Patent Publication Number: US-2015066925-A1

Title: Method and Apparatus for Classifying Data Items Based on Sound Tags

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to classifying data items in mobile devices. More specifically, the present disclosure relates to classifying data items based on context information of mobile devices. 
     BACKGROUND 
     In recent years, the use of mobile devices such as smartphones and tablet computers has become widespread. These devices typically allow users to perform a variety of functions such as data and/or voice communication, browsing the Internet, taking photographs or videos, uploading blog posts and SNS (Social Network Service) posts to the Internet, making phone or video calls, sending e-mails, text messages, and MMS messages, generating memos, etc. Due to such convenient features, users typically carry such a mobile device in person most of the time. 
     Conventional mobile devices are often used to capture data such as photographs, sound clips, etc. that can be stored in the mobile devices. In the case of photographs, such mobile devices may tag photographs with GPS (Global Positioning System) location information to indicate the locations where the photographs were taken. By using the GPS location information, photographs taken in a specified geographic location may be organized into a same group. In addition, photographs may also be tagged with time at which the photographs were taken. The photographs may then be organized according to the time information. 
     However, conventional mobile devices may capture data items in a variety of contexts. For example, photographs may be taken in a same location (e.g., a building) but have different contexts (e.g., a restaurant and a convenience store in a building). Also, photographs may be taken at different locations but in a similar context such as restaurants in different locations. In such cases, mobile devices may not be able to organize the photographs to sufficiently reflect similar or different contexts. 
     SUMMARY 
     The present disclosure provides methods and apparatus for classifying data items based on a sound tag in mobile devices. 
     According to one aspect of the present disclosure, a method for grouping data items in a mobile device is disclosed. In this method, a plurality of data items and a sound tag associated with each of the plurality of data items are stored, and the sound tag includes a sound feature extracted from an input sound indicative of an environmental context for the data item. Further, the method may include generating a new data item, receiving an environmental sound, generating a sound tag associated with the new data item by extracting a sound feature from the environmental sound, and grouping the new data item with at least one of the plurality of data items based on the sound tags associated with the new data item and the plurality of data items. This disclosure also describes apparatus, a device, a system, a combination of means, and a computer-readable medium relating to this method. 
     According to another aspect of the present disclosure, a method for grouping data items in a mobile device is disclosed. This method includes generating a first data item, receiving a first environmental sound, and generating a first sound tag by extracting a first sound feature from the first environmental sound. Further, the method may include generating a second data item, receiving a second environmental sound, generating a second sound tag by extracting a second sound feature from the second environmental sound, and grouping the first and second data items based on the first and second sound tags. This disclosure also describes apparatus, a device, a system, a combination of means, and a computer-readable medium relating to this method. 
     According to still another aspect of the present disclosure, a mobile device includes a storage unit, a data item generator, a sound sensor, a sound tag generator, and a grouping unit. The storage unit is configured to store a plurality of data items and a sound tag associated with each of the plurality of data items, and the sound tag includes a sound feature extracted from an input sound indicative of an environmental context for the data item. The data item generator is configured to generate a new data item. The sound sensor is configured to receive an environmental sound. The sound tag generator is configured to generate a sound tag associated with the new data item by extracting a sound feature from the environmental sound. The grouping unit is configured to group the new data item with at least one of the plurality of data items based on the sound tags associated with the new data item and the plurality of data items. 
     According to yet another aspect of the present disclosure, a mobile device includes a data item generator, a sound sensor, a sound tag generator, and a grouping unit. The data item generator is configured to generate a first data item and a second data item. The sound sensor is configured to receive a first environmental sound and a second environmental sound. The sound tag generator is configured to generate a first sound tag by extracting a first sound feature from the first environmental sound and a second sound tag by extracting a second sound feature from the second environmental sound. The grouping unit is configured to group the first and second data items based on the first and second sound tags. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments of the inventive aspects of this disclosure will be understood with reference to the following detailed description, when read in conjunction with the accompanying drawings. 
         FIG. 1  illustrates a mobile device configured to group data items including a plurality of photographs, a memo, a blog post, and an SNS post generated in a specified geographical location based on environmental sounds, according to one embodiment of the present disclosure. 
         FIG. 2  illustrates a mobile device configured to group data items including a plurality of photographs, a memo, a blog post, and an SNS post generated in three different buildings, according to one embodiment of the present disclosure. 
         FIG. 3  illustrates a block diagram of a mobile device configured to generate and group data items by classifying the data items based on sound tags according to one embodiment of the present disclosure. 
         FIG. 4  is a flowchart of a method performed in a mobile device for grouping data items based on sound tags indicating environmental contexts according to one embodiment of the present disclosure. 
         FIG. 5  illustrates generating a sound tag including a sound feature, an audio group identifier, and a context label from an environmental sound according to one embodiment of the present disclosure. 
         FIG. 6  illustrates a flowchart of an exemplary method performed in a mobile device for extracting an audio fingerprint from an environmental sound as a sound feature according to one embodiment of the present disclosure. 
