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BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to an electronic musical instrument which generates
parameters specifying a musical tone wave form and controlling a musical
tone wave form generation with the aid of a neural network.
2. Description of the Prior Art
The parameters to control the musical tone in the electronic musical
instrument are a wave form data which specifies the wave form of the
musical tone, an envelope data which specifies an output level of the
musical tone wave form, a pitch data which specifies a tone pitch, etc.
Several musical tone parameters are stored in a memory of the electronic
musical instrument, and can be freely read by a player to play, varying
expression. A part of the musical tone parameters is previously stored in
the memory of the musical instrument before shipping thereof, and
remaining another part of the musical tone parameters can be stored in the
memory before the player plays the instrument.
The musical tone parameters stored in the memory in advance can be freely
selected for playing by the player while playing the instrument. However,
the parameters not stored in the memory cannot be selected. It takes too
much time to set new parameters while the player plays the musical
instrument, so that this setting was impractical. As a result of this the
parameter selection range is too narrow, and play expression is poor.
If more musical tone parameters are stored in the memory for widening the
selection range, larger memory is required and it takes much time to store
the parameters.
The FM (Frequency Modulation) tone source is one of the conventionally
applied tone sources to the electronic musical instrument. The FM tone
source synthesize a musical tone by combining (modulating and adding) four
or six operators each of which can be set basic wave form (sine wave,
triangle wave, etc.), frequency (tone pitch), out put level, envelope,
etc. based on a specific algorithm. This tone source can generate
beautiful and varied musical tones with simple configuration.
However, it has been regarded that it is difficult to generate the musical
tone using the FM tone source according to player's intention. This is
mainly due to that it is difficult to predict the change of the musical
tone based on the change of the operator and the algorithm since the
musical tone is generated by using many parameters and FM modulation.
An example is an electronic musical instrument which is designed to set the
pitch of the musical tone to be generated according to ON/OFF pattern of
several play keys such as electronic musical wind instrument. Generally,
such an electronic musical instrument is provided with a table which
stores the pitch data corresponding to several ON/OFF patterns. This table
is retrieved according to the ON/OFF pattern by player's operation to find
the specific pitch.
In such electronic musical instrument, the ON/OFF pattern of the playing
keys can set only the pitch, but the tone color (wave form) of the musical
tone was constant irrespective of pitch. In case of natural musical
instrument the tone color (wave form) varies delicately depending on the
pitch even when the musical instrument (tone color) is the same, and this
delicate change of the tone color affects significantly expression of the
musical instrument. Moreover, even when the pitch is constant, the tone
color changes if fingering pattern is changed. So as to change the tone
color as discussed above on the electronic musical instrument, generally,
it is necessary to sample the wave form of natural musical instrument for
each ON/OFF pattern of the keys and for each pitch and to read out a wave
form data. However, these wave form data for each pitch need a large
memory capacity to store them, due to which the size of the musical
instrument is increased, and its cost rises.
SUMMARY OF THE INVENTION
It is therefore an object of the present invention to provide a musical
tone parameter generating method which has solved the above-mentioned
problems by generating also the unstored parameters with the aid of a
neural network.
The second object of the present invention is to provide an electronic
musical instrument which is provided with a musical tone generating device
capable of generating easily the musical tone as imaged with the aid of
the neural network which infers and outputs the wave form specifying
parameters based on several image parameters expressing the features of
tone color.
The third object of this invention is to provide an electronic musical tone
generating device in which the above-mentioned problems have been solved
by inferring and setting the wave form with the aid of the neural network.
The musical tone parameter generating method of this invention features
that the neural network learns the input pattern and the musical tone
parameter of an expected output which correspond to each other. Because
such a neural network is provided, any value other than that of the
musical tone parameter stored previously in the memory can be got freely,
which enhances expression ability and saving of memory.
The above-mentioned neural network learns several combinations of the input
patterns and the musical tone parameter patterns according to an algorithm
such as back propagation. Consequently, when the learnt input pattern is
inputted, the musical tone parameter pattern corresponding thereto is
outputted. Even when any pattern not learnt is inputted, a new musical
tone parameter pattern is outputted as a result of associative
compensation with the aid of synapse joint of the neural network, which
affords possibility of new musical tone expression and enables to save the
time for pre-setting the musical tone parameter and the memory to store
many musical tone parameters.
The musical tone generating device of this invention functions as follows.
