A parameter inference method realized by a computer, includes obtaining target performance information indicating a performance of music using an electronic musical instrument; inferring assist information from the target performance information with use of a trained inference model generated through machine learning, the assist information being related to setting of a parameter of the electronic musical instrument that conforms to a tendency of the performance; and outputting the inferred assist information related to the setting of the parameter.

An arrangement generation method executed by a computer includes acquiring target musical piece data that include performance information that indicates a melody and a chord of at least a part of a musical piece and include meta information that indicates characteristics of at least the part of the musical piece, generating, from the acquired target musical piece data, by using a generative model trained by machine learning, arrangement data obtained by arranging the performance information in accordance with the meta information, and outputting the generated arrangement data.

Provided are systems and methods for transforming a digital score file into one or more of a plurality of levels of simplified visualization outputs. Methods of the present invention may be computer implemented. Systems of the present invention may include at least one display device, a non-transitory memory having instructions embedded thereon, and a processor in communication with the non-transitory memory and the at least one display device. Systems and methods of the present invention may be configured to receive at least one digital score file, upon which one or more simplification rules are executed, resulting in at least one simplified visualization output. Simplification rules may include, but are not limited to, song length, tempo adjustment, tie, rhythm, harmonic rhythm, and chord. One or more simplified visualization outputs are then provided.

In one aspect, provided herein is a device for notating a musical composition. The device, in various implementations, is structured so as to be less laborious to notate, easier to read, and more simple to employ in notating, reading, and/or playing the music of a given composition to be composed and/or played. Accordingly, in its most basic form, the device herein disclosed includes a template, upon which template one or more symbols may be notated, where such notation is configured in a manner that more closely relates the note to be played with the mechanical action needed to be performed so as to play that note, such as on an instrument to be or being played.

A musical piece generation device includes an electronic controller including at least one processor. The electronic controller is configured to execute a plurality of modules including a data acquisition module configured to acquire target musical piece data indicating at least a part of a musical piece, a parameter acquisition module configured to acquire a value of a difficulty level parameter, a generation module configured to, by using a trained generative model, generate, from the target musical piece data and the value of the difficulty level parameter, new musical piece data indicating at least a part of a new musical piece obtained by changing a difficulty level of the musical piece to a difficulty level specified by the difficulty level parameter, and an output module configured to output the new musical piece data that has been generated.

A method for online music learning of playing a musical instrument, that may be a string instrument, a woodwind instrument, a brass instrument, a percussion instrument, or vocal (singing) is described. A client device, such as a smartphone or a tablet notifies a person, such as visually by a display or audibly by a sounder, of a sequence of musical symbols that may be part of a musical piece to be played on the musical instrument. The pace or tempo of the notified sequence is adapted according to a stored skill level value of the person and the pace or tempo associated with the musical piece. The client device, monitors, using a microphone(s) in the client device, the errors in the playing of the sequence, and using a predefined accordingly updates the stored skill level value, and accordingly changes the arrangement, pace and/or tempo of the next sequence of musical symbols.

A system for providing a user a virtual exercise in playing a music instrument relative to the user's skill characteristics includes: a processing entity and a memory entity for processing and storing data, respectively to execute the system functions, and a data transfer entity for receiving and transmitting data, the system configured to: obtain musical notation data, analyze it to assign the musical piece to which such data pertains a number of difficulty characteristics with scalar values, provide the user with a number of musical pieces, with known difficulty characteristics, as virtual exercises to be completed by playing an instrument, obtain user performance data of completed virtual exercises, analyze the user performance data to determine and assign the user with a number of skill characteristics values in accordance with the difficulty characteristic values of the completed musical pieces, and determine a musical piece for the user as a virtual exercise.

A non-transitory computer-readable storage medium stored with an automatic music arrangement program, and an automatic music arrangement device are provided. An outer voice note having a highest pitch among notes of which sound production start times are approximately the same in a melody part acquired from musical piece data is identified. A melody part acquired by deleting inner voice notes of which sound production starts within a sound production period of the outer voice note and of which pitches are low from the melody part is generated. Candidate accompaniment parts in which root notes of chords of chord data of the musical piece data are arranged to be produced at sound production timings thereof for each pitch range acquired by shifting a range of pitches corresponding to one octave by one semitone at each time are generated, and an accompaniment part is selected among them.

A method for online music learning of playing a musical instrument, that may be a string instrument, a woodwind instrument, a brass instrument, a percussion instrument, or vocal (singing) is described. A client device, such as a smartphone or a tablet notifies a person, such as visually by a display or audibly by a sounder, of a sequence of musical symbols that may be part of a musical piece to be played on the musical instrument. The pace or tempo of the notified sequence is adapted according to a stored skill level value of the person and the pace or tempo associated with the musical piece. The client device, monitors, using a microphone(s) in the client device, the errors in the playing of the sequence, and using a predefined accordingly updates the stored skill level value, and accordingly changes the arrangement, pace and/or tempo of the next sequence of musical symbols.

Apparatus and associated methods relate to comparing a musical score model to a captured performance of the musical score, calculating a degree of similarity between the musical score model and the captured performance based on the comparison, and automatically evaluating the captured performance based on the degree of similarity and musical score degree of difficulty determined as a function of the musical score entropy. In an illustrative example, a musician may be learning to play the musical score. The musical score may be modelled, for example, based on pitch, volume, and rhythm, permitting comparison to the captured performance of the musical score. In some examples, the musical score degree of difficulty may be adapted based on the captured performance evaluation. Some embodiments may generate musical scores based on the captured performance evaluation. Various examples may advantageously provide corrective instruction based on the degree of difficulty and the captured performance evaluation.