         FIG. 7  illustrates a flowchart of a method performed in a mobile device for extracting an MFCC vector from an environmental sound as a sound feature according to one embodiment of the present disclosure. 
         FIG. 8  illustrates a more detailed block diagram of a sound tag generator and a control unit in a mobile device for classifying or grouping data items by generating a sound tag including a sound feature, an audio group identifier, and a context label for each data item, according to one embodiment of the present disclosure. 
         FIG. 9  illustrates an exemplary tagged data item in which a data item is appended with a sound tag including a sound feature, an audio group identifier, and a context label, according to one embodiment of the present disclosure. 
         FIG. 10  illustrates grouping a selected data item with other data items by determining a similarity value between a sound feature associated with the selected data item and each sound feature associated with the other data items, according to one embodiment of the present disclosure. 
         FIG. 11  illustrates a selected data item and other data items displayed as a single group on a display screen of a mobile device, according to one embodiment of the present disclosure. 
         FIG. 12  is an exemplary context label database illustrating context labels for a plurality of input audio group identifiers according to one embodiment of the present disclosure. 
         FIG. 13  illustrates a plurality of groups of data items displayed on a display screen of a mobile device based on audio group identifiers in sound tags associated with the data items, according to one embodiment of the present disclosure. 
         FIG. 14  illustrates a plurality of groups of data items displayed on a display screen of a mobile device based on context labels in sound tags associated with the data items in another embodiment of the present disclosure. 
         FIG. 15  illustrates a block diagram of an exemplary mobile device in which the methods and apparatus for classifying data items based on a sound tag may be implemented according to some embodiments. 
     
    
    
     DETAILED DESCRIPTION 
       FIG. 1  illustrates a mobile device  140  configured to group data items including a plurality of photographs  110 ,  120 , and  130 , a memo  112 , a blog post  122 , and an SNS post  132  generated in a specified geographical location  100  based on environmental sounds, according to one embodiment of the present disclosure. As illustrated, the specified geographical location  100  is at or near a building  102  and may be classified or identified by the mobile device  140  as a same location. At various locations within the specified geographical location  100 , a user may operate the mobile device  140  to generate the data items. 
     For each of the data items generated at various locations, the mobile device  140  may be configured to receive or capture an environmental sound to indicate the environmental context. In one embodiment, the mobile device  140  may be configured to capture an environmental sound associated with a data item for a predetermined period of time. Based on the captured environmental sound, a sound tag indicating an environmental context of the associated data item may be generated in the mobile device  140 . The data items may then be classified by the mobile device  140  into a plurality of groups based on the sound tags. 
     In the illustrated embodiment, a user may operate the mobile device  140  in various locations within the specified geographic location  100  such as outdoors in front of the building  102 , a restaurant inside the building  102 , and a grocery market inside the building  102 . The various locations may have different environmental contexts. In the outdoor case, the user operates the mobile device  140  to generate the data items including the photograph  110  and the memo  112 . For each of these data items, the mobile device  140  may capture an environmental sound to generate a sound tag indicating an outdoor environment, which may include outdoor sounds such as wind noise, traffic sound, pedestrian sound, etc. 
     When the user is in the restaurant, the user may operate the mobile device  140  to generate the data items including the photograph  120  and the blog post  122 . For each of these data items, the mobile device  140  may capture an environmental sound to generate a sound tag indicating a restaurant environment, which may include sounds such as sounds of utensils, music, food ordering, etc. In the case of the grocery market, the user may operate the mobile device  140  to generate the data items including the photograph  130  and the SNS post  132 . For each of these data items, the mobile device  140  may capture an environmental sound to generate a sound tag indicating a grocery market environment, which may include sounds such as sounds of shopping carts, cash registers, announcements, etc. 
     Based on the sound tags, the mobile device  140  may classify or group the data items into groups A, B, and C according to the three different environmental contexts. For example, the data items including the photograph  110  and the memo  112  may be grouped together in group A according to the sound tags indicating the outdoor environment. On the other hand, the data items including the photograph  120  and the blog post  122  may be grouped in group B according to the sound tags indicating the restaurant environment, while the data items including photograph  130  and the SNS post  132  may be grouped together in group C according to the sound tags indicating the grocery market environment. Accordingly, data items of a same data type as well as data items of different data types, which are generated within the specified geographical location  100 , may be grouped into different groups according to their environmental contexts. 
       FIG. 2  illustrates the mobile device  140  configured to group data items including a plurality of photographs  212 ,  222 , and  232 , a memo  214 , a blog post  224 , and an SNS post  234  generated in three different buildings  210 ,  220 , and  230 , according to one embodiment of the present disclosure. The three buildings  210 ,  220 , and  230  are located in three different geographical locations and are classified or identified by the mobile device  140  as being in different locations. The buildings  210 ,  220 , and  230  may include premises with a similar environmental context. 