Several operators are generated based on several wave form specifying
parameters. A musical tone is generated by synthesizing such operators
according to a specific algorithm. The wave form specifying parameter for
generating the musical tone and the synthesis algorithm are inferred and
outputted by the neural network.
Entry for the inference is an image parameter. Applicable image parameters
are, for example, tone hardness/softness, showiness/quietness of tone,
beauty/dirtiness of tone, thickness/thinness of tone, etc. The neural
network learns previously so that the wave form specifying parameter which
causing ordinary player's imaging musical tone is outputted. Consequently,
proper image parameters input allows of suiting generation musical tone to
that image parameters.
Moreover, the electronic musical instrument of this invention features that
by inputting the combination (ON/OFF pattern) of play keys into the neural
network the data which specifies the wave form of the musical tone is
outputted. As the data, the tone color can be inferred and outputted
simultaneously in addition to the tone pitch. If this neural network
learns, for example, so as to associate tone color change according to the
.key ON/OFF pattern of a natural musical instrument, it is possible to
infer all tone color patterns of the whole tone range with the aid of one
set of synapse weights data. In case of a harmonics synthesis type tone
source circuit which is designed so that the musical tone wave form is
generated by additively synthesizing a sine wave, the result of Fourrie
analysis can be used directly as neural network learning data, if the
neural network can output the ratio of harmonics component, thereby
facilitating remarkably the embodiment. It is allowed to set also the
pitch simultaneously in the neural network. It is also allowed to set the
pitch with another means (table, etc.) and to change the frequency of tone
color wave which is inferred by the neural network.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of control section which is an embodiment of this
invention.
FIG. 2(A) shows a partial configuration of the ROM of the control section.
FIG. 2(B) shows a partial configuration of the RAM of the control section.
FIG. 3 shows a configuration of a neural network which is arranged in this
control section.
FIGS. 4 (A) to (F) are flow charts showing the operation of the control
section.
FIG. 5 is a block diagram of control section of an electronic musical
element which is another embodiment of the invention.
FIG. 6 shows a configuration of a neural network which is used in the
control section.
FIG. 7 is a block diagram of control section of an electronic musical
instrument which is also an embodiment of the invention.
FIG. 8 shows an approximate configuration of a playing unit, a neural
network and a tone source circuit of the electronic musical instrument.
DESCRIPTION OF THE PREFERRED EMBODIMENT
FIG. 1 is a block diagram of an electronic musical instrument employing the
musical tone parameter generating method which is an embodiment of this
invention. This electronic musical instrument is an electronic keyboard
type musical instrument having a playing keyboard 16. A tone generator 14
generates the musical tone whose pitch is specified by the playing
keyboard 16. A wave form of the musical tone to be generated is generated
by a neural network. Namely, in this electronic keyboard type musical
instrument the musical tone wave form (plotted amplitude level train) is
generated by the neural network. For software configuration of the neural
network, a CPU 10 is used. Inter-neuron synapse weights are stored in a
memory. The CPU 10 which is designated to perform neural network operation
and control, a ROM 12 which stores control program, and a RAM 13 which
stores synapse weights are connected through a bus 11, through which data
is transmitted and received. The tone generator 14, a function switch
group 15, a keyboard 16, a display 17, and a wave form inputting device 18
are connected to the bus 11. The tone generator 14 has several tone
generating channels which can operate individually. It generates musical
tone whose pitch is specified by the keyboard 16. A sound system 19 is
connected to this tone generator 14. The musical tone generated by the
tone generator 14 is amplified and outputted from a speaker. The function
switch group 15 has a wave form number inputting means, a vector
specifying means, a preset mode switch, a learning mode switch, and a
registration mode switch. The keyboard 16 has 61 (5 octaves) keys. The
display 17 consists of a liquid crystal matrix indicator which displays
specified vector value and wave form. The wave form inputting device 18 is
a sampling device which converts the musical tone wave form inputted from
a microphone into PCM (Pulse Coded Modulation) which is stored in a
memory.
FIG. 2 (A) shows a partial configuration of the ROM 12. M1 is a preset wave
form memory area, and M2 is preset synapse weights memory area. The preset
wave form and the preset synapse weights are stored in advance in these
memory areas. When the player enters a wave form number in the preset
mode, a pertinent wave form is read from the preset wave form memory area,
and sent to the tone generator 14. When the player enters a vector value
in the preset mode, the CPU 10 performs neural network operation to
determine the output pattern based on the preset synapse weights and
outputs data.
