Abstract:
A multimedia organization and playback system intelligently organizes media objects, such as music files, and plays back their contents. The system considers and analyzes multiple media object attributes to determine groups of similar songs. As a result, the system delivers a consistent selection of media to the listener despite wide variations in media characteristics and without overburdening the listener with complicated configuration input. The listener may play back grouped songs based on the similarities between songs. Successive selections may be more or less similar to a current selection based on the organization of the music objects.

Description:
BACKGROUND OF THE INVENTION 
       [0001]    1. Technical Field 
         [0002]    The present invention relates to a multimedia object organization and playback system. In particular, the present invention relates to a multimedia object organization and playback system that groups objects heuristically and allows playback based on similarities between the grouped objects. 
         [0003]    2. Related Art 
         [0004]    Enormous growth in digital technology has given rise to vast collections of information, including music and multimedia objects, such as files and data streams; phone, personal, or restaurant contacts; news articles; photographs; and many other types of data files. For example, fast processors, Internet music stores, and almost boundless and inexpensive data storage have given the everyday individual the ability to build an extensive digital collection of music. At the same time, advanced music players have taken on the challenge of providing convenient access to the hundreds or thousands of songs that may be in such a collection. 
         [0005]    Music objects may be organized based on pre-defined or user-defined designations, such as an artist, album, or genre of the music. For example, MP3 or other music object types may include ID3 tags associated with the music object. The organized music objects may be stored in playlists based on the designations, and may be transferred to music players and played back based on the playlist or designation. 
         [0006]    The user interface on a music playback device typically displays a list of tagged objects stored or available on the device. However, the user interface typically presents only simplified views of the objects, such as displaying the entire list of objects, or lists of objects restricted by a limiting selection. For example, the objects may be displayed in alphabetical order, or displayed according to a genre or artist selection. Once the objects are tagged or catalogued, the listener may decide what objects to listen to using the user interface. 
         [0007]    Listeners may be interested in playing a small number of groups of music, where each group is similar by some musical or psychological attribute. Conventional user interface applications for data object lists do not allow groupings of objects other than by pre-defined designations and categories. Other limited playback features may also be present. As one example, the user interface may permit a listener to select random shuffle playback. Another option often provided is to permit playback from a manually defined and entered playlist of songs. In other words, conventional user interfaces provided insufficient playback flexibility in many situations. The explosive growth of digital music collections exacerbates this problem. 
         [0008]    Therefore a need exists for a system that allows grouping of objects based on heuristic characteristics and allows playback of music objects based on such a grouping to improve the music selection and playback experience. 
       SUMMARY 
       [0009]    A multimedia organization and playback system intelligently organizes information and delivers the information on request. The information may be present in virtually any type of storage mechanism, including music files, streaming music objects, address entries, video objects, telephone contacts, restaurant selections or other types of objects. In the context of a music object organization and playback system, for example, the system may intelligently deliver a selection of music to the listener in which each song is chosen to be consistent with the last song, or more or less similar to the last song. 
         [0010]    A music object organization system assigns music objects to groups based on heuristic characteristic of the music objects. The music object organization system determines music object groups based on a music object characteristic, such as a genre, album, artist, or music object length. The music object organization system identifies a heuristic distance measure applicable to the music objects, and assigns the music objects among the music object groups to satisfy a clustering criterion, such as a minimization of the heuristic distance measures between music objects within the music object groups. 
         [0011]    A music object playback system that may be used with the music object organization system presents a music object group as a selectable “channel” to the listener. The music object group may be organized based on a music object characteristic associated with the music objects. The system organizes the music object groups such that a heuristic distance measure between the music objects satisfies a clustering criterion, such as a minimization of the heuristic distance measures between music objects within the music object groups. The music object playback system determines which music objects are candidates for selection as the next music object to play from the music object group by adjusting a next music object selection space based on a maximum heuristic distance measure. Then, actual selection is at random, at alphabetical order, track length order, or any other selection criteria in the space. A listener may select a music object that is “more like” a currently playing music object, in which case the system reduces the next music object selection space. The listener may also select a music object that is “less like” the currently playing music object, in which case the system expands the next music object selection space. 
         [0012]    A graphical user interface for the system presents a “more like this” user interface element through which an operator reduces a next music object selection space, to select a next music object that is “more like” the currently playing music object. A “less like this” user interface element is also presented, through which an operator expands the next music object selection search space. The operator may select a next music object that is “less like” the currently playing music object using the “less like this” user interface element. 
         [0013]    Other systems, methods, features and advantages of the invention will be, or will become, apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the following claims. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]    The invention can be better understood with reference to the following drawings and description. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. Moreover, in the figures, like referenced numerals designate corresponding parts throughout the different views. 
           [0015]      FIG. 1  is an example heuristic map of music objects. 
           [0016]      FIG. 2  is a music object organization system. 
           [0017]      FIG. 3  is a music object playback system. 
           [0018]      FIG. 4  is an example of heuristic distance measures. 
           [0019]      FIG. 5  is a music object selection space based on the heuristic distance measures of  FIG. 4 . 
           [0020]      FIG. 6  is an example of heuristic distance measures. 
           [0021]      FIG. 7  is a music object selection space based on the heuristic distance measures of  FIG. 6 . 
           [0022]      FIG. 8  is an example of heuristic distance measures. 
           [0023]      FIG. 9  is a schematic diagram of a music object selection space based on the heuristic distance measures of  FIG. 8 . 
           [0024]      FIG. 10  illustrates a graphical user interface. 
           [0025]      FIG. 11  shows the acts the music object organization system takes to group music objects based on a music object characteristic. 