     As illustrated, the buildings  210 ,  220 , and  230  include billiard rooms in which the user may operate the mobile device  140  to generate the data items having a similar environmental context (e.g., billiard room). In a billiard room located in the building  210 , the user may operate the mobile device  140  to generate the data items including the photograph  212  and the memo  214 . While in another billiard room located in the building  220 , the user may operate the mobile device  140  to generate the data items including the photograph  222  and the blog post  224 . Inside yet another billiard room within the building  230 , the user may operate the mobile device  140  to generate the data items including the photograph  232  and the SNS post  234 . 
     When each of the data items is generated, the mobile device  140  may capture an environmental sound for a predetermined period of time. The captured environmental sound may include sounds such as sounds of billiard balls striking each other, cue sticks, rolling billiard balls, etc. From the captured environmental sound, the mobile device  140  may generate a sound tag indicating a billiard environment for each of the data items. Based on the sound tags for the data items, the mobile device  140  may determine the data items as having a similar context of a billiard environment, and classify or group the data items, including the photographs  212 ,  222 , and  232 , the memo  214 , the blog post  224 , and the SNS post  234 , into a same group X. In this manner, data items of a same data type as well as data items of different data types that are generated in different geographical locations may be grouped into a same group according to their environmental context. 
       FIG. 3  illustrates a block diagram of the mobile device  140  configured to generate and group data items by classifying the data items based on sound tags according to one embodiment of the present disclosure. The mobile device  140  may include an I/O unit  320 , a data item generator  330 , a sound sensor  340 , a sound tag generator  350 , a control unit  360 , and a storage unit  370 . The mobile device  140  may be any suitable mobile device capable of generating a data item and equipped with a sound capturing and processing capability such as a cellular phone, a smartphone, a laptop computer, a tablet computer, a gaming device, a multimedia recorder/player, etc. 
     In the mobile device  140 , the data item generator  330  may be activated in response to a first user input to activate the data item generator  330  via the I/O unit  320 . In one embodiment, the data item generator  330  may be any application, device, or a combination thereof and includes a camera module, a camera application, an image capture application, a memo application, an SNS application, a blog generating application, a contact application, a phone application, an application execution log module, etc. While the data item generator  330  is activated, a data item may be generated in response to a second user input for generating the data item via the I/O unit  320 . For example, a camera application may be activated by the first user input to initiate a preview mode and generate a photograph in response to the second user input. Similarly, a memo application may be activated by the first user input to initiate a memo editor and generate a memo according to the second user input. In another embodiment, the data item generator  330  may be configured to directly generate a data item in response to a single user input. Once the data item is generated, the data item generator  330  may provide the data item to the control unit  360 . 
     As used herein, a data item may be any data representation of an object, file, or information in a specified format such as a photograph, a memo, an SNS post, a blog post, contact information, a call history, an application execution log, etc. In the case of the SNS post or the blog post, the data item may include basic information and a link to the on-line post since the contents of the on-line post are typically stored in an on-line server. The basic information such as a title, date of creation, a thumbnail of a representative picture, etc. may be output on the I/O unit  320 , for example on a display screen, as a data item. Alternatively, the data item for the SNS post or the blog post may include the entire contents of the on-line post. 
     The sound sensor  340  may be activated to receive and capture an environmental sound  310  of the mobile device  140  for use in generating a sound tag indicative of an environmental context in which the data item is generated. When the data item generator  330  is activated, it may send a notification to the sound sensor  340  that a data item may be generated. If the sound sensor  340  has been inactive, the notification may activate the sound sensor  340 . In response, the sound sensor  340  may capture the environmental sound  310  for a predetermined period of time. 
     In one embodiment, the sound sensor  340  may capture the environmental sound  310  for a predetermined period of time after the first user input. Alternatively, the sound sensor  340  may capture the environmental sound  310  for a predetermined period of time after the second user input. In the case of data items such as blog posts and SNS posts, the environmental sound  310  may be captured while the blog post or the SNS post is being composed by the user. In another embodiment, the sound sensor  340  may capture the environmental sound  310  for a predetermined period of time after the single user input. The sound sensor  340  may include one or more microphones or any other types of sound sensors that can be used to receive, capture, and/or convert the environmental sound  310  into digital data, and may employ any suitable software and/or hardware for performing such functions. 
     The sound tag generator  350  may be configured to receive the captured environmental sound  310  from the sound sensor  340  and generate a sound tag indicating an environmental context for the data item. The sound tag may include at least one of a sound feature, an audio group identifier, and a context label, as will be described in detail below. The sound tag generator  350  may then provide the sound tag to the control unit  360  for use in classifying or grouping the data item. 
     The control unit  360  may receive the data item and the associated sound tag from the data item generator  330  and the sound tag generator  350 , respectively, and combine the sound tag with the data item. The data item and the sound tag may be combined by appending the sound tag to the data item. Alternatively, the sound tag may be linked with the data item using a pointer, a database table, etc., and stored together or separately in the storage unit  370 . The control unit  360  may also classify the data item according to a context indicated in the sound tag. The data item combined with the sound tag may be stored in the storage unit  370 . The storage unit  370  may be implemented using any suitable storage or memory devices such as a RAM (Random Access Memory), a ROM (Read-Only Memory), an EEPROM (Electrically Erasable Programmable Read-Only Memory), a flash memory, or an SSD (Solid State Drive). 