FIG. 2 (B) shows a partial configuration of the RAM 13. M3 is a user-set
wave form memory area, and M4 is a user-set synapse weights memory area.
The wave form data and the synapse weights to be stored in these areas are
written by the user of the electronic musical instrument.
The following flags and registers are set in the RAM 13.
PRI: Preset Mode Flag: Flag to be set in the preset mode.
ST: Learning Mode Flag: Flag to be set in the learning mode.
REG: Registration Mode Register: Flag which is set when a sampling wave
form is inputted from the wave form inputting device 18 and is reset when
this wave form is stored in the specified user-set wave form memory area.
BUF: Wave form Buffer: Buffer which stores temporarily the wave form which
is sampled by the wave form inputting device 18.
VEC: Input Vector Register: Register which stores temporarily the vector
value inputted from the vector specifying means.
WEV: Wave form Number Register: Register which stores temporarily the wave
form number inputted from the wave form number inputting means.
FIG. 3 shows a concept of the neural network. This neural network is a
hierarchy neural network. It comprises an input layer, an intermediate
layer and an output layer, each of which consists of several neurons. The
input layer consists of 5 neurons, I1 to I5. It can accept five dimensions
vector (input pattern), each term of the vector may be any real number.
Tone color image data can be used as the vector. Each neuron of the input
layer is synapse-jointed to all neurons of the intermediate layer. The
joint strength is determined by synapse weights w. The intermediate layer
consists of m neurons N1 to Nm, and each neuron is synapse-jointed to all
neurons of the output layer. The output layer consists of n neurons O1 to
On, and each neuron corresponds to the amplitude of each timing of the
musical tone wave form. Namely, the musical tone wave form can be formed
by plotting the output value of the specific each neuron O1 to On in time
series.
FIGS. 4 (A) to (F) are flow charts showing the operation of the control
section. FIG. 4 (A) shows a main routine. In this main routine, at first
initializing such as register reset, etc. is performed after power
turning-on (n1), so that the electronic musical element is made ready to
play. Then, the function switch and the keyboard operations are detected
to execute the corresponding processing (n2, n3).
FIG. 4 (B) shows the preset mode switch ON event operation. When the preset
mode switch is turned on, the preset mode flag PRI is inverted (n4). If
PRI is set as a result of this inversion, the preset mode lamp is lit
since the current mode is the preset mode to specify the wave form with
the aid of data such as preset (stored in The ROM12) synapse weights, etc.
(n6). In the case when PRI is reset, the preset mode lamp is turned off
(n7), since the current mode is the user-set mode where the user uses the
learnt data.
FIG. 4 (C) shows the learning mode switch ON event operation. This learning
mode switch is operated when the relation between the vector and the
output wave form is taught to the neural network. When the learning mode
switch is turned on, the learning mode flag ST is inverted (n8). If ST is
set as a result of this inversion, the learning mode is started.
Therefore, the vector register VEC and the wave form number register WAV
are cleared (n10), the learning mode lamp is lit (n11), and then the
process returns. If ST is reset as a result of inversion, teaching to the
neural network is executed by making the vector value stored in VEC to
correspond to the wave form data of wave form number stored in WAV (n12).
Accordingly, the vector value is an input pattern and the wave form data
is an expected output pattern corresponding to this input pattern. These
data, the vector value and the wave form data, are set in the registers
VEC and WAV as described later in FIG. 4 (E) and FIG. 4 (F). The wave form
data includes, for example, an attack part, a sustain part, and a decay
part of the wave form of a piano tone. In the learning mode, the neural
network is learned as follows.
First, a vector value and a wave form data corresponding to the attack part
are inputted and set into the above registers VEC and WAV. Therefore the
learning process is performed.
Second, a vector value and a wave form data corresponding to the sustain
part are inputted and set, therefore the learning process is performed.
Third, a vector value and a wave form data corresponding to the decay part
are inputted and set, therefore the learning process is performed.
According to the such learning process, a smooth varying piano tone is
simulated by gradual varying of the vector value.
The above learning process is executed according to the back propagation
system. After that the learning model lamp is turned off (n13), and the
process returns.
FIG. 4 (D) shows registration switch ON operation. This is an operation to
sample the musical tone wave form from the wave form inputting device 18.
