may also base query  230  sent to social network database  170  on a location of group  122  or microphones  104 . For example, processor  106  may be analyzing sound data  114  generated by a microphone  104  located in a mall in Pennsylvania. Processor  106  may send query  230  to social network database  170  for names of members of social network database  170  that reside within Pennsylvania, or for members in a radius of 0-50 miles from the mall in Pennsylvania. In response to query  230  from processor  106 , set of filtered identified names  160  may be compared to names in social network database  170  such as by processor  106  or by a processor associated with social network database  170 . The comparison may be made on a first name basis. For example, if set of identified filtered names  160  includes names  202 ,  204 ,  206 , and  208 , processor  106  may analyze social network database  170  for members and connected members who are connected to members with the first names  202 ,  204 ,  206 , and  208 . 
     In another example, set of filtered identified names  160  may include names  202 ,  204 ,  206 , and  208  related to identification numbers  133 ,  134 ,  136 , and  139  respectively. Processor  106  may send query  230  to social network database  170  to compare names  202 ,  204 ,  206 , and  208  to social network database  170 . Query  230  may include names  202 ,  204 ,  206 ,  208 . Processor  106  may receive a response  232  to query  230 . Response  232  may indicate that data related to a social network member  210  includes and is connected to names  202 ,  204 ,  206 , and  208 . Processor  106  may then determine that social network member  210  is in group  122  because, in part, sound data  114  included names  202 ,  204 ,  206 ,  208  and social network member  210  includes and is connected to names  202 ,  204 ,  206 ,  208  in his/her social network. For example, social network member  210  may be “Sally Smith”, name  202  may be “Sally” and Sally the individual may be identified as corresponding to identification number  133 . 
     Response  232  from social network database  170  may depend on the size of social network database  170 , the number of names in query  230 , and any other query information which may refine query  230 , such as location detail. A larger social network database  170  may require more names to determine one single social network member with names in set of filtered identified names  160  in his/her social network. The required amount of names to identify an individual may vary depending on the size of group  122 , the size of social network database  170  and the popularity of the names. For example, more popular names may be associated with more social network members. An individual may be identified for social network database  170  with as little as one filtered identified name. 
     Response  232  may identify social network members corresponding to each name  202 ,  204 ,  206 , and  208 . For example, as depicted at  222 , response  232  may identify social network member  210  as name  202 , social network member  216  as name  204 , social network member  214  as name  206  and social network member  212  as name  208 . As discussed above, processor  106  may identify members of group  122  as including social network members  210 ,  216 ,  214  and  212 . Social network database  170  may include a first and last name for each social network member  210 ,  216 ,  214 , and  212 . Processor  106  may analyze social network database  170  related to social network members  210 ,  216 ,  214 , and  212  for marketing or advertising purposes. Processor  106  may then send advertising content to a device associated with any of social network members  210 ,  216 ,  214 , and  212  in group  122 . 
     Response  232  may include other information related to social network members  210 ,  216 ,  214 , and  212 . For example, processor  106  may determine that social network members  210 ,  216 ,  214 , and  212  all played together on a high school football team. In the example, processor  106  may send an advertisement for a football fantasy league to a device associated with social network members  210 ,  216 ,  214 , and  212  in response. 
     Memory  110  may include a database of specific advertising content. Processor  106  may analyze social network member information for specific social network member criteria dependent on the specific advertising content. For example, ladies business clothing may be best advertised to social network members identified as working women. Memory  110  may also have advertising content which is not dependent on specific social network member criteria, for example, a lunch special at a local restaurant may appeal to anyone wanting lunch. 
       FIG. 3  illustrates example system  100  of  FIG. 1  and  FIG. 2  with additional details relating to identifying a group by sound data, arranged in accordance with at least some embodiments described herein.  FIG. 3  is substantially similar to  FIG. 1  and  FIG. 2 , with additional details. Those components in  FIG. 3  that are labeled identically to components of  FIG. 1  and  FIG. 2  will not be described again for the purposes of clarity. 
     As discussed above, processor  106  may not determine a specific name for each identified individual within group  122  due to insufficient sound data  114 . For example, processor  106  may not be able to associate social network members with a corresponding identification number. But, processor  106  may be able to identify social network members in group  122  (e.g. Sally, Riley, Tom and Mike are in group  122  but we don&#39;t know the respective identification numbers). Processor  106  may send query  230  to social network database  170  to compare set of filtered identified names  160  of group  122  to social network database  170 . In response to query  230  from processor  106 , filtered identified names  160  may be compared to social network database  170 . Processor  106  may receive response  232  to query  230  that may include identifications of social network members  210 ,  216 ,  214 , and  212  identified as names  202 ,  204 ,  206 , and  208 . Processor  106  may then determine social network members  210 ,  212 ,  214 , and  216  are in group  122 . However, processor  106  may not be able to associate each social network member  210 ,  212 ,  214 ,  216  to an identification number  133 ,  134 ,  136 ,  139  in group  122 . Processor  106  may identify advertising content based on group  122  and may send advertising content to a device associated with any social network members  210 ,  216 ,  214 , and  212  in group  122 . 