     The mobile device  140  may generate and store a plurality of data items and associated sound tags. In such cases, the control unit  360  may also access the data items and their sound tags from the storage unit  370  and group the data items into one or more groups based on their sound tags. For example, data items may be grouped into a same group when their sound tags indicate a similar environmental context. The control unit  360  may receive user inputs for generating or displaying data items as well as outputting data items, which have been generated or grouped, via the I/O unit  320  such as a touchscreen display. 
       FIG. 4  is a flowchart of a method  400  performed in the mobile device  140  for grouping data items based on sound tags indicating environmental contexts according to one embodiment of the present disclosure. Initially, the data item generator  330  may be activated in response to receiving a first user input, at  410 . The activated data item generator  330  may generate a data item in response to a second user input, at  420 . 
     The sound sensor  340  may capture an environmental sound for a predetermined period of time at  430 . The predetermined period of time is sufficient to identify an environmental context, in which the data item is generated. In one embodiment, the sound sensor  340  may be activated by a notification from the data item generator  330  indicating that a data item may be generated. At  440 , the sound tag generator  350  may generate a sound tag for the data item indicating the environmental context based on the captured environmental sound. The data item may be generated at  420  while the environmental sound is captured at  430  or the sound tag is generated at  440 . In some embodiments, the data item may be generated at  420  before the environmental sound is captured at  430  or after the sound tag is generated at  440 . In another embodiment, at least a portion of the environmental sound may be captured during the time of generating the data item at  420 . 
     Upon receiving the data item and the sound tag from the data item generator  330  and the sound tag generator  350 , the control unit  360  may combine the sound tag with the data item at  450 . The data item combined with the sound tag may be stored in the storage unit  370 . Then, the method  400  proceeds to  460  to determine whether a new data item is to be generated. For example, when the mobile device  140  receives another second input via the I/O unit  320 , it may be determined that the new data item is to be generated. If it is determined that the new data item is to be generated, the method  400  proceeds back to  420  to generate the new data item and also to  430  to capture a new environmental sound for the new data item. Otherwise, the method proceeds to  470  and the control unit  360  classifies or groups the data item generated at  420 . In this case, the data item may be grouped with one or more data items stored in the storage unit  370  based on the associated sound tags. 
       FIG. 5  illustrates generating a sound tag  500  including a sound feature  510 , an audio group identifier  520 , and a context label  530  from an environmental sound  310  according to one embodiment of the present disclosure. When the environmental sound  310  is received, the sound feature  510  may be extracted using any suitable feature extraction scheme such as an audio fingerprint method, an MFCC (Mel-frequency cepstral coefficients) method, etc. For example, the sound feature  510  may be represented as a sequence of m binary codes (e.g., “110 . . . 111”) in the case of the audio fingerprint method, and as a vector having n-dimensional values (e.g., vector {C 1 , C 2 , . . . , C n }) in the case of the MFCC method. In some embodiments, the sound tag  500  may include a plurality of sound features, for example, a sound feature represented as an audio fingerprint and another sound feature represented as an MFCC vector. 
     In another embodiment, the audio group identifier  520  for the extracted sound feature  510  may be determined by accessing a reference audio group database. The reference audio group database may include a plurality of reference audio groups, each of which is associated with an audio group identifier. Each reference audio group may include statistical characteristics which can be generated through audio sample training. The reference audio group to which a sound feature belongs may be determined by using any algorithm adapted for identifying data groups such as the EM (Expectation Maximization) algorithm. For example, when the EM algorithm is used, a probability value of the sound feature belonging to each of the reference audio groups is calculated. After calculating the probability values, the reference audio group with the highest probability value is identified. The audio group identifier associated with the reference audio group with the highest probability value (e.g., audio group identifier “1”) is determined to be the audio group identifier  520  for the sound feature  510 . 
     In still another embodiment, the context label  530  may be identified for the audio group identifier  520  by accessing a context label database. The context label database may include context labels for the audio group identifiers. The context labels may be assigned to the audio group identifiers based on the trained audio samples. Each of the context labels may be a text string or one or more words that identify an environmental context. For example, a context label “BILLIARD” may be identified for the audio group identifier “1” by accessing a lookup table in the context label database. As will be discussed in more detail below, some of the audio group identifiers may not have an assigned context label, for example, due to a lack of sufficient data for associating a context label to an audio group identifier. 
       FIG. 6  illustrates a flowchart of an exemplary method  600  performed in the mobile device  140  for extracting an audio fingerprint from the environmental sound  310  as the sound feature  510  according to one embodiment of the present disclosure. Initially, the sound sensor  340  may receive the environmental sound  310  at  610 . Typically, the environmental sound  310  is received in the form of a signal in the time domain. At  620 , a Fourier transform operation may be performed on the environmental sound  310  to transform the time domain signal to a frequency domain signal. Then, at  630 , the spectrum of the frequency domain signal may be divided into a plurality of frequency bands and a power of the signal may be calculated for each frequency band. 