           [0026]      FIG. 12  shows the acts the music object organization system takes to satisfy a clustering criterion for the music objects. 
           [0027]      FIG. 13  shows the acts the music object playback system takes to select and play a next music object. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0028]    Music listeners may not desire to listen to a musical collection as a random collection of tracks. Music listeners may be interested in listening to one of a small number of groups of music, where each group is similar by some attribute, such as a musical or psychological attribute. Listeners may desire to organize a music collection based on heuristic music object characteristics associated with the music objects in the music collection, such as by grouping music objects that are more similar to each other than to other groups of music objects. Such a heuristic grouping may allow a continuum of music object organization based on music object characteristics associated with the music objects. 
         [0029]      FIG. 1  is an example heuristic map  100  of music objects. The map  100  locates music objects in an N-dimensional space, such as a vector space, starting with giving each music object a location in this space. Each axis of the N-dimensional space may be based on a music object characteristic, or combination of characteristics, associated with the music objects. The music objects may be represented in the vector space as ordered n-tuples, where n may be an integer greater than 0. Vectors in these spaces may be ordered “tuples” of real numbers. A pair of music objects, such as two classical music objects, may be located “close” to each other in the space, whereas another pair, such as a classical music object and punk rock music object, may be “far” from each other in the space. 
         [0030]    One way of assigning locations to each music object may include determining or obtaining a genre tag of each music object, and comparing the information in the genre tag to a two-dimensional map of genres to locate the music object. For example, a “tag” object associated with the music objects may be processed, such as by extracting music object characteristics from an ID3 tag associated with the music object. The music object&#39;s resulting location may be related to the location of that genre on the map. For example, given the example heuristic map  100  shown in  FIG. 1 , a rock song  102  would have the location of (1,1), and a jazz song  104  would have the location of (−1,1). Other mappings are possible, including mappings of the music objects in more than two-dimensional space. For example, additional music object characteristics may include an artist, album, composer, music object length, beat-per-minute count, time period, and/or a user-defined characteristic for a music object. The music objects may adhere to the WAV, MP3, WMA, OOG, AAC, AIF, or other music object formats. The music objects may also include streaming objects such as satellite radio objects, streaming audio objects, objects stored on a server and streamed to a device, high definition radio objects, cellular-transmitted audio objects, or other audio objects streamed over a wired or wireless connection. 
         [0031]    The heuristic map  100  may be determined by a listener&#39;s preferences and selection of music object characteristics. The listener may set locations for music objects within the heuristic map  100  as a pre-processing operation, or during operation using a graphical user interface. In some systems, one or more heuristic maps  100  may be pre-defined (e.g., as a system default heuristic map) and installed or loaded in a memory in the system at a factory or original equipment manufacturer (OEM) location. The system may receive operator input that switches between the currently active map during system operation. In other systems, an expert system may be configured to analyze the music objects and determine the heuristic map  100 . The heuristic map  100  may be based on listener statistics, such as previously played music objects, ranked lists of music objects, subscriptions to music object services, and/or other behavioral statistics that may determine a heuristic map of music objects. 
         [0032]      FIG. 2  illustrates an example music object organization system  200  (“system  200 ”). The system  200  may organize music objects based on a music object characteristic associated with the music objects to create heuristic groupings of music objects. The system  200  may present the heuristic groupings to a listener for selection. The system  200  includes a system processor  202 , a system co-processor  203 , and a user interface  204  configured to accept input and present output to a listener. The system  200  also includes a system memory  206 , databases  208  configured to retain data related to system operation, a display  240 , and a communications interface  250 . As will be discussed in more detail below, the system processor  102  may execute music object organization logic  230  to organize the music objects based on the music object characteristic associated with the music objects, for example. 
         [0033]    The system co-processor  203  may assist with the execution of instructions by the system processor  202 . The system co-processor  203  may be implemented as a math co-processor to execute analysis or statistical programs associated with the music object grouping. The system co-processor  203  may be integral with the processor  202 , or may be a separate processor. 
         [0034]    The user interface  204  accepts input from a listener. A listener may desire to view a list of music objects, search the music objects, enter data for input to the system  200 , select applications, play a music object, define new or modify existing heuristic maps, or take other desired actions. The user interface  204  may include an interface display  205  configured to display user interface elements for viewing and/or selection. The user interface  204  may include one or more selectors  207 , such as buttons, actuable by the listener to select an action or to enter data. The user interface  204  may also include a user input logic  209 . The user input logic  209  may include a mouse, keyboards, keypads, touchpads, styli, light pens, microphone inputs, wireless receivers, remote input, trackpads, trackballs, joysticks, track pointers, haptic modules, force-feedback modules, motion detectors, biomeasure input modules, and/or other input modules. The user interface  204  may be coupled wirelessly to the system  200 , such as through a Bluetooth, infrared, WiFi, RF, cellular connection, and/or other wireless connection. Alternatively or additionally, the use r interface  204  may be coupled by a wired connection to the system  200 , such as through an Ethernet, RCA, USB, Firewire, or other wired connection. In some systems, the user interface  204  may be integral with the system  200 . 
         [0035]    The system memory  206  may include a music object information database  210 . The music object information database  210  may include data records related to the music objects, each data record containing fields together with a set of operations for searching, sorting, recombining, and/or other functions related to the fields and data records. For example, the music object information database  210  may retain the music object characteristics associated with the music objects such as a genre field  212 , an album field  213 , an artist field  214 , a beats-per-minute field  215 , a time period field  216 , and/or a user field  217 , such as a user-defined or user-configurable field. Additional, fewer, or different data records associated with the music objects may be stored in the memory  206 . The music object information database  210  may store an ID3 tag or other metadata container associated with a music object. The music object information database  210  may be editable or may be locked or read-only. 