     Among other potential benefits, a system in accordance with the disclosure may identify individuals and groups for specific and individualized advertising. Another potential benefit of the disclosed system is identification through sound data may be less expensive compared to video identification methods. Microphones may be less expensive than cameras and each microphone may cover a larger area than a camera. An individual does not need to be looking at a microphone to produce sound data that may be usable. 
       FIG. 4  depicts a flow diagram, for example, processes  400  for implementing identification by sound data, arranged in accordance with at least some embodiments described herein. The process in  FIG. 4  could be implemented using, for example, system  100  discussed above. An example method for implementing identification by sound data may include one or more operations, actions, or functions as illustrated by one or more of blocks S 2 , S 4 , S 6 , S 8 , S 10  and/or S 12 . Although illustrated as discrete blocks, various blocks may be divided into additional blocks, combined into fewer blocks, or eliminated, depending on the desired implementation. Processing may begin at block S 2 , “Receive sound data at a microphone.” A microphone may receive sound data. The microphone may be a microphone in a mobile device such as a cell phone, or tablet device. The microphone may include multiple microphone units arranged in an array. The sound data received may relate to sound perceptible to humans of the frequencies from about 20 Hz to about 20,000 Hz. The sound may be produced by voice data, such as individuals speaking, within a zone which may be a sound range of the microphone. 
     Processing may continue from block S 2  to block S 4 , “Determine, by a processor configured to be in communication with the microphone, a name from the sound data.” At block S 4 , the processor may determine a name from the sound data. The processor may be configured to be in communication with the microphone. The processor may receive the sound data from the microphone over a network. The processor may execute diarization instructions on the sound data and may analyze the sound data to determine voice data. The processor may identify individuals by the determined voice data. The processor may assign an identification number to the voice data. The processor may execute speech recognition instructions on the sound data to identify a name or names within the sound data. The speech recognition instructions may convert the sound data into text, separate the text into words, and separate a name or names from the words. 
     The processor may filter the names. The filtering may be accomplished by comparing names against a list of names. The processor may also further analyze the sound data and the name or names to determine names associated with individuals. For example, the speech recognition instructions may include natural language processing algorithms which may use grammar to extract meaning from words in the sound data. 
     Processing may continue from block S 4  to block S 6 , “Generate, by the processor, a query based on the name.” At block S 6 , the processor may generate a query based on the name or names determined. The query may include the name and other names detected in the sound data. The processor may also base the query on a location. 
     Processing may continue from block S 6  to block S 8 , “Send, by the processor, the query to a social network database.” At block S 8 , the processor may send a query to a social network database. The social network database may be a database related to any social or professional network such as FACEBOOK, LINKED-IN, etc. 
     Processing may continue from block S 8  to block S 10 , “Receive, by the processor, a response to the query from the social network database.” The processor may receive a response from the social network database. The response may include social network members identified as the name or names. 
     Processing may continue from block S 10  to block S 12 , “Identify, by the processor, the individual based on the response.” The processor, in response to receiving the response from the social network database, may identify the individual. The identification may include a first and last name. The processor may send advertising content to a device associated with the social network member. 
       FIG. 5  illustrates computer program products  500  effective to implement identification by sound data, arranged in accordance with at least some embodiments described herein. Computer program product  500  may include a signal bearing medium  502 . Signal bearing medium  502  may include one or more instructions  504  that, when executed by, for example, a processor, may provide the functionality described above with respect to  FIGS. 1-4 . 
     In some implementations, signal bearing medium  502  may encompass a computer-readable medium  506 , such as, but not limited to, a hard disk drive (HDD), a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, memory, etc. In some implementations, signal bearing medium  502  may encompass a recordable medium  508 , such as, but not limited to, memory, read/write (R/W) CDs, R/W DVDs, etc. In some implementations, signal bearing medium  502  may encompass a communications medium  510 , such as, but not limited to, a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communication link, a wireless communication link, etc.). Thus, for example, computer program product  500  may be conveyed to one or more modules of the system  100  by an RF signal bearing medium  502 , where the signal bearing medium  502  is conveyed by a wireless communications medium  510  (e.g., a wireless communications medium conforming with the IEEE 802.11 standard). 
       FIG. 6  is a block diagram illustrating an example computing device  600  that is arranged to implement identification by sound data, arranged in accordance with at least some embodiments presented herein. In a very basic configuration  602 , computing device  600  typically includes one or more processors  604  and a system memory  606 . A memory bus  608  may be used for communicating between processor  604  and system memory  606 . 
     Depending on the desired configuration, processor  604  may be of any type including but not limited to a microprocessor (μP), a microcontroller (μC), a digital signal processor (DSP), or any combination thereof. Processor  604  may include one or more levels of caching, such as a level one cache  610  and a level two cache  612 , a processor core  614 , and registers  616 . An example processor core  614  may include an arithmetic logic unit (ALU), a floating point unit (FPU), a digital signal processing core (DSP core), or any combination thereof. An example memory controller  618  may also be used with processor  604 , or in some implementations memory controller  618  may be an internal part of processor  604 . 