     At  640 , a binarization operation may be performed on each band power so that a binary value “1” is outputted when the band power exceeds a predetermined power, while a binary value “0” is outputted when the band power does not exceed the predetermined power. The binary values outputted at  640  may be used as the binary codes in the audio fingerprint. The method  600  illustrated in  FIG. 6  is an exemplary method for extracting an audio fingerprint from the environmental sound  310 , and any other suitable methods for extracting an audio fingerprint may be employed. Such methods may analyze various characteristics of the environmental sound  310 , for example, average zero crossing rate, estimated tempo, average spectrum, spectral flatness, prominent tones across a set of bands, bandwidth, etc. 
       FIG. 7  illustrates a flowchart of a method  700  performed in the mobile device  140  for extracting an MFCC vector from the environmental sound  310  as the sound feature  510  according to one embodiment of the present disclosure. Initially, the sound sensor  340  may receive the environmental sound  310  at  710  in the form of a time domain signal. The time domain signal may be transformed to a frequency domain signal by performing a Fourier transform operation on the environmental sound  310  at  720 . The spectrum of the frequency domain signal may be divided into a plurality of frequency bands and a power of the signal may be calculated for each frequency band, at  730 . 
     At  740 , the calculated band powers may be mapped onto the mel scale using triangular overlapping windows to generate mel frequencies. A logarithm operation may be performed on the mel frequencies to generate mel log powers at  750 , and a DCT (discrete cosine transform) operation may then be performed on the mel log powers to generate DCT coefficients at  760 . The generated DCT coefficients may be used as components in the MFCC vector. 
       FIG. 8  illustrates a more detailed block diagram of the sound tag generator  350  and the control unit  360  in the mobile device  140  for classifying or grouping data items by generating a sound tag including a sound feature, an audio group identifier, and a context label for each data item, according to one embodiment of the present disclosure. The sound tag generator  350  may include a sound feature extractor  810 , an audio group determining unit  820 , and a context label identifying unit  830 . The control unit  360  may include a tagging unit  840  and a grouping unit  850 . The mobile device  140  may also include the I/O unit  320 , the data item generator  330 , the sound sensor  340 , and the storage unit  370 , as described above with reference to  FIG. 3 . 
     When the data item generator  330  is activated for generating a data item in response to a user input, the sound sensor  340  may also be activated to receive and capture an environmental sound for a predetermined period of time. The sound feature extractor  810  in the sound tag generator  350  may receive the captured environmental sound from the sound sensor  340  and extract a sound feature from the received environmental sound. In the sound feature extractor  810 , any suitable feature extraction method such as an audio fingerprinting method, an MFCC (Mel-frequency cepstral coefficients) method, etc. may be used to extract the sound feature from the received environmental sound. The sound feature extractor  810  may then provide the extracted sound feature to the audio group determining unit  820 . 
     Upon receiving the sound feature from the sound feature extractor  810 , the audio group determining unit  820  may access a reference audio group database in the storage unit  370 . The reference audio group database may include a plurality of reference audio groups, each of which is associated with an audio group identifier. The audio group determining unit  820  may determine a reference audio group to which the sound feature belongs and output the associated audio group identifier. 
     The reference audio group to which a sound feature belongs may be determined by using any algorithm adapted for identifying data groups such as the EM (Expectation Maximization) algorithm. For example, when the EM algorithm is used, the audio group determining unit  820  calculates a probability value of the sound feature belonging to each of the reference audio groups. After calculating the probability values, the audio group determining unit  820  identifies the reference audio group with the highest probability value. The audio group determining unit  820  then provides the audio group identifier associated with the reference audio group with the highest probability value to the context label identifying unit  830 . 
     The context label identifying unit  830  may receive the audio group identifier from the audio group determining unit  820  and access a context label database from the storage unit  370 . The context label database may include context labels for the audio group identifiers. Each of the context labels may be a text string or one or more words that identify an environmental context (e.g., restaurant environment, billiard environment, stadium environment, etc.). As will be discussed in more detail below, some of the audio group identifiers may not have an assigned context label, for example, due to a lack of sufficient data for associating a context label to an audio group identifier. The context label identifying unit  830  may then identify the context label associated with the received audio group identifier in the context label database and output the identified context label. 
     The sound tag generator  350  may generate the sound tag that indicates an environmental context of the associated data item. In one embodiment, the sound tag generator  350  may generate a sound tag that includes at least one of the sound feature, the audio group identifier, and the context label and provide the sound tag to the tagging unit  840  in the control unit  360 . Alternatively, the sound tag generator  350  may provide at least one of the sound feature, the audio group identifier, and the context label to the tagging unit  840  to be used as a sound tag. 
     When a data item associated with the sound tag is generated in the data item generator  330 , the tagging unit  840  in the control unit  360  may receive the data item from the data item generator  330 . In addition, the tagging unit  840  may receive the sound tag for the data item including at least one of the sound feature, the audio group identifier, and the context label from the sound tag generator  350 . In one embodiment, the data item and the sound tag may then be combined and output as a tagged data item by the tagging unit  840 . In another embodiment, at least one of the sound feature, the audio group identifier, and the context label may be received from the sound tag generator  350  and appended to the data item as a sound tag by the tagging unit  840 . 