         [0036]    The interface control logic  220  may include instructions that control and accept input from the user interface  204 . The interface control logic  220  may process input from the selectors  207  or the input module  209  to control the system  200 . The interface control logic  220  may include instructions to process actions selected through the user interface  204 . For example, the interface control logic  220  may display a list of music objects available in the music object information database  210 , or allow a listener to search or select a desired music object or group of music objects. In some systems, the interface control logic  220  accepts input from the user interface  204  to organize the music objects into groups based on the music object characteristics associated with the music objects. 
         [0037]    The user interface generation logic  225  may include instructions that cause the user interface  204  to display user interface elements on the interface display  205  or the display  240 . Examples of user interface elements include screen window panes, soft keys, touchscreen elements, soft buttons, scroll bars, dials, and/or other graphical interface elements. The instructions may be calls to graphical user interface libraries, draw primitives, graphical libraries, or other user interface element instructions. 
         [0038]    The music object organization logic  230  may include instructions that organize the music objects into groups based on the music object characteristics associated with the music objects. The music object organization logic  230  may be implemented as processor executable instructions, executed by the system processor  202 , to determine groups of music objects, such as at least a first group of music objects and a second group of music objects. These groups may represent the common listening styles preferred by the listener. The groups may be based on the heuristic map  100 . The music object organization logic  230  may identify a heuristic distance measure applicable to the music files, such as a heuristic distance measure based on the heuristic map  100 . The heuristic distance measure may be a function of the ordered n-tuples associated with the music objects in the vector space associated with the music object characteristics. For example, the heuristic distance measure may be defined as a geometric distance between the music objects in the vector space: 
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         [0039]    where D H  is the heuristic distance measure, {x1, y1, z1, . . . q1} may represent a location of a first music object in the vector space, and {x2, y2, z2, . . . q2} may represent a location of a second music object in the vector space. The music object organization logic  230  may determine the heuristic distance by determining an N-dimensional scalar magnitude of a vector between the music objects within a group. The heuristic distance measure may include a similarity measure between the music objects, where music objects with a smaller heuristic distance measure separating them may considered “more similar.” Other functions for D H  may be possible, where coordinates of the ordered n-tuples are mapped from the music object characteristics. The music objects may be ordered in the heuristic map to form a vector space, where the music objects satisfy mathematical relations related to vector operations on the music objects. 
         [0040]    The music object organization logic  230  may determine the groups by assigning each music object among at least the first music object group and the second music object group such that heuristic distance measures between the music objects satisfy a clustering criterion. In some systems, the clustering criterion comprises a minimization of the heuristic distance measures between the music objects within a group. The music object organization logic  230  may organize the music objects within a group such that an intra-group variance of the music objects is minimized. 
         [0041]    The music object organization logic  230  may use an expectation-maximization process to group the music objects. The music object organization logic  230  may use a K-means process. The K-means process groups objects based on attributes, such as the music object characteristics, into k initial partitions. The music object organization logic  230  may determine a number k means of data generated from distributions (such as Gaussian distributions) of the music objects in the vector space. The music object organization logic  230  may minimize a total intra-group variance, or, a squared error function: 
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         [0042]    where there are k groups S i , i=1, 2, . . . , k, and μ i  is the centroid or mean point of all the points x j εS i . In the system  200 , x j  may represent a music object available for organization. The music object organization logic  230  may set the number of groups based on a determined music object characteristic, such as a pre-determined or user-determined number of groups. The music object organization logic  230  may set the number of groups based on the heuristic map  100 . In some systems, the music object organization logic  230  may adjust the number of groups dynamically, such as by allowing an operator to set the number of groups. 
         [0043]    The music object organization logic  230  partitions the input points into k initial groups, either at random or using some heuristic data. The music object organization logic  230  then calculates the mean point, or centroid, of each group. The music object organization logic  230  determines a new partition by associating each point, or music object, with a closest centroid. Then, the centroids are recalculated for the new groups. The music object organization logic  230  may repeat the process by alternate application of these two steps until convergence of the process is reached, which may obtained when music objects no longer switch clusters (or alternatively, centroids are no longer changed). 
         [0044]    The music object organization logic  230  may store the groups of music objects, determined during the organization process, in the system memory  206 . The system  200  may display the groups of music objects on the display  240 . The listener may select a group for playback, view group characteristics, select a repeat of the organization process, and/or other actions. 
         [0045]    The music object information  210  may additionally or alternatively be obtained from databases  208 , through operator input at the user interface  204 , through a communication interface  250  (e.g., through a network connection to a data warehouse, to equipment in the system  200 , such as storage, server, computer, or to other logic) or from other sources. As examples, the databases  208  may be local or remote databases that store music object information, program module code means, or system music object characteristic information. 
         [0046]    The system  200  may organize the music objects within the group so that all of the music objects are “close” to each other by a heuristic measure. For example, the system  200  may organize music objects that have a genre music object characteristic of “classical” within a group. Music objects that have a “classical” genre music object characteristic and that also have the same “album” music object characteristic and “artist” music object characteristic may be considered “closer” heuristically than music objects that have a “classical” genre and the same “artist” music object characteristic but have a different “album” music object characteristic. A listener may configure the system  200  to organize music objects closer within a group based on different music object characteristics. The listener may set the music object organization logic  230  to initially place the music objects within the groups. In some systems, the music object organization logic  230  may load a mapping template, such as the heuristic map  100 , or a set of rules to place the music objects within the vector space. 