     Depending on the desired configuration, system memory  606  may be of any type including but not limited to volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.) or any combination thereof. System memory  606  may include an operating system  620 , one or more applications  622 , and program data  624 . 
     Application  622  may include identification by sound data algorithm  626  that is arranged to perform the functions as described herein including those described previously with respect to  FIGS. 1-5 . Program data  624  may include identification by sound data  628  that may be useful for identification by sound data as is described herein. In some embodiments, application  622  may be arranged to operate with program data  624  on operating system  620  such that identification by sound data may be provided. This described basic configuration  602  is illustrated in  FIG. 6  by those components within the inner dashed line. 
     Computing device  600  may have additional features or functionality, and additional interfaces to facilitate communications between basic configuration  602  and any required devices and interfaces. For example, a bus/interface controller  630  may be used to facilitate communications between basic configuration  602  and one or more data storage devices  632  via a storage interface bus  634 . Data storage devices  632  may be removable storage devices  636 , non-removable storage devices  638 , or a combination thereof. Examples of removable storage and non-removable storage devices include magnetic disk devices such as flexible disk drives and hard-disk drives (HDDs), optical disk drives such as compact discs (CDs) drives or digital versatile disk (DVDs) drives, solid state drives (SSDs), and tape drives to name a few. Example computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. 
     System memory  606 , removable storage devices  636  and non-removable storage devices  638  are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by computing device  600 . Any such computer storage media may be part of computing device  600 . 
     Computing device  600  may also include an interface bus  640  for facilitating communication from various interface devices (e.g., output devices  642 , peripheral interfaces  644 , and communication devices  646 ) to basic configuration  602  via bus/interface controller  630 . Example output devices  642  include a graphics processing unit  648  and an audio processing unit  650 , which may be configured to communicate to various external devices such as a display or speakers via one or more A/V ports  652 . Example peripheral interfaces  644  include a serial interface controller  654  or a parallel interface controller  656 , which may be configured to communicate with external devices such as input devices (e.g., keyboard, mouse, pen, voice input device, touch input device, etc.) or other peripheral devices (e.g., printer, scanner, etc.) via one or more I/O ports  658 . An example communication device  646  includes a network controller  660 , which may be arranged to facilitate communications with one or more other computing devices  662  over a network communication link via one or more communication ports  664 . 
     The network communication link may be one example of a communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and may include any information delivery media. A “modulated data signal” may be a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), microwave, infrared (IR) and other wireless media. The term computer readable media as used herein may include both storage media and communication media. 
     Computing device  600  may be implemented as a portion of a small-form factor portable (or mobile) electronic device such as a cell phone, a personal data assistant (PDA), a personal media player device, a wireless web-watch device, a personal headset device, an application specific device, or a hybrid device that include any of the above functions. Computing device  600  may also be implemented as a personal computer including both laptop computer and non-laptop computer configurations. 
     The present disclosure is not to be limited in terms of the particular embodiments described in this application, which are intended as illustrations of various aspects. Many modifications and variations can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. Functionally equivalent methods and apparatuses within the scope of the disclosure, in addition to those enumerated herein, will be apparent to those skilled in the art from the foregoing descriptions. Such modifications and variations are intended to fall within the scope of the appended claims. The present disclosure is to be limited only by the terms of the appended claims, along with the full scope of equivalents to which such claims are entitled. It is to be understood that this disclosure is not limited to particular methods, reagents, compounds compositions or biological systems, which can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. 
     With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity. 
     It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as “open” terms (e.g., the term “including” should be interpreted as “including but not limited to,” the term “having” should be interpreted as “having at least,” the term “includes” should be interpreted as “includes but is not limited to,” etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation, no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases “at least one” and “one or more” to introduce claim recitations. However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim recitation to embodiments containing only one such recitation, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and/or “an” should be interpreted to mean “at least one” or “one or more”); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should be interpreted to mean at least the recited number (e.g., the bare recitation of “two recitations,” without other modifiers, means at least two recitations, or two or more recitations). Furthermore, in those instances where a convention analogous to “at least one of A, B, and C, etc.” is used, in general, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, and C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to “at least one of A, B, or C, etc.” is used, in general, such a construction is intended in the sense one having skill in the art would understand the convention (e.g., “a system having at least one of A, B, or C” would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase “A or B” will be understood to include the possibilities of “A” or “B” or “A and B.” 
     As will be understood by one skilled in the art, for any and all purposes, such as in terms of providing a written description, all ranges disclosed herein also encompass any and all possible subranges and combinations of subranges thereof. Any listed range can be easily recognized as sufficiently describing and enabling the same range being broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range discussed herein can be readily broken down into a lower third, middle third and upper third, etc. As will also be understood by one skilled in the art all language such as “up to,” “at least,” “greater than,” “less than,” and the like include the number recited and refer to ranges which can be subsequently broken down into subranges as discussed above. Finally, as will be understood by one skilled in the art, a range includes each individual member. Thus, for example, a group having 1-3 cells refers to groups having 1, 2, or 3 cells. Similarly, a group having 1-5 cells refers to groups having 1, 2, 3, 4, or 5 cells, and so forth. 
     While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.