     The data item may be classified into a group based on the appended sound tag. For example, the data item may be classified into a group according to the audio group identifier or the context label in the appended sound tag. The data item appended with the sound tag may be provided to the storage unit  370  for storage and/or to the grouping unit  850  to be grouped with one or more tagged data items that may be stored in the storage unit  370 . 
     In the control unit  360 , the grouping unit  850  may receive the tagged data item from the tagging unit  840  for grouping with one or more other tagged data items accessed from the storage unit  370 . Alternatively, the tagged data item may have been stored in the storage unit  370  by the tagging unit  840 . In this case, the grouping unit  850  may access the tagged data item along with other tagged data items stored in the storage unit  370  and group the tagged data items based on their sound tags. The grouping unit  850  may group the tagged data items based on any one or combination of a sound feature, an audio group identifier, and a context label in the sound tags. The control unit  360  may also group the data items for output via the I/O unit  320  in response to a user input. 
       FIG. 9  illustrates an exemplary tagged data item  900  in which a data item  910  is appended with a sound tag  920  including a sound feature  922 , an audio group identifier  924 , and a context label  926 , according to one embodiment of the present disclosure. The sound feature  922 , the audio group identifier  924 , and the context label  926  may, individually or in combination, indicate an environmental context of the data item  910 . Although the illustrated sound tag  920  includes the sound feature  922 , the audio group identifier  924 , and the context label  926 , the sound tag  920  may also be configured to include any one or a combination of the sound feature  922 , the audio group identifier  924 , and the context label  926 . In addition, the appended order of the data item  910 , the sound feature  922 , the audio group identifier  924 , and the context label  926  is not limited to the example of  FIG. 9  and may be properly determined. 
     In one embodiment, when a plurality of tagged data items has been generated in the mobile device  140 , they may be grouped based on sound features in the associated sound tags. For example, sound features for a pair of data items may be compared to calculate a similarity value. If the calculated similarity value exceeds a predetermined similarity threshold, the two data items may be determined to be similar to each other as will be described in more detail with reference to  FIGS. 10 and 11 . 
     In another embodiment, a plurality of data items may be classified or grouped into a same group based on the associated audio group identifiers. In this case, data items having the same audio group identifier may be classified into a same group. The plurality of data items may also be classified or grouped based on the associated context labels. In this case, data items that have the same context label may be grouped together. Classifying and grouping of data items based on the associated audio group identifiers and context labels are described in more in detail with reference to  FIGS. 13 and 14  below. 
       FIG. 10  illustrates grouping a selected data item  1010  with other data items  1020 ,  1030 , and  1040  by determining a similarity value between a sound feature associated with the selected data item  1010  and each sound feature associated with the data items  1020  to  1040 , according to one embodiment of the present disclosure. Initially, the data item  1010  to be grouped may be selected when it is generated or in response to a user input. For each of the data items  1020 ,  1030 , and  1040 , a similarity value between the sound feature of the selected data item  1010  and the sound feature associated with the data item  1020 ,  1030 , or  1040  may be calculated. 
     A similarity value between a pair of sound features may be calculated by employing any suitable distance metrics such as Mahalonobis distance, p-norm distance, Hamming distance, Euclidean distance, Manhattan distance, Chebyshev distance, etc. For example, in the case of audio fingerprints used as sound features, a similarity value may be determined by calculating a Hamming distance between a pair of audio fingerprints, and taking a multiplicative inverse of the distance. In the case of using MFCC vectors as sound features, a similarity value may be determined by calculating a Euclidean distance between a pair of MFCC vectors, and taking a multiplicative inverse of the distance. 
     Once a similarity value has been determined for a pair of data items, the similarity value may be compared to a predetermined similarity threshold. If the similarity value exceeds the threshold, the two data items may be determined to have a similar environmental context and thus are grouped into a same group. On the other hand, if the similarity value does not exceed the threshold, the data items may be considered to have different environmental contexts and are not grouped into a same group. 
     In the illustrated embodiment, similarity values between the sound feature associated with the data item  1010  and the sound features of the data items  1020  to  1030  are determined and compared with a similarity threshold value which is predetermined to be, for example, 0.6. The determined similarity value between the sound features of the data items  1010  and  1020  (i.e., S 12 ) is 0.8, which is greater than the predetermined similarity threshold. Thus, the data items  1010  and  1020  may be determined to have a similar environmental context and can be grouped together. For the sound features of the data items  1010  and  1030 , the determined similarity value (i.e., S 13 ) of 0.7 is greater than the predetermined similarity threshold. Accordingly, the data items  1010  and  1030  are also determined to have a similar environmental context and can be grouped into a same group. On the other hand, the similarity value between the sound features of the data items  1010  and  1040  (i.e., S 14 ) is 0.5, which is smaller than the predetermined value 0.6. Thus, data items  1010  and  1040  are determined to have different environmental contexts and are not grouped together. Based on the above grouping, the data items  1010 ,  1020 , and  1030  may be grouped and displayed as a single group. 