         [0047]    When the system  200  organizes the music objects into groups, the listener may play the music objects.  FIG. 3  illustrates a music object playback system  300  (“system  300 ”) that may present the organized groups to the listener, and allow playback of a selected group of music objects. The music object playback system  300  may include a user interface  304 , a system memory  306  configured to retain a music object playback logic  340 , user interface generation logic  345 , databases  308  that contain data related to the music object information and equipment information, and speakers  380 . 
         [0048]    The system  300  may present a list of groups of music objects to the listener as selectable “channels” to listen to. The user interface generation logic  345  may present the list of groups on the display  240  and/or the user interface  304 . The system memory  306  may retain channel assignment logic  350  that maps the groups to channel names that the user interface  304  displays. The channel assignment logic  350  map assign channel names, such as Group  1 -&gt;“Channel  1 ”, Group  2 -&gt;“Channel  2 ,” etc. Other channel assignments may be possible, such as assignments based on the heuristic map  100 , user selected channel mappings, or other assignments. The user interface generation logic  345  may be implemented as instructions that cause the interface display  205  and/or the display  240  to display user interface elements. The listener may select a group of music objects, and may select a music object from within the group for playback, using the user interface  304 . Alternatively, the system  300  may randomly select a music object for playback when the listener selects a group. For example, the system  300  may randomly choose a starting music object with uniform probability from all the music objects in the group, and begin playback. 
         [0049]    Once playback has begun, the system  300  may present the listener with playback controls that direct the system  300  to take specific actions. The listener may use the user interface  304 , such as by actuating selectors  307  or other user input  309 , to select an action. For example, the listener may activate a “track forward” control to direct the system  300  to play a next music object in the group. Other examples of playback controls include a “less similar” playback control and a “more similar” playback control. The listener may also allow the current music object to play to the end of track, “EOT.” 
         [0050]    The listener may also indicate to the system  300  that the listener desires to next hear a song that is “less like” or “less similar” to the current playing music object. In this case, the listener may desire a music object within the group that sounds less like the currently playing music object. A “less similar” song may be determined based on song characteristics such as album, artist, genre, beats-per-minute, music object length, time period, or and/or a different user-defined field associated with the music object. 
         [0051]    The listener may also indicate to the system  300  to select a music object within the group that sounds “more like” or is “more similar” to the current playing music object. Such a song may be determined based on song characteristics such as album, artist, genre, beats-per-minute, time period, music object length and/or a different user-defined associated with the music object. 
         [0052]    The next music object may be chosen randomly from a set of music objects within a maximum heuristic distance measure from the current music object and within the current group. The maximum heuristic distance is chosen as a function of a heuristic distance scaling factor, K. The music object playback logic  340  may select a next music object to play from the group of objects by adjusting a next music object selection space based on the maximum heuristic distance measure. The next music object selection space may encompass a subset of music objects in the selected group that the music object playback logic  340  may select from based on the maximum heuristic distance measure. 
         [0053]    The music object playback logic  340  may determine a uniform random variable X. The music object playback logic  340  may use a random number generator, a look-up table, or other logic to determine the uniform random variable X. The music object playback logic  340  determines the heuristic distance scaling factor K, where K may be based on a value retained in the system memory  304 , the databases  308 , from listener input, or from a program executed by the processor  202 . The maximum heuristic distance measure D may be determined by the following equation: 
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         [0054]    The system  300  may implement other maximum heuristic distance measure functions. 
         [0055]      FIG. 4  illustrates an example graph  400  of maximum heuristic distance measures as a function of the uniform random variable X.  FIG. 4  is a graph of Equation 3 with K=50. The system  300  influences the selection space from which the next song will be chosen by changing the K parameter. For a song that is similar to the last song, for example, the system  300  may use a value for K of 50, such that about 50% of the maximum heuristic distances will be 1 or less, and the largest maximum distance is less than 1.5. Once the maximum distance has been determined using X and K, the music object playback logic  340  may randomly choose a music object that has a heuristic distance less than or equal to the determined maximum distance D from the current music object. The result is that subsequent music objects may be similar to the current music object. The system may select similar music objects when the listener uses the track forward control, when the currently playing song ends, or for other reasons. 
         [0056]      FIG. 5  illustrates an example three dimensional heuristic map  500 .  FIG. 5  labels the axes  502 ,  504 ,  506  for each of the three dimensions. A group of music objects (several of which are labeled  508 ,  510 ,  512 ,  514 ,  516  and  518 ) are located in the heuristic map  500 . Based on the example function illustrated in  FIG. 4 , a next music object selection space  520  may be represented as a sphere with a radius D  522 , which may represent the maximum heuristic distance measure. The next music object selection space  518  encompasses a set of music objects ( 510 ,  512 ,  514 ,  516  and  518 ) that are located within the radius D  522  of a music object  510 . The music object  510  may represent the currently playing music object. The radius D  522  may be determined based on the heuristic distance scaling factor, such as by using Eqn. 3. In  FIG. 5 , radius D  522  may be determined using a heuristic distance scaling factor of K=50, based on the example in  FIG. 4 . The music object playback logic  340  may randomly select a next music object to play from the music objects ( 510 ,  512 ,  514 ,  516  and  518 ). 
         [0057]    The music object  508  lies outside the next music object selection space  520 . Because the music object  508  is located a distance greater than radius D  522  from the music object  510 , the music object  508  may be excluded from the next music object selection space  520 . The music object playback logic  340  does not select music objects that lie outside the next music object selection space when the music objects, such as the music object  508  for example, are located a distance greater than the radius D  522 . 
