![]() ![]() ![]() The pads go dark and the right arrow begins to flash, but when I hit a pad nothing happens. In Push Pong I’m able to initialize push. This happens for both Push Pong and Live Pong. It loads up and all of the buttons (besides the grid buttons) seem to function properly, however no bouncing points are generated. 9×9 generative grid controlled by mouse/trackpad clicksįigure I’d just answer your questions in one post :),.As a result of this, Live Pong was created with the extra step. Push Pong was designed to be used with Push which has an 8×8 grid, because of the unique way Push Pong sequences notes, a step is missed every time the cell direction changes by hitting the outer grid area, this results in 7 step sequences. Use the Random MIDI Effects before Scale to allow for some variations in Pitch, by placing the effect before Scale, all note variations will still work within the selected scale.įinally, try the Velocity MIDI Effect, I use the ‘Add some Random’ preset, which works great for creating variations in velocity, or how hard or soft the notes are triggered. Although it is possible to create a scale via the editable note grid, you may wish to use the Scale MIDI Effect for a quick way to access a full collection of scale presets. Push Pong has been designed to be used in conjunction with Live’s powerful MIDI effects. Cell mode for individual control per cell.Global mode for control of rate sync, note velocity and length, chance of cells bouncing.8×8 generative grid controlled by Push’s pads.By pressing pads on Push when Live is running, cells are generated which will create notes whenever they reach the outer grid area on Push (top and bottom rows, left and right columns). Push Pong is a generative sequencer built in Max for Live inspired by Batuhan Bozkurt’s excellent Otomata Generative Musical Sequencer. In our case, the new defined sequence multiset kernel can also be seen as a tree kernel.First in a series of game inspired sequencers built for Push, grab the first release for FREE Finally, we prove that the proposed formalization can be generalized to sequence multiset kernels showing the robustness of our approach. In order To highlight the unification aspect of our model, we study the relationship between our general framework and a variety of common sequence kernels. The results reveal that the kernel evaluation using our proposed technique is faster than the one that uses standard intersection. The experiments use the Reuters-21578 collection. Regarding kernel computation efficiency, we propose a forward lookup automaton intersection technique to prevent unsuccessful ε-paths while evaluating the WA computations. The computation of the kernel K ( s, t ) between two strings is the behavior of the intersection weighted automaton A s, t = A s ∩ A t. In fact, the mapping of a string s to a high dimensional feature space can be modeled by a formal power series that can be realized by a weighted automaton (WA) A s representing all subsequences of the string s. We provide a more formal presentation of the framework fully supported with proofs. As a contribution in developing a unified theory of machine learning, in this paper, we complement our previous general framework that deals with sequence kernels, termed weighted automata sequence kernel. In recent years, a significant effort has been devoted to sequence kernels focusing on individual problems, and so devising several approaches. Sequence kernels have been widely used for learning from sequential data.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |