A method of producing dynamic controllable data composites from two or more data segments includes: building or training one or more function mappers to map between one or more extracted feature envelopes sets from the original data and one or more general parametric representations of the data; combining the extracted feature envelopes or the function mappers using two or more audio segments; and feeding the extracted feature envelopes or combined feature envelopes to the function mappers to obtain synthesis parameters to drive a synthesis process.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-29. (canceled) 30. A method for remotely generating synthesis parameters based on captured data, the method comprising: training one or more function mappers to map between one or more feature envelopes that are extracted from captured data and one or more corresponding parametric representations of the captured data, the one or more feature envelopes corresponding to one or more characteristics of the captured data, and the one or more parametric representations corresponding to an analysis-synthesis process that fits the one or more parametric representations to the captured data; and generating synthesis parameters for a synthesizer corresponding to the one or more parametric representation, by remotely feeding the feature envelopes to the corresponding function mappers in a client-server architecture, the outputs of the function mapper being used as synthesis parameters to drive a synthesis process. 31. The method of claim 30, wherein the captured data corresponds to perceptual data, and the characteristics of the captured data correspond to perceptual characteristics. 32. The method of claim 30, wherein the captured data is audio and the one or more feature envelope sets that are extracted from the data segments of the captured data are based on at least one of pitch, loudness or brightness. 33. The method of claim 30, wherein the captured data is audio and the one or more corresponding parametric representations of the captured data include values for spectral peaks. 34. The method of claim 30, wherein the one or more function mappers include at least one neural network that is trained adaptively in the training process for the one or more function mappers. 35. The method of claim 30, wherein remotely controlling the features envelopes includes transmitting feature envelopes over a data network to be fed to the function mapper. 36. The method of claim 30, wherein remotely controlling the features envelopes includes transmitting parameters that modify the original envelopes fed to the function mapper. 37. The method of claim 30, wherein the client-server architecture includes a smartphone and a computer. 38. The method in claim 30, wherein the client-server architecture includes a first computer program module that includes a graphical user interface and a second computer program module that includes a game engine. 39. The method of claim 30, wherein the captured data is body-motion data and the one or more feature envelope sets that are extracted from the data segments of the captured data are based on at least one of position, rotation angle, or velocity for one or more body joints or for one or more end effectors. 40. The method of claim 30, wherein the captured data is body motion data and the one or more parametric representations of the captured data include at least one of position, rotation angle, or velocity for one or more body joints or for one or more end effectors. 41. The method of claim 30, wherein the captured data is texture data and the one or more characteristics of the captured data are based on neighboring pixel intensity values for a given location. 42. The method of claim 30, wherein the captured data is texture data and the one or more corresponding parametric representations of the captured data include pixel intensity values for a given location. 43. The method of claim 30, wherein the one or more function mappers are trained by using a magnitude-based weighting in an error function for a training process for the one or more function mappers. 44. An apparatus for remotely generating synthesis parameters based on captured data; the apparatus including at least one computer that performs operations for computer-implemented modules including: a training module for training one or more function mappers to map between one or more feature envelopes that are extracted from captured data and one or more corresponding parametric representations of the captured data, the one or more feature envelopes corresponding to one or more characteristics of the captured data, and the one or more parametric representations corresponding to an analysis-synthesis process that fits the one or more parametric representations to the captured data; and a synthesis module for generating synthesis parameters for a synthesizer corresponding to the one or more parametric representation, by remotely feeding the feature envelopes to the corresponding function mappers in a client-server architecture, the outputs of the function mapper being used as synthesis parameters to drive a synthesis process 45. A non-transitory computer-readable medium that stores a computer program for remotely generating synthesis parameters based on captured data, the computer program including instructions that, when executed by a computer, cause the computer to perform operations including: training one or more function mappers to map between one or more feature envelopes that are extracted from captured data and one or more corresponding parametric representations of the captured data, the one or more feature envelopes corresponding to one or more characteristics of the captured data, and the one or more parametric representations corresponding to an analysis-synthesis process that fits the one or more parametric representations to the captured data; and generating synthesis parameters for a synthesizer corresponding to the one or more parametric representation, by remotely feeding the feature envelopes to the corresponding function mappers in a client-server architecture, the outputs of the function mapper being used as synthesis parameters to drive a synthesis process. 46-53. (canceled)