         [0058]      FIG. 6  illustrates an example graph  600  of maximum heuristic distance measures, as a function of uniform random variable X, when the listener desires a next music object that is “less like” or less similar to the current playing music object. If the listener desires the next music object to be less similar or “less like” the current playing music object, the music object playback logic  340  may decrease the value of the heuristic distance scaling factor, such as by using a value for K of 25. In this case, there may be only a 20% probability that the maximum heuristic distance measure is less than 1. This probability would indicate that it may be now more likely that the next music object is less similar to the current playing music object, because the average distance between selected music objects is further. Other values for K may be selected by the music object playback logic  340 . 
         [0059]      FIG. 7  illustrates an example heuristic map  700  based on the example graph of maximum heuristic distance measures in  FIG. 6 . Based on the example group illustrated in  FIG. 6 , a next music object selection space  724  may be represented as a sphere with a radius D ( 726 ), which may represent the maximum heuristic distance measure. The next music object selection space  724  may include a set of music objects ( 510 ,  512 ,  514 ,  516 ,  518 ,  720  and  722 ) that are located within the radius D  726  of a music object  510 . Music object  510  may represent the currently playing music object. The radius D  726  may be determined based on the heuristic distance scaling factor, which may be decreased, based on the example in  FIG. 6 . In  FIG. 7 , the radius D  726  may be determined using a heuristic distance scaling factor of K=25, based on the example in  FIG. 4 . In  FIG. 7 , the next music object selection space  724  has been increased, including more music objects to select. The music object playback logic  340  may randomly select a next music object to play from the music objects ( 510 ,  512 ,  514 ,  516 ,  518 ,  720  and  722 ). The music object  508  may be excluded from the next music object selection space  724  because the music object  508  is located a distance from the music object  510  that is greater than the radius D  726 , which represents the maximum heuristic distance measure for the next music object selection space  724 . The music object that is selected may therefore be less similar or “less like” the currently playing music object  510  because the maximum heuristic distance measure is larger than the maximum heuristic distance measure in  FIG. 5 . 
         [0060]      FIG. 8  illustrates an example graph  800  of maximum heuristic distance measures when the listener desires a next music object that is “more like” or more similar to the current playing music object. If the listener desires the next music object to be “more like” or more similar to the current playing music object, the music object playback logic  340  may increase the value of the heuristic distance scaling factor, such as by using a value for K of 75. In this case, there may be an 80% probability that the maximum distance is less than 1. This probability would indicate that it is now more likely that the next music object is more similar to or “more like” the current playing music object, because the average distance between selected music objects is less. Other values for K may be selected by the music object playback logic  340 . 
         [0061]      FIG. 9  illustrates an example heuristic map  900  based on the example graph of maximum heuristic distance measures in  FIG. 8 . Based on the example group illustrated in  FIG. 8 , a next music object selection space  920  may be represented as a sphere with a radius D ( 922 ), which may represent the maximum heuristic distance measure. The next music object selection space  920  may encompass a set of music objects ( 510 ,  512  and  518 ) that are located within the radius D  922  of a music object  510 . The music object  510  may represent the current playing music object. The radius D  922  may be determined based on the heuristic distance scaling factor, which may be increased, based on the example in  FIG. 8 . In  FIG. 9 , the radius D  922  may be determined using a heuristic distance scaling factor of K=75, based on the example in  FIG. 8 . The music object playback logic  340  may randomly select a next music object to play from the music objects ( 512  and  518 ) in the next music object selection space  920 . The music object playback logic  340  may select a next music object to play that is more like or more similar to the currently playing music object  510 . The music objects  514  and  516  lie outside the next music object selection space  920 . The music object playback logic  340  may not select the music objects  514  and  516  because they are located at distances from the music object  510  that are greater than the radius D  922 , which may represent the maximum heuristic distance measure. 
         [0062]    In  FIG. 3 , when the listener selects an action, such as by using the user interface  304 , the system  300  may display the next music object on the display  240  and/or the interface display  305 . The system  300  may play the next music object on the speakers  380 . 
         [0063]    The system  300  may also include special purpose processors. For example, one or more Digital Signal Processors (DSPs)  360  may be provided. The DSPs  360  may digitally manipulate signal samples that determine the sound output from one or more speaker system speakers  380 , including applying signal processing algorithms, or taking other processing steps. The DSPs  360  may interface with driver logic  370 , such as pre-amplifiers, amplifiers, signal conditioners, or any other logic that influences an audio signal delivered to the speakers  380 . 
         [0064]      FIG. 10  illustrates an example graphical user interface  1000 . The graphical user interface  1000  accepts input for the music playback system  300 . The graphical user interface  1000  may be presented on a display  1002  that presents data related to applications, group listings, and music object listings. For example, the display  1002  may present windows or display panes, such as an application menu  1003 , a group list menu  1004 , and a playing music object description  1005 . Other data may be presented. Other display formats may be adopted, such as a command line interface, a speech recognition transcription or handwriting recognition transcription pane, or other display formats. The display  1002  may receive stylus input, such as through a touch screen or tablet PC interface. The display  1002  may include the display  240 , or the interface display  205 . 
         [0065]    The graphical user interface  1000  may include one or more user interface elements  1010 - 1014 . The user interface elements  1010 - 1014  may be assigned functions related to playback of the organized groups of music objects. For example, the graphical user interface  1000  may include a “play to end” user interface element  1010  that allows playing a music object to play until completion of the music object; a “next song” user interface element  1011  that allows selecting a next music object from the group of music objects; a “more like this” user interface element  1012  that allows selecting a next music object that is more similar to or “more like” a playing music object; and a “less like this” user interface element  1013  that allows selecting a next music object that is less similar to or “less like” a playing music object. The graphical user interface  1000  may also include a user interface selector element  1014  to select other actions, to repeat a previous action, to access other data display options, and/or other options. The user interface elements  1010 - 1014  may be defined in a liquid crystal display, or configured as a raster image on a pixelated CRT screen. 