When the registration switch is turned on, this operation is started. It
is repeatedly executed while the switch is kept turned on. At first, at
step n14 the address to specify the area of the wave form buffer is reset.
Buffer data read/write operation is executed according to this address.
Then, the musical tone data (instantaneous value) of specific timing is
fetched from the wave form inputting device 18, and this data is stored in
the buffer (n16). After that, a judgment as to whether or not the
registration switch is ON is executed (n17). If this switch is ON, the
address is updated (n18), and the process returns to the step n15. If the
registration switch is OFF, sampling is ended. Namely, the process
proceeds from the step n17 to the step n19, the registration model flag
REG is set, and then the process returns.
FIG. 4 (E) is a flow chart showing the processing when the vector number is
inputted. When the vector number is inputted, this value is stored in the
input vector register VEC (n21), and a judgment as to whether the preset
mode flag PRI and the learning mode flag ST have been set or reset is
executed (n22, n23). If PRI has been set, the process proceeds from step
n22 to step n24 where the wave form data is calculated by the neural
network using the preset synapse weights and this vector value. This wave
form is indicated (n25), and at the same time the wave form data is sent
to the tone generator 14 (n26). If the learning model flag ST has been
set, the vector to be learnt is regarded to have been inputted. This
vector is indicated on the display 17, and the process returns (n30).
If both the PRI and the ST have been reset, the process proceeds to step
n27 where the musical tone wave form data is calculated by the neural
network using the user-set synapse weights. The wave form is indicated on
the display 17 (n28) and sent to the tone generator (n29).
FIG. 4 (F) is a flow chart showing the operation which is executed when the
wave form selection witch is set to ON. When the wave form selection
switch is set to ON, ON wave form number is stored in the wave form number
register (n31), and a judgment as to whether the registration mode flag
REG and the preset model flag PRI have been set or reset is performed
(n32, n33). If the REG has been set, the wave form data stored currently
in the wave form buffer BUF is registered in the user-set wave form data
memory area (n34). The registration area is an area identified by wave
form number (WEV). After the REG is reset (n35), and the process returns.
If the PRI has been set, the wave form data stored in the area which is
identified by the wave form number (WEV) in the preset wave form data
memory area is sent to the tone generator 14 (n36). The musical tone is
generated by this wave form data.
If both the REG and the PRI have been reset, data of the WEV is indicated
on the display (n37), and the process returns. This operation is performed
when the wave form is selected in the learning mode.
Thus, this electronic musical instrument features that since the input
pattern and the expected output are learnt to the neural network in
specific conformance, any value of the musical tone parameter other than
that stored previously in memory can be got freely, delicate change of the
musical tone parameter can be obtained thereby enhancing the expression.
Moreover, since there is no need to store many parameters in memory,
memory can be saved.
The above-mentioned electronic musical instrument has been designed so that
the musical tone parameter pattern is generated through the neural network
for the wave form data. The same processing can be performed also for
other musical tone parameters.
FIG. 5 is a block diagram showing the control section of an electronic
musical instrument which is an another embodiment of the invention. This
electronic musical instrument is controlled by a CPU 20 and generates a
musical tone according to operation of a player. The CPU 20 is connected
to specific circuit through a bus 21. The provided circuits comprises a
ROM 22, a RAM 23, a neural network (NN) 24, a keyboard 25, an operation
panel 26, and a FM sound tone circuit 27. A sound system 28 designed to
amplify the generated musical tone and output it through a speaker, etc.
is connected to an FM tone source circuit 27.
The ROM 22 stores a program and preset synapse weights. The RAM 23 has
registers to store various data which are created during playing and
stores the synapse weights which is obtained as a result of learning by
user. The neural network 24 has a function to decide the wave form
specifying parameter int eh FM tone source based on an inputted image
parameter. This neural network 24 is a hierarchy neural network as shown
in FIG. 6. The image parameter is inputted into an input layer from tone
color image specifying dials 26a to 26d. The wave form specifying
parameter as shown in FIG. 6 is outputted to an output layer according to
inference based on this parameter. Any hardware configuration is
applicable for the neural network provided that the hierarchy inference as
shown in the figure is possible. The keyboard 25 is for playing and covers
tone range of about 5 octaves. The operation panel 26 has a learning
mode/normal mode selection switch, a synapse weights selection switch in
addition to the above-mentioned tone color image specifying dials 26a to
26d. The learning mode is a mode in which an image for the currently set
wave form specifying parameter is inputted with the aid of the tone color
image specifying dials so that the tone color is learnt. The normal model
is an ordinary play mode. The synapse weights selection switch is a
selection switch to specify use of the synapse weights which are
previously stored in the ROM 22 or use of synapse weights which are learnt
in the above-mentioned learning mode and stored in the RAM 23.
