Data processing: artificial intelligence – Neural network – Learning task
Reexamination Certificate
1998-06-16
2001-09-18
Powell, Mark (Department: 2122)
Data processing: artificial intelligence
Neural network
Learning task
C706S015000, C706S016000
Reexamination Certificate
active
06292791
ABSTRACT:
FIELD OF THE INVENTION
This invention relates generally to music synthesis by computers, and more specifically, to the synthesis of sounds produced by plucked string instruments.
BACKGROUND OF THE INVENTION
Realistic electronic sound synthesis is becoming increasingly important, in part due to the rapid development of multimedia and virtual reality applications. Two traditional techniques for synthesizing music are frequency modulation (FM) synthesis and Wavetable (sample-and-play) synthesis. Generally, FM methods generate sounds using sinusoidal oscillators. Wavetable methods store sound segments produced by actual instruments and synthesize the instruments by playing back digitally processed sequences of the sound segments. Both FM and Wavetable sound synthesis systems have their drawbacks. For instance, although FM synthesis can produce a wide range of interesting sounds and Wavetable synthesis is able to produce tones having timbres close to that of musical instruments, both methods are deficient at producing the large dynamic sound range capable of being produced by acoustic instruments.
An alternate, more recent technique, for electronically synthesizing sound use, digital waveguide filters (DWFs). DWFs mathematically simulate, using a bi-directional delay line with modification filters, wave propagation on a plucked string. That is, DWFs physically model wave propagation occurring in the string. A number of patents and publications describe music synthesis using DWFs, including: U.S. Pat. No. 4,984,276 to Julius O. Smith; U.S. Pat. No. 5,212,334 to Julius O. Smith; U.S. Pat. No. 5,448,010 to Julius O. Smith; and the publication by Julius O. Smith, “Physical Modeling Using Digital Waveguides,”
Computer Music Journal
, Vol. 16, No. 4, 1992. The contents of these three patents and the publication are incorporated by reference herein.
DWFs provide a “virtual string” that can authentically simulate the dynamics of a plucked string. However, DWF modeling requires a number of physical string parameters that are difficult to measure directly. For example, parameters describing the amount of energy loss (the energy loss constant) and traveling wave reflection (reflection coefficient) at any particular point in the string often must be determined by cumbersome trial-and-error methods.
There is, therefore, a need in the art to be able to synthesize sounds produced by a plucked string using a “virtual string” that can be modeled with easily obtainable parameters.
SUMMARY OF THE INVENTION
Objects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objects and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the appended claims.
To achieve the objects and in accordance with the purpose of the invention, as embodied and broadly described herein, a first aspect consistent with the present invention is directed to a method of synthesizing sounds produced by a plucked string. The method comprises a plurality of steps, including, generating an input waveform corresponding to an initial plucking of a string; initializing nodes of a neural network with values based on the input waveform; iteratively changing the node values; outputting a waveform based on selected ones of the node values at a plurality of the iterations; and generating sound based on a sequence of the output waveforms, the generated sound simulating the sound made by this particular plucked string.
A second method consistent with the present invention is directed to simulating the behavior of traveling waves in a string. The method comprises the steps of: stimulating a recurrent neural network, the recurrent neural network including groups of neurons arranged to simulate wave propagation through a scattering junction; and iteratively evaluating the neurons of the neural network.
A third method consistent with the present invention is directed to a method of training a recurrent neural network. The method comprises the steps of: measuring a time-varying sequence of vibrational values of a plucked string; initializing the recurrent neural network by setting displacement neurons in the neural network to values based on the measured time-varying sequence; iteratively calculating values of neurons in the recurrent neural network based on the initial values of the displacement neurons; calculating a total cost function value based on the measured sequence of vibrational values and the values of the displacement neurons obtained in the step of iteratively calculating values; and adjusting weights corresponding to the neurons in the recurrent neural network when the total cost function is above a predetermined threshold.
Other aspects consistent with the present invention are directed to a computer readable media containing instructions for execution the methods similar to the first, second, and third aspects of the invention.
Another aspect consistent with the present invention is directed to a recurrent neural network having displacement nodes, arrival nodes, and departure nodes. The departure nodes receive values output from the displacement nodes and values output from arrival nodes that are associated with a displacement node corresponding to a first predetermined location on the string, the departure nodes outputting values to arrival nodes that are associated with a displacement node corresponding to a second predetermined location on the string.
Still another aspect of the present invention is directed to an electronic apparatus for synthesizing sounds produced by plucked string instruments. The apparatus comprises: a memory for storing virtual models of strings that are to be synthesized; a waveform generation section for generating initial waveforms stimulating the virtual models; a scattering recurrent network synthesis section for generating time-varying waveforms using a recurrent neural network with neurons assigned the weighting values stored in the memory; and a speaker for outputting the synthesized time-varying waveforms.
REFERENCES:
patent: 4984276 (1991-01-01), Smith
patent: 5138924 (1992-08-01), Ohya et al.
patent: 5212334 (1993-05-01), Smith, III
patent: 5308915 (1994-05-01), Ohya et al.
patent: 5448010 (1995-09-01), Smith, III
patent: 5986199 (1992-08-01), Peevers
Liang, S.F. and Su, Alvin, W.Y, Dynamics Modeling of Musical String by Linear Scattering Recurrent Network, Joint Conference of 1996 International Computer Symposium, Dec. 19-21, ICS '96 Proceedings on Artificial Intelligence, Taiwan, Jan. 1996, pp. 263-270.*
Su, A.W.Y.; Liang San-Fu, Synthesis of plucked-string tones by physical modeling with recurrent neural networks, Multimedia Signal Processing, Jan. 1997., IEEE First Workshop on, 1997, pp.: 71-76.*
Sheng-Fu Liang; Su, A.W.Y.; Chin-Teng Lin, Model-based synthesis of plucked string instruments by using a class of scattering recurrent networks, Neural Networks, IEEE Transactions on, vol.: 11 1, Jan. 2000, pp.: 171-185.*
Bresin, R.; Vedovetto, A., Neural networks for a simpler control of synthesis algorithm of musical tones and for their compression, Time-Frequency and Time-Scale Analysis, Jan. 1994., Proceedings of, the IEEE-SP International Symposium on, 1994, p.: 628.*
Burr, D.J.; Miyata, Y., Hierarchical recurrent networks for learning musical structure, Neural Networks for Processing [1993]III. Proceedings of the, 1993 IEEE-SP Workshop, Jan. 1993, pp.: 216-225.*
Helweg, D.A.; Roiblat, H.L.; Nachtigall, P.E., Using a biomimetric neural net to model dolphin echolocation, Artificial Neural Networks and Expert Systems, Jan. 1993. Proceedings., First New Zealand International Two-Stream Conference on, 1993, p.: 247.*
Masri, P.; Canagarajah, N., Synthesis from musical instrument character maps, Audio and Music Technology: The CHallenge of Creative DSP (Ref. No. 1998/470), IEE Colloquium on, Jan. 1998, pp.: 10/1-10/5.*
John W. Gordon; System architectures for compu
Chung Tien-Ho
Liang Sheng-fu
Su Wen-yu
Finnegan Henderson Farabow Garrett & Dunner L.L.P.
Industrial Technology Research Institute
Powell Mark
Starks Wilbert
LandOfFree
Method and apparatus of synthesizing plucked string... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method and apparatus of synthesizing plucked string..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and apparatus of synthesizing plucked string... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-2499238