         [0066]    When the listener selects the user interface elements  1010  or  1011 , the system  300  may select the next music object from the existing music object selection space. For example, the system  300  may determine the heuristic distance scaling factor K to be 50, such that about 50% of the maximum heuristic distance measures will be of 1 or less, and the largest maximum distance is less than 1.5. Once the maximum distance D has been chosen, a music object is randomly chosen that has a maximum heuristic distance measure less than or equal to D from the current music object. As a result, subsequent music objects may be similar to the current music object. 
         [0067]    When the listener selects the “more like this” user interface element  1012 , the system  300  may select the next music object by reducing the next music object selection space.  FIG. 9  shows an example of reducing the selection space, from the original space  510  to the reduced space  910 . The system  300  may adjust the heuristic distance scaling factor K, such as by increasing K to be 75, for example. Other values of K may be used. The next music object may be more similar to the current playing music object, because the average distance between selected music objects is less. 
         [0068]    When the listener selects the “less like this” user interface element  1013 , the system  300  may select the next music object by expanding the next music object selection space.  FIG. 7  shows an example of reducing the selection space, from the original space  510  to the reduced space  710 . The system  300  may adjust the heuristic distance scaling factor K, such as by decreasing K to be 25, for example. Other values of K may be used. The next music object is less similar to the current playing music object, because the average distance between selected music objects is less. 
         [0069]      FIG. 11  illustrates the acts the music object organization system  200  takes to group music objects based on a music object characteristic. The acts illustrated in  FIG. 11  may be implemented by the elements of  FIG. 2 , such as the processor  202 , the co-processor  203 , instructions or logic retained in the system memory  206 , the databases  208 , and/or programs or logic retained in modules coupled with the system  200 . For example, the organization logic  230  may be implemented with processor executable instructions that implement the acts shown in  FIG. 11  when executed by the processor  202 . The system  200  may initialize by loading music objects, processing music object characteristics and/or network connections (Act  1102 ). The system  200  may prompt for user input during initialization, or the system  200  may execute a boot script to initialize. The system  200  may determine if the music objects are associated with a music object characteristic (Act  1104 ). The system  200  may determine if the music objects are tagged or associated with a genre, artist, album, composer, beats-per-minute measure, music object length, time period and/or a user-defined field. The system  200  may process an ID3 tag or other metadata container associated with the music objects. 
         [0070]    If the music objects are not associated with a music object characteristic, the system  200  may prompt the listener to enter music object characteristics for the music objects (Act  1106 ). In some systems, the listener may be prompted to locate a data file containing music object characteristics to associate with the music objects, or to select an ID3 tag or other identifier containing music object characteristics. If the music objects are associated with a music object characteristic, the system  200  may determine an initial set of groups based on the music object characteristic (Act  1108 ). The system  200  may use the initial groups to assign music objects to the groups based on the music object characteristics associated with the music objects (Act  1110 ). The listener may be prompted to select a music object characteristic to use for the initial groups. In some systems, the system  200  uses a determined music object characteristic and/or a heuristic map to determine the initial groups. 
         [0071]    When the initial groups are determined, the system  200  processes the music objects to satisfy a clustering criterion for the music objects, such as to minimize a heuristic distance measure between the music objects within the group (Act  1112 ). As discussed above, the system  200  may use an expectation-maximization process, such as a K-means process, to process the music objects. The system  200  processes the music objects, as represented in a vector space determined by the music object characteristic associated with the music object, to minimize the heuristic distance measure. The heuristic distance measure between any two music objects in the vector space may be defined as a geometric distance between the music objects, such as a scalar magnitude of a vector between the two music objects in the vector space, or other measure. 
         [0072]    The system  200  may, at a stage of the processing, determine if the heuristic distance measures between the music objects within the group are minimized (Act  1114 ). If the heuristic distance measures are minimized, the system  200  may complete the processing. The system  200  may assign channel names to the groups of music objects (Act  1118 ). The system  200  may assign heuristic names to the groups of music objects based on the heuristic map, from a list of channel names associated with the groups of music objects, or by operator selection. The system  200  may present the channel names to the listener on a GUI for selection (Act  1120 ). If the heuristic distance measures are not minimized, the system  200  may determine if a determined stopping threshold has been reached (Act  1116 ). The system  200  may use a maximum number of process iterations to determine when to stop the object assignment process. The system  200  may use the determined stopping threshold to prevent the system  200  from iterating without bound. The system  200  may return an error message if the stopping threshold has been reached. The system  200  may then present the group of music objects as determined by the system  200  at the time the system  200  ends processing. If the stopping threshold has not been reached, the system  200  may continue to Act  1112  to process the music objects. 
         [0073]      FIG. 12  shows the acts the music object organization system takes to satisfy a clustering criterion for the music objects. The system  200  may use a K-means process to assign the music objects. The system  200  assigns music objects to an initial set of groups (Act  1202 ). The system  200  may use a heuristic map, such as example heuristic map  100 , to assign the music objects to the initial set of groups. The system  200  may use music object characteristics associated with the music objects to determine the initial groups. Alternatively, the system  200  may prompt the listener to select a list of initial groups. Vector space diagram  1203  illustrates four groups ( 1220 - 1223 ) of music objects. For example, based on the initial list of groups, there are seven music objects initially assigned to group  1220 , five music objects initially assigned to group  1221 , five music objects initially assigned to group  1222 , and six music objects initially assigned to group  1223 . Other initial groupings may be possible. 