       FIG. 11  illustrates the selected data item  1010  and the data items  1020  and  1030  displayed as a single group on a display screen  1100  of the mobile device  140 , according to one embodiment of the present disclosure. As illustrated, the selected data item  1010  may be displayed on an upper portion  1110  of the display screen  1100  of the mobile device  140 . The data items  1020  and  1030  may be displayed as having a similar context as the selected data item  1110  in a lower portion  1120  of the display screen  1100 . In this manner, the mobile device  140  may group and display a data item with other data items having similar context based on sound features extracted from captured environmental sounds. 
       FIG. 12  is an exemplary context label database  1200  illustrating context labels for a plurality of input audio group identifiers according to one embodiment of the present disclosure. The context label database  1200  may include N context labels associated with N audio group identifiers. In the illustrated embodiment, context labels “BILLIARD,” “STADIUM,” “RESTAURANT,” and “CAR” are associated with audio group identifiers “1,” “3,” “N−2,” and “N−1,” respectively. The context label database  1200  may be implemented as a lookup table or any other data structure that associates audio group identifiers with context labels. 
     As described above with reference to  FIG. 8 , the context label identifying unit  830  may access the context label database  1200  based on an audio group identifier and identify a context label associated with the audio group identifier. For example, when an audio group identifier “3” is received, the context label identifying unit  830  identifies and outputs the context label “STADIUM.” Similarly, the context label “RESTAURANT” may be output for the audio group identifier “N−2.” 
     In the context label database  1200 , if a unique context label is not available for an audio group identifier (e.g., audio group identifiers “2” and “N”), a context label “UNKNOWN” may be assigned. In one embodiment, data items having the context label “UNKNOWN” may be classified and grouped into a same group. In this manner, data items may be classified and grouped according to their context labels. 
       FIG. 13  illustrates a plurality of groups of data items  1310 ,  1320 ,  1330 , and  1340  displayed on the display screen  1100  of the mobile device  140  based on audio group identifiers in sound tags associated with the data items, according to one embodiment of the present disclosure. As described with reference to  FIGS. 1 and 2  above, the plurality of photographs  212 ,  222 , and  232 , the memo  214 , the blog post  224 , and the SNS post  234  are generated in a billiard environment and are combined with the same audio group identifier (e.g., audio group identifier “1” in  FIG. 12 ). Accordingly, the data items  212 ,  214 ,  222 ,  224 ,  232 , and  234  may be grouped and displayed as the first group of data items  1310 . 
     The photograph  130  and the SNS post  132  are generated in a grocery market environment and are combined with the same audio group identifier. Thus, the data items  130  and  132  may be grouped and displayed as the second group of data items  1320 . The photograph  120  and the blog post  122  are generated in a restaurant environment and are combined with the same audio group identifier. Therefore, the data items  120  and  122  may be grouped and displayed as the third group of data items  1330 . The photograph  110  and the memo  112  are generated in an outdoor environment and are combined with the same audio group identifier. Accordingly, the data items  110  and  112  may be grouped and displayed as the fourth group of data items  1340 . 
     In one embodiment, each of the groups  1310  to  1340  may be displayed with an audio group number to distinguish the groups  1310  to  1340  (e.g., “AUDIO GROUP 1” to “AUDIO GROUP 4” as illustrated in  FIG. 13 ). Additionally or alternatively, a context label associated with each of the audio group identifiers for the groups  1310  to  1340  may be displayed on the display screen  1100  of the mobile device  140 . For example, the context labels “BILLIARD” and “RESTAURANT” may be displayed above the first and third groups of data items  1310  and  1330  while the context label “UNKNOWN” may be displayed above the second and fourth groups of data items  1320  and  1340 . 
       FIG. 14  illustrates a plurality of groups of data items  1410 ,  1420 , and  1430  displayed on the display screen  1100  of the mobile device  140  based on context labels in sound tags associated with the data items in another embodiment of the present disclosure. As described with reference to  FIGS. 1 and 2  above, the plurality of photographs  212 ,  222 , and  232 , the memo  214 , the blog post  224 , and the SNS post  234  are generated in a billiard environment and are combined with the context label “BILLIARD.” Accordingly, the data items  212 ,  214 ,  222 ,  224 ,  232 , and  234  may be grouped and displayed as the first group of data items  1410 . The photograph  120  and the blog post  122  are generated in a restaurant environment and are combined with the same context label “RESTAURANT.” Thus, the data items  120  and  122  may be grouped and displayed as the second group of data items  1420 . 