The FM tone source circuit 27 is a circuit which specifies generation
contents of 4 or 6 operators by settings several parameters, synthesize
the musical tone according to the specified algorithm which designates the
combination of operators and modification procedure. By properly adjusting
the parameters and algorithm, complex changed tone colors and high-order
harmonic overtones can be obtained. The parameters and the algorithm are
set by the CPU 20 before playing. The musical tone of specific pitch is
generated based on a key-ON signal and a key code sent from the CPU 20
during playing.
FIG. 6 shows an outline of the tone color image specifying dials 26a to
26d, as well as an outline of the neural network 24. The tone color image
specifying dials 26a to 26d can set the extent of 4 types of tone color
image. The dial 26a specifies hardness of tone (hard/soft). The dial 26b
specifies tone thickness (thick/thin). The dial 26c specifies beauty of
tone (beauty/dirt). The dial 26d specifies showiness of tone
(showy/quiet). A value which is specified by the tone color image
specifying dials 26a to 26d is inputted into the neural network 24 as
image parameter. These 4 image parameters are inputted into the input
layer of the neural network 24. Each neuron of the input layer and a joint
layer is synapse-jointed with specific weighing, and each neuron of the
joint layer and the output layer is also synapse-jointed with specific
weighting. An output of specific neuron of the output layer corresponds to
the wave form specifying parameter of specific operator and algorithm. For
simpler explanation, FIG. 6 shows an operator ON/OFF, a frequency ration
(pitch of musical tone to be generated with respect to frequency) and an
envelope rate as the wave form specifying parameters of each operator. The
real operator is specified by more parameters including musical effect
parameter, such as a vibrato rate or a portamento. This output is sent to
the FM tone source circuit 27 through the CPU 20, and the FM tone source
circuit 27 generates the musical tone according to this parameter.
This neural network 24 learns previously several teach data. Proper output
parameter can be set according to this learning irrespective of what
parameter is inputted. Statistic data of unspecified many players are used
as the teach data. The neural network is learnt so that it outputs the
wave form specifying parameter which makes the FM tone source circuit 27
generate the musical tone suited to specified image by the tone color
image specifying dial 26. This simplifies greatly tone generation.
Several sets of learnt the synapse weights to be stored in the RAM 23
applied in the above electronic musical instrument are applicable. Data of
the musical tone to be learnt is allowed to be not the wave form
specifying parameter which has been set in the electronic musical
instrument but be data inputted from other equipment.
The musical tone generating device of this invention can generate the
musical tone, using the neural network, so that the musical tone suited to
image can be generated easily without operating complicated parameters.
This simplifies, for example, a tone color edit of the FM tone source
circuit.
The third embodiment of the invention is explained below by referring to
FIG. 7 and FIG. 8.
FIG. 7 is a block diagram which shows a control section of the electronic
musical instrument which is the 3rd embodiment of the invention. This
electronic musical instrument is provided with a wind instrument type
playing device (wind controller) 35 (see FIG. 8). It generates musical
tone when a player blows. The whole operation is controlled by a CPU 30.
The CPU 30 is connected to a ROM 32, a RAM 33, a neural network (NN) 34,
an interface 39, an operation panel 36 and a harmonics additive type tone
source circuit 37 through a bus 31. The above-mentioned wind controller 35
is connected to the interface 39. A sound system 38 to amplify a generated
musical tone and to output it from a speaker is connected to a tone source
circuit 37.
An operation control program and synapse weights corresponding to a musical
instrument name chosen by the player are stored in the ROM 32. When the
player chooses a musical instrument name, synapse weights corresponding
thereto are read from this ROM 32 and set in the neural network 34.
Several registers to store various data generated during playing are
provided in the RAM 33. The neural network 34 executes an inference so as
to decide a musical tone which must be generated according to the ON/OFF
pattern of a key system 41 (see FIG. 8) of the wind controller 35. This
neural network 34, as outlined in FIG. 8, is an hierarchy neural network.
The ON/OFF signal of each key is inputted into each neuron of the input
layer, and a frequency control signal of the harmonics to be synthesized
and its amplitude control signal are outputted from the output layer. It
is possible to use the neural network of any hardware configuration
provided that the hierarchy inference as shown in the figure is feasible.