         [0074]    The four groups ( 1220 - 1223 ) may represent genres of the music objects. Example genres include, but are not limited to, pop, oldies, country, alternative, rock, classical, hip-hop, rap, jazz, opera, contemporary, heavy metal, or other genres. The genres may be determined by an ID3 tag, metadata container, or other identifier associated with the music objects, or may be determined based on listener input. The four groups ( 1220 - 1223 ) may represent other music object characteristics, such as an artist, album, composer, music object length, beats-per-minute count, time period, tempo, a user-defined field, or other music object characteristics. The number of possible groups in the vector space is likewise not limited to four, but may include lesser or greater numbers of groups. 
         [0075]    The system  200  may calculate a “centroid” measure for each group of music objects, based on the initial groups (Act  1204 ). Vector space diagram  1205  illustrates the four centroids of the music objects in the initial groups ( 1220 - 1223 ), illustrated as crosses ( 1233 - 1236 ). The centroid for each group may be determined using a relation such as: 
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         [0076]    Eqn. 4 represent the x-coordinate of the centroid of the group g, and Eqn. 5 represents the y-coordinate of the centroid of the group g, where (x,y) are coordinates locating the music objects in the vector space determined by the music object characteristics, and n is the number of music objects in group g. Eqns. 4-5 may use a weighting factor w i  for each music object. The weighting factor w i  may represent a rating, importance factor, or other scaling factor desired by the listener. The system  200  may use ethnomusical preferences, such as maintaining the track order within a performance (e.g., track order in an opera). The weighting factor w i  may be adapted to place a next logical track in an album closer in distance than other tracks. The weighting factor w i  may take on a value of 1. In  FIG. 9 , centroid  1233  represents the centroid of the music objects for group  1220 , centroid  1234  represents the centroid of the music objects for group  1221 , centroid  1235  represents the centroid of the music objects for group  1222 , and centroid  1236  represents the centroid of the music objects for group  1223 . Distances between the centroids ( 1233 - 1236 ) and the music objects within a group are represented as solid lines. 
         [0077]    The system  200  then assigns each music object to a group associated with a closest centroid (Act  1206 ). Vector space diagram  1205  illustrates an illustrative situation where three music objects are closer to a different initial group&#39;s centroid (indicated by dashed lines between a music object and a centroid). Music objects  1240  and  1241  are closer to the centroid of group  1222  than to the centroid of group  1220 . Music object  1242  is also closer to the centroid of group  1222  than to the centroid of group  1223 . The system  200  then assigns music objects  1240  and  1241  to group  1222 , and assigns music object  1232  to group  1222 . Other sets of music objects and music object characteristics may generate different placements of the music objects, and different distances between the music objects and the centroids of the groups. 
         [0078]    The system  200  may determine if new groups are created based on assigning each music object to the group associated with a closest centroid (Act  1208 ). If no new groups are created, then the system  200  may determine that the music objects within the groups are assigned such that the heuristic distance measures between the music objects are minimized, and the process ends. The groups may be determined to be the groups to present to the listener for selection. 
         [0079]    If new groups are created based on assigning each music object to the group associated with a closest centroid, then the system  200  may calculate centroids for the newly created groups (Act  1200 ). The system  200  may use Eqns. 4-5 to determine the new centroids. Vector space diagram  1207  illustrates example new groups ( 1251 - 1254 ) based on the re-assigned music objects from vector space diagram  1205  and Act  1206 . Newly determined centroids ( 1224 ,  1227 ,  1228  and  1229 ) are illustrated in vector space diagram  1207 , along with vectors (indicated by solid lines) representing distances between the music objects and the centroids ( 1224 ,  1227 ,  1228  and  1229 ). The system  200  may then determine if convergence of the process has been reached (Act  1212 ). For the k-means process, convergence may be reached when no music object switches groups during a process iteration. Alternatively, convergence may be reached when centroid positions do not change after assigning the music objects to the nearest centroids and re-calculating the centroids. If convergence is not reached, the system  200  continues with Act  1206 , where the system  200  assigns each music object to the closest centroid associated with a group. In vector space diagram  1207 , as an example, no music objects change groups. Therefore, convergence will be reached after the next iteration and the process ends. In other example music object configurations, music objects may switch groups over several iterations. The system  200  may also use a stopping threshold to prevent the system  200  from boundless iterations. The system  200  may use a determined maximum number of iterations before terminating the process. Other expectation-maximization processes may use different steps or use different criterion for terminating the process. 
         [0080]      FIG. 13  shows the acts the music object playback system takes to select and play a next music object. The acts illustrated in  FIG. 13  may be implemented by the elements of system  300 , such as the processor  202 , the co-processor  203 , instructions or logic retained in the system memory  306 , the databases  308 , or retained in a computer-readable medium interfaced or coupled with the system  300 . After the music objects are organized based on the music object characteristic, as described in  FIG. 11 , the system  300  may present a list of groups of music objects for the listener to select (Act  1302 ). The system  300  may present the list on the display  240 , the user interface  304 , or may output the list as an aural output on the speakers  380 . The system  300  may determine if the listener selects a group from the list of groups (Act  1304 ). The system  300  may receive listener input through the user interface  304 , such as through selectors  307  or other user input  309 . If the listener does not select a group, the system  300  may prompt the listener to select a group from which to play music objects (Act  1306 ). The system  300  may then present the list of groups again. In some systems, the system  300  randomly selects a group to play. In other systems, the system  300  may use a heuristic factor to select a group to play, such as a previously played group, a group that is rated by the listener, or from a playlist. 