     In the illustrated example of  FIG. 14 , the photograph  110  and the memo  112  are generated in an outdoor environment and are combined with the context label “UNKNOWN.” Further, the photograph  130  and the SNS post  132  are generated in a grocery market environment and are combined with the context label “UNKNOWN.” Although the audio group identifiers for the data items  110  and  112  may be different from the audio group identifier for the data items  130  and  132 , the different audio group identifiers are associated with the same context label “UNKNOWN.” Thus, the data items  110 ,  112 ,  130 , and  132  may be grouped according to the same context label “UNKNOWN” and displayed together in the third group of data items  1430 . As illustrated in  FIG. 14 , each of the groups  1410  to  1430  may be displayed with the context labels (e.g., “BILLIARD,” “RESTAURANT,” and “UNKNOWN”) to distinguish the groups  1410  to  1430 . 
       FIG. 15  illustrates a block diagram of a mobile device  1500  in a wireless communication system in which the methods and apparatus for classifying or grouping data items may be implemented according to some embodiments of the present disclosure. The mobile device  1500  may be a cellular phone, a terminal, a handset, a personal digital assistant (PDA), a wireless modem, a cordless phone, a tablet, and so on. The wireless communication system may be a Code Division Multiple Access (CDMA) system, a Global System for Mobile Communications (GSM) system, a Wideband CDMA (W-CDMA) system, a Long Term Evolution (LTE) system, a LTE Advanced system, and so on. 
     The mobile device  1500  may be capable of providing bidirectional communication via a receive path and a transmit path. On the receive path, signals transmitted by base stations are received by an antenna  1512  and are provided to a receiver (RCVR)  1514 . The receiver  1514  conditions and digitizes the received signal and provides the conditioned and digitized signal to a digital section  1520  for further processing. On the transmit path, a transmitter (TMTR) receives data to be transmitted from a digital section  1520 , processes and conditions the data, and generates a modulated signal, which is transmitted via the antenna  1512  to the base stations. The receiver  1514  and the transmitter  1516  is part of a transceiver that supports CDMA, GSM, W-CDMA, LTE, LTE Advanced, and so on. 
     The digital section  1520  includes various processing, interface, and memory units such as, for example, a modem processor  1522 , a reduced instruction set computer/digital signal processor (RISC/DSP)  1524 , a controller/processor  1526 , an internal memory  1528 , a generalized audio encoder  1532 , a generalized audio decoder  1534 , a graphics/display processor  1536 , and/or an external bus interface (EBI)  1538 . The modem processor  1522  performs processing for data transmission and reception, e.g., encoding, modulation, demodulation, and decoding. The RISC/DSP  1524  performs general and specialized processing for the mobile device  1500 . The controller/processor  1526  controls the operation of various processing and interface units within the digital section  1520 . The internal memory  1528  stores data and/or instructions for various units within the digital section  1520 . 
     The generalized audio encoder  1532  performs encoding for input signals from an audio source  1542 , a microphone  1543 , and so on. The generalized audio decoder  1534  performs decoding for coded audio data and provides output signals to a speaker/headset  1544 . It should be noted that the generalized audio encoder  1532  and the generalized audio decoder  1534  are not necessarily required for interface with the audio source, the microphone  1543  and the speaker/headset  1544 , and thus are not shown in the mobile device  1500 . The graphics/display processor  1536  performs processing for graphics, videos, images, and texts, which is presented to a display unit  1546 . The EBI  1538  facilitates transfer of data between the digital section  1520  and a main memory  1548 . 
     The digital section  1520  is implemented with one or more processors, DSPs, microprocessors, RISCs, etc. The digital section  1520  is also fabricated on one or more application specific integrated circuits (ASICs) and/or some other type of integrated circuits (ICs). 
     In general, any device described herein is indicative of various types of devices, such as a wireless phone, a cellular phone, a laptop computer, a wireless multimedia device, a wireless communication personal computer (PC) card, a PDA, an external or internal modem, a device that communicates through a wireless channel, and so on. A device may have various names, such as access terminal (AT), access unit, subscriber unit, mobile station, client device, mobile unit, mobile phone, mobile, remote station, remote terminal, remote unit, user device, user equipment, handheld device, etc. Any device described herein may have a memory for storing instructions and data, as well as hardware, software, firmware, or combinations thereof. 
     The techniques described herein are implemented by various means. For example, these techniques are implemented in hardware, firmware, software, or combinations thereof. Those of ordinary skill in the art would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure. 
     For hardware implementation, the processing units used to perform the techniques are implemented within one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-controllers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, a computer, or a combination thereof. 
     Thus, the various illustrative logical blocks, modules, and circuits described in connection with the disclosure herein are implemented or performed with a general-purpose processor, a DSP, an ASIC, a FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternate, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. 
     If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates the transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a computer. By way of example, and not limited thereto, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Further, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc, where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. 
     The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein are applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. 
     Although exemplary implementations are referred to utilizing aspects of the presently disclosed subject matter in the context of one or more stand-alone computer systems, the subject matter is not so limited, but rather may be implemented in connection with any computing environment, such as a network or distributed computing environment. Still further, aspects of the presently disclosed subject matter may be implemented in or across a plurality of processing chips or devices, and storage may similarly be effected across a plurality of devices. Such devices may include PCs, network servers, and handheld devices. 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.