Method for automatically controlling electronic musical...

Music – Instruments – Electrical musical tone generation

Reexamination Certificate

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C084S602000, C084S609000, C084S649000

Reexamination Certificate

active

06313390

ABSTRACT:

FIELD OF THE INVENTION
The present invention generally relates to a computer-based method of constructing a multi-level data-structure called ‘levelbase’ from at least one sequence of digital codes, each code representing one or more predetermined features of a musical note, a fraction of a musical note, or group of musical notes, and searching the levelbase according to predetermined criteria while simultaneously (in real-time) generating digital codes to automatically control electronic musical instruments, and the invention also relates to a system that implements the above mentioned method.
BACKGROUND OF THE INVENTION
In history people have always been interested in methods and devices, be it mechanical, electronic or other, to automate the composing and/or playing of music. Especially since the so called MIDI-standard (Musical Instrument Digital Interface) was established in 1983 (MIDI 1.0 specification, Document No. MIDI-1.0, August 1983, International MIDI association), which defines a standard interface through which synthesizers, rhythm machines, computers, etc. can be linked together, substantial research has been devoted to the automated composing and/or playing of music.
Most of the resulting methods and devices were meant to automatically generate accompaniments to a solo instrument, to compose background music for films, plays and presentations or to produce music to entertain customers and to create the desired atmosphere in for instance restaurants or shops, commonly referred to as ‘elevator music’ or ‘quiet music’.
One way of producing music automatically is to use an electronic system that produces so-called synthesized music. These systems generally comprise one or more electronic musical instruments or synthesizers and an automatic device producing control signals for them, which signals consist of digital code sequences in the case of this invention.
In the prior art methods and devices have been disclosed that use statistic approaches and employ for instance Markov processes, in which each musical note, fraction of a musical note or group of musical notes is treated as a single stochastic state in a sequence of states. Music is generated by probability functions stored in memory, starting from an initial musical code sequence (state) to which is added a successor code sequence having the highest probability according to the probability function. The Markov process is now in a new state and (part of) the increased sequence is used as a new initial code sequence so that the process endlessly generates control codes for one or more electronic musical devices that produce the resulting music accordingly. Additional rules are however necessary to produce typical musical structures and generate agreeable music. This method requires large amounts of training material to form the necessary conditional probability density functions in addition to the fact that synthesized music based on stochastic processes is usually quite monotonous.
Other prior art methods use heuristical rules based on musical expertise to produce such digital code sequences to control musical instruments. This technique is frequently employed in the field of artificial intelligence and is for instance disclosed in U.S. Pat. No. 4,926,737 issued to Minamitaka. The document U.S. Pat. No. 5,418,323 issued to Kohonen, discloses a method for controlling an electronic musical device that does not use heuristical rules, but forms a rulebase automatically on the basis of digital training sequences. The algorithm disclosed by Kohonen is based on finding a set of ‘grammatical’ rules in a sequence of codes representing musical information. Kohonen uses so-called DEC (Dynamically Expanding Context) grammars, which were originally developed for on-line speech recognition. Human speech consists however of sentences that form grammatically well formed pieces of information of limited length, while musical ‘sentences’ can be structures of undetermined length (e.g. long improvisations in Jazz). Because of this difference the rule base will haves to be updated very frequently during the training phase.
The construction of the rulebase from training material can either be static, i.e. based on the input and (batch)processing of existing (previously recorded) code sequences representing musical information, or dynamic, i.e. based on real-time input of codes by an electronic musical device, for instance an electronic musical instrument. The Kohonen algorithm is reasonably efficient in the case of input and batch processing of existing training sequences but, given the current performance of PC hardware and software, inefficient to use for real-time improvisation, because of the necessary frequent updates of the rule base.
The present invention avoids or alleviates certain problems and drawbacks associated with the prior art some of which are mentioned above.
SUMMARY OF THE INVENTION
The object of the present invention is to provide a method for fast (real-time) storage and retrieval of digital code sequences representing one or more predetermined features of a musical note, a fraction of a musical note, or group of musical notes, with the aim to automatically control electronic musical devices and thereby to avoid or alleviate certain problems associated with the prior art.
The above mentioned method of the invention comprises at least the following steps:
storing at least one digital training sequence consisting of one or more digital codes, each code representing one or more predetermined features of a musical note, a fraction of a musical note, or group of musical notes, in a multi-level datastructure called levelbase, consisting of information objects which are ranked in the levelbase on the basis of certain predetermined criteria, whereby every single object either contains a combination of one of the digital codes of said training sequence and so called related data, in the basic version of the invention being the next code in the sequence, or a combination of one of the digital codes of said training sequence and a reference to a sub-levelbase consisting of information objects related to the predecessing codes of the current digital code sequence, effectively forming a deeper level in the datastructure, whereby said storing of the digital training sequence can be conducted in a real-time manner, for instance during generation of the training sequence by an electronic musical device;
automatically generating a digital output sequence consisting of digital codes, each code representing one or more predetermined features of a musical note, a fraction of a musical note, or group of musical notes, including at least the steps of:
determining search and matching criteria according to which the levelbase is to be searched;
generating a digital output sequence by conducting sequential nearest-neighbour-matching of elements of an input sequence of digital codes each representing one or more predetermined features of a musical note, a fraction of a musical note, or group of musical notes and the objects in the levelbase, according to said predetermined search and matching criteria;
converting said automatically generated digital output sequence into digital control signals, suitable for controlling musical devices;
automatically controlling the musical devices with said digital control signals.
According to another realization the method according to the present invention is additionally characterized by the fact that separate features of a musical note, a fraction of a musical note, or group of musical notes, being for instance harmonic information or metrical information, are stored in separate levelbases, and the digital output sequence is generated by searching these levelbase simultaneously according to predetermined search criteria, enabling for instance superposition of different musical styles and arrangements of musical compositions.
Furthermore the method according to the invention can be characterized by the fact that prior to storage the training sequences are grouped on the basis of their related data,

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