support_tags > cabin.tubecell.net

Music stand extender
2010-03-11
AbstractA device to extend the support area of a music stand, where the music stand comprises a lower horizontally extending support member having a support flange to engage the lower edge of music sheets, an upstanding central main support bar, and a plurality of collapsible support struts. The present invention comprises a separate upper support section adapted to be mounted to the central support bar and having two laterally extending upper arms. There are a pair of extension wings slide mounted to the stand, with each wing having a lower flange and retaining portion engaging the lower support member of the stand, and an upper retaining portion engaging a related one of the support arms. The two extension wings can be moved toward one another to a middle position, and can be moved slideably to an extended position to provide increased support area. In a second embodiment, the stand is adapted to be mounted from a table top.ClaimsWhat is claimed is:

1. An extension apparatus to enlarge the support area of a stand, such as a music stand, said stand having a main central support member and a lower support member extendinglaterally from the central support member, said apparatus comprising:

a. an upper support section adapted to be mounted to said stand at an upper end thereof, said support section comprising two laterally and oppositely extending support arms,

b. a pair of extension wings adapted to be mounted to said stand and to said upper support section, each wing comprising:

1. a panel portion,

2. a lower support flange means adapted to be positioned against the lower support member of the stand,

3. a lower retaining portion adapted to engage said lower support member to permit lateral slide motion between the wing and the lower support member,

4. an upper retaining portion adapted to engage a related support arm to permit lateral slide motion between the wing and the related support arm,

whereby with said upper support section mounted to said stand, and with each of the extension wings mounted to the stand and to the upper support section, the two wings can be moved horizontally inward to a middle position, or horizontallyoutwardly to an expanded position to provide an expanded support area for the stand.

2. The apparatus as recited in claim 1, wherein said support section comprises a middle mounting leg having securing means thereon and adapted to be connected to said main central support member of the stand.

3. The apparatus as recited in claim 2, wherein said securing means comprises a pair of retaining lips adapted to releasably engage edge portions of said main central support member.

4. The apparatus as recited in claim 1, wherein said upper retaining portion of each of the extension wings comprises an upper retaining flange defining a retaining slot to receive a related one of said support arms.

5. The apparatus as recited in claim 4, wherein each of said upper retaining flanges and its related arm is provided with mating stud and slot means to provide a sliding stud and slot connection between the arms and the retaining flanges.

6. The apparatus as recited in claim 1, wherein each lower retaining portion comprises a lower retaining flange positioned below said support flange means and defining with said support flange means a lower slot to receive a support flange ofthe lower support member of the stand.

7. The apparatus as recited in claim 6, wherein each lower retaining portion further comprises an upstanding lip secured to a rear portion of said retaining flange, with said retaining flange and said lip defining an angled slot to receive thelower support member of said stand.

8. The apparatus as...

Music intelligence universe server
2010-03-04
service.

[0091] Finally, to obtain a list of recommended songs, classic EVE evaluates all the songs with the music taste estimated vector and sorts them decreasingly by grade.

[0092] As described above, classical EVE has some limitations that arise from its linear learning capability and because it was designed to be able to generate ideal questions while in the music framework, the number of songs is limited and a compromise has to be selected because not all ideal questions (pairs of songs) exist. The main limitation, however, of classic EVE is that input songs must be normalized. That means that instead of having two descriptors, by normalizing we obtain a single descriptor that is related to the ratio between them and it is not possible to go back. In the real world, this would mean that two songs one with small values of tempo and rhythm and the other with high values of both descriptors would be considered by the system as being very similar. In the same way, the estimated vector cannot isolate between rhythm and tempo as the estimated vector is again a relation between them. It is not possible to differentiate between the following two music tastes: [0093] "I like high tempo and high rhythm" [0094] "I like low tempo and low rhythm"

[0095] This is solved by using a technique called Kernelization derived from the Support Vector Machines. It can be viewed as keeping a linear learning method like Eva but instead of using the original space (in our case, bidimensional for tempo and rhythm), using an extended space usually with more dimensions. This allows EVE to learn non-linear music taste vectors. The drawback of adding more dimensions, of course, is the increase of the uncertainty in the estimated taste.

[0096] It is based on a transformation from two input variables [0, 1]脳[0, 1] to three variables. The output 3D vector is normalized using a cylindrical transformation. From a geographical point of view, the kernel maps a 2D quadrant into the surface of a quadrant of a sphere. This is depicted in FIG. 16.

[0097] The first step of the kernelization adds a margin to the data in order to avoid null border values in order to maintain nulls out of the transformation chain. gx=0.005 0.99sx gy=0.005 0.99sy

[0098] In classic EVE, only the angle between the two variables was significant. In the current algorithm, the non-linearity comes from also using the module of the vector. This makes it possible to learn in which part of the Music Universe the user taste resides. r= {square root over (gx2 gy2)},r>1鈫抮=1 胃=atan2(gy,gx)

[0099] The 2D to 3D transformation is performed mapping input variables into spherical coordinates: k r = 1 k 胃 = 蟺 2 脳 r k 桅 = 胃

[0100] The operational space for classic EVE is Cartesian and kernelized variables are finally obtained by the Spherical-to-Cartesian transformation: kx=k.sub.r cos k蠁 sin k胃k.sub.y=k.sub.r sin k蠁 sin k胃k.sub.z=k.sub.r cos k胃

[0101] In summary, the Kernelized Eva is a classic EVE that operates within a universe that has one more dimension. This makes possible to learn non-linear music tastes in a controlled manner as the convergence of classic EVE is assured because it is a linear algorithm. The estimated vector has, therefore, three dimensions. Accordingly, the recommendation of songs also must be performed in the kernelized universe. 3D classic EVE is a bit more complicated because there is one more dimension and the region update strategy is not so straightforward. Furthermore, in the selection of pairs of songs an extra degree of freedom has to be taken into consideration.

[0102] Imagine that the database consisted of less than a thousand songs. Then the total number of pair of songs is given by the following expression: N(N-1)/2

[0103] With less than 1000 songs, it is possible to compute all the possible pairs (0.5 Mpairs) and select and store those more adequate for learning. The MIU Server, however, supports music databases of several millions of songs. FIG. 17 shows the number of pairs to evaluate as a function of the total number of songs.

[0104] Looking to the rapidly growing function, it is clear that it is not possible to sweep all pairs to determine the preferable pair of songs for learning. This is the key of the music learning process, defining what a good question means. Intuitively, two songs very similar (with a small Euclidean distance in the Music Universe) cannot form a learning pair because it should be difficult for the user to select the preferred one. This is one of the three criteria: Euclidean distance has to be maximized in song pair selection for the learning process. The second criterion is quite obvious but it has a significant impact in the design. Sometimes, a song has some properties (resides near the border of the music universe for example) that makes it very useful for music taste learning. However, it is very poor for a music interface point of view to repeat songs in consecutive questions so the algorithm needs to have memory. Finally, the last and most important idea: the pair that maximizes music taste learning convergence, besides the song distance, depends on the current state of the algorithm; in other words, it depends on the answers given by the user to previous questions. To summarize these facts, the conditions for song pair selection are: [0105] Maximum Euclidean distance between the songs to help the user sel...