Moreover, the Neumann type microprocessor is applicable if high speed
inference processing is possible.
The wind controller 35, as shown in FIG. 8, is a wind musical instrument
(recorder) type playing device. It controls tone generation/silencing and
tone generation level according to an intensity of breath blown into a
mouthpiece 40. The key system 41 is controlled by fingers of both hands of
the player. The pitch of musical tone to be generated is decided by ON/OFF
pattern of the key system 41. The operation panel 36 is provided with a
tone color selection witch and a display. Tone source circuit 37 which is
harmonics additive type is a tone source circuit which generates musical
tone by adding sine waves of different frequencies to synthesize
(generate) the musical tone as shown in FIG. 8 (right). Frequency and
amplitude of the sine waves to be synthesized are inferred by the neural
network 34.
FIG. 8 shows the approximate configuration of the wind controller 35, the
neural network 34 and the tone source circuit 37 of the electronic musical
instrument. The wind controller 35 has a shape similar to that of the wind
instrument as shown in the figure. The player blows in breath from the
mouthpiece 40, and controls the key system 41 with his fingers of both
hands to play the instrument. Each key composing the key system 41 is an
electronic switch. The ON/OFF signal caused by operation is given to the
input layer 42 of the neural network 34 as an electric signal. The neural
network 34 is a hierarchy neural network having 4 layers, namely an input
layer 42, a 1st intermediate layer 42, a 2nd intermediate layer 44, and an
output layer 45. The input layer 42 has the same number of neurons as the
key system 41 and is connected to the 1st intermediate layer 44, with
specific synapse weights. The 1st intermediate layer 44 and the 2nd
intermediate layer 45 are also mutually connected with specific synapse
weights, and the 2nd intermediate layer 44 and the output layer 45 are
also mutually connected with specific synapse weights. The number of
neurons of the output layer 45 is equal to the number of sine wave
generating circuits 46 of the tone source circuit 37, or the number of
distributors 47 of the tone source circuit 37. Each neuron of the output
layer 45 outputs the frequency control signal of the sine wave to be
generated to the sine wave generating circuit 46 and at the same time
outputs a distribution rate (amplitude) control signal of the inputted
sine wave to the distributor 47. The tone source circuit 37 comprises the
above-mentioned sine wave generating circuit 46, the distributor 47, an
adding circuit 48, and a ED/A converter 49. The sine wave generated by the
sine wave generating circuit is restricted to the specified amplitude
value by the distributor 47, and the restricted signal is inputted into
the adding circuit 48. In the adding circuit 48 all the inputted sine
waves are added to synthesize, and the obtained signal is inputted into
the D/A converter 49. In the D/A converter 49 the inputted synthesis
signal is shaped to give smooth envelop, and then the shaped signal is
outputted. The outputted signal is the musical tone signal which is
amplified by the sound system 38 and outputted herefrom.
Because the harmonics synthesis type tone source circuit is controlled by
the neural network to generate the musical tone, it is possible to use the
result of analysis by FFT (Fast Fourier Transformer) as teach pattern of
the neural network. That is, musical tone of specific pitch of the musical
instrument to be learnt is FFT-analyzed, and the result of FFT, to which
the ON/Off pattern to generate the FFT-analyzed musical tone corresponds,
is given to the neural network as the teach pattern. As above learning is
performed for the whole tone range, it becomes possible to infer properly
the musical tone of the whole tone range with one set of synapse weights.
The applicable tone source circuit is not restricted to the harmonics
synthesis tone source circuit. FM tone source is also applicable. In this
case the neural network outputs FM parameters specifying the music tone
such as key level scaling parameter which enables operators of the FM tone
source to vary generating sine wave according to a turned on key data
(pitch data). And in this case the learning mode is performed by using
pitch data and the key level scaling parameter.
It is allowed to include a blow intensity detected at the mouthpiece 40 in
the input variables of the neural network 34. This makes it possible to
infer simultaneously a change of tone color depending on the tone
generation level.
Thus, this electronic musical instrument makes it possible to infer not
only the pitch of the musical tone but also the tone color based on the
ON/OFF pattern of several play keys. This enables the player to variegate
the musical tone depending on the pitch similarly to the natural musical
instrument, which enhances the expression of the electronic musical
instrument.
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