         [0081]    When the listener selects a group to play, the system  300  randomly selects a music object from the group for playback (Act  1308 ). The system  300  may play the music object over the speakers  380 . In some systems, the system  300  selects the music object to play based on a heuristic factor, such as a previously played group, or a group that is rated by the listener, or from a playlist. The system  300  may then determine when the listener selects a next music object to play from the group (Act  1310 ). If the listener does not select a next music object to play, the system  300  may play the current music object until the end of the music object track (“EOT”). In some systems, the system  300  prompts the listener to take an action to select the next music object. 
         [0082]    When the listener selects a next music object to play, the listener is presented with at least four actions to select from (Block  1311 ). The listener may play the current music object to EOT (Act  1314 ). The listener may track to the next music object (Act  1316 ). The listener may select a next music object that is “more like,” or more similar, to the current playing music object (Act  1320 ). The listener may select a next music object that is “less like,” or less similar, to the current playing music object (Act  1324 ). 
         [0083]    When the listener desires to play the current music object to EOT, of if the listener desires to track to the next music object, the system  300  randomly selects the next music object from within the group of music objects (Act  1318 ). As discussed above, the system  300  may also select the next music object based on a heuristic factor. When the listener desires to select a next music object that is “more like,” or more similar to the current playing music object, the system  300  may determine the next music object by reducing the next music object selection space, such as by increasing a heuristic distance scaling factor associated with the group of music objects (Act  1322 ). When the listener desires to select a next music object that is “less like,” or less similar to the current playing music object, the system  300  may determine the next music object by expanding the next music object selection space, such as by decreasing a heuristic distance scaling factor associated with the group of music objects (Act  1326 ). 
         [0084]    The system  300  then may determine a uniform random variable (Act  1328 ). The system  300  may use a random number generator, a seed function, a look-up table, and/or other processes to determine a uniform random number. The system  300  may determine a maximum heuristic distance measure based on the uniform random number and the heuristic distance scaling factor (Act  1330 ). In some systems, the system  300  uses a square root function of the uniform random number and the heuristic distance scaling factor, such as by determining Eqn. 3 above. The system  300  may select the next music object to play using the maximum heuristic distance measure (Act  1332 ). For example, if the listener desires a next music object more similar to or “more like” the current music object, the maximum heuristic distance measure is less than for a desired next music object that is less similar to or “less like” the current music object. The next music object is more likely to be similar to the current playing music object if the maximum heuristic distance measure is smaller than for a desired next music object less similar to the current music object. The maximum heuristic distance measure may also be adjusted to take into account ethnomusical preferences, such as maintaining the track order within a performance (e.g., track order in an opera). The system  300  may place the next logical track in an album closer in distance than other tracks. The system  300  may then continue with further actions selected by the listener from Block  1311 . 
         [0085]    The music object organization system  200  may be adapted to process other types of object formats. The system  200  may be adapted to organize multimedia objects such as video or picture object formats, including WMV, MPEG, JPEG, GIF, BMP, AVI, TIFF, or other multimedia object formats. The multimedia object formats may be associated with an object characteristic that may be used to organize the objects heuristically. For example, video or picture objects may be associated with an object characteristic associated with events (e.g. vacation, birthday, graduation, sporting events, or other events), locations, time recorded, object content (e.g., museum exhibits, still life, action, people, animals, nature or other content indicia), and/or other object characteristics. The system  200  may be adapted to organize the multimedia objects based on the object characteristics using the process described in  FIG. 11 . The playback system  300  may also be adapted to present the multimedia objects using similar playback actions. 
         [0086]    The systems  200  and  300  may also be adapted for other multimedia object content, such as entry objects in address books or phone books. Instead of a flat list, or a most recently called list, the system  200  may be adapted to group phone numbers together by how the user has statistically called them. Once enough statistics have been gathered, the system  200  may generate groups like ‘Friends’, ‘Work’, ‘Restaurants’, etc. 
         [0087]    The methods shown in  FIGS. 11-13  may be encoded in a signal bearing medium, a computer readable medium such as a memory, programmed within a device such as one or more integrated circuits, or processed by a controller or a computer. If the methods are performed by software, the software may reside in a memory resident to or interfaced to the music object organization system  200 , the music object playback system  300 , a communication interface, or any other type of non-volatile or volatile memory interfaced or resident to the system memory  204  or  304 . The memory may include executable instructions for implementing logical functions. Logic or a logical function may be implemented through digital circuitry, processor executable instructions, through analog circuitry, or through an analog source such as through an analog electrical, audio, or video signal. Executable instructions may be embodied in any computer-readable or signal-bearing medium, for use by, or in connection with an instruction executable system, apparatus, or device. Such a system may include a computer-based system, a processor-containing system, or another system that may selectively fetch instructions from an instruction executable system, apparatus, or device that may also execute instructions. 
         [0088]    A “computer-readable medium,” “machine-readable medium,” “propagated-signal” medium, “product,” “computer program product,” and/or “signal-bearing medium” may comprise any means that contains, stores, communicates, propagates, or transports software for use by or in connection with an instruction executable system, apparatus, or device. The machine-readable medium may selectively be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. A non-exhaustive list of examples of a machine-readable medium includes: an electrical connection having one or more wires, a portable magnetic or optical disk, a volatile memory such as a Random Access Memory “RAM” (electronic), a Read-Only Memory “ROM” (electronic), an Erasable Programmable Read-Only Memory (EPROM or Flash memory) (electronic), or an optical fiber (optical), a signal that propagates through space or along an optical or electrical conductor. A machine-readable medium may also include a tangible medium upon which software is printed, as the software may be electronically stored as an image or in another format (e.g., through an optical scan), then compiled, and/or interpreted or otherwise processed. The processed medium may then be stored in a computer and/or machine memory. 
         [0089]    While various embodiments of the invention have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the invention. Accordingly, the invention is not to be restricted except in light of the attached claims and their equivalents.