A recommendation information provision device includes at least one processor that is configured to acquire a result of scoring relating to a user's singing of a musical piece for each section in time; acquire pitch information representing pitches of notes configuring the musical piece in the section; build a learning model predicting a result of scoring relating to the user's singing of a musical piece from the pitch information using the result of scoring and the pitch information as training data; acquire a result of scoring relating to a user's singing of a target musical piece by inputting the pitch information to the learning model while changing pitches of notes into a plurality of types; and output a setting detail of the pitches of the notes as the recommendation information on the basis of results of scoring for the pitch information of the plurality of types.

An electronic musical instrument includes at least one processor that, in accordance with a user operation on an operation unit, obtains lyric data and waveform data corresponding to a first tone color; inputs the obtained lyric data to a trained model so as to cause the trained model to output acoustic feature data in response thereto; generates waveform data corresponding to a singing voice of a singer and corresponding to a second tone color that is different from the first tone color, based on the acoustic feature data outputted from the trained model and the obtained waveform data corresponding to the first tone color; and outputs a singing voice based on the generated waveform data corresponding to the second tone color.

A computer-implemented method includes obtaining a trained model trained to store a relationship between first performance data and first evaluation data. The first performance data indicates a performance performed by a performer. The first evaluation data indicates a first evaluation of the performance. The first evaluation has been made by an audience who has received the performance. The method also includes obtaining second performance data. The method also includes processing the second performance data using the trained model to make an inference of a second evaluation of the second performance data. The method also includes outputting second evaluation data indicating the inference of the second evaluation.

A method for analyzing one or more notes in a musical composition, comprising for each note: getting a note, a chord and a scale. computing note properties using the note's value and the chord and the scale. A method for transforming one or more input notes into one or more new notes, comprising for each input note: getting an input note and its note properties, getting a new chord and a new scale for the input note, getting a list of notes candidates, computing distances between the input note and every note in the list, using input note's value, input note's note properties, candidate note's value and candidate note's note properties, finding the candidate that has the minimal distance, and setting a new note value using a note value of the candidate with the minimal distance.

A tool is needed for music instrument learners to get feedbacks on the correctness of their performances of a particular piece of music. The invention disclosed here is such a tool that can provide music instrument players instant feedback on note pitch and rhythms accuracy based on sheet music. This is accomplished through audio signal processing, sheet music image processing, and conversion of both analogue images and audio signals into standard digital music representation so a comparison can be done and hence a feedback can be presented to the player. An advanced feature will allow users to save the data to the cloud and retrieve later for comparison of progress. It also will allow user to participate an online competition with other players of the same piece of music.

A method of determining a data output timing, performed by at least one processor in an information processing device for an electronic musical instrument includes, via the at least one processor: obtaining data of a first performance operation on the electronic musical instrument by a user; obtaining data of a second performance operation on the electronic musical instrument by the user; and determining the data output timing for outputting a data to the user based on a time interval between the first and second performance operations.

Provided is an information processing device including an acquisition unit that acquires subjective evaluation information from a second user about each performance performed by at least part of a body of a first user moving, a learning unit that performs machine learning on a relationship between the each performance and the corresponding subjective evaluation information and generates relationship information between the each performance and the corresponding subjective evaluation information, and a presentation unit that presents feedback information to the second user based on the relationship information.

An estimation model construction method realized by a computer includes preparing a plurality of training data that include first training data that include first feature amount data that represent a first feature amount of a performance sound of a musical instrument and first onset data that represent a pitch at which an onset exists, and second training data that include second feature amount data that represent a second feature amount of sound generated by a sound source of a type different than the musical instrument, and second onset data that represent that an onset does not exist, and constructing, by machine learning using the plurality of training data, an estimation model that estimates, from a feature amount data that represent a feature amount of a performance sound of the musical instrument, estimated onset data that represent a pitch at which an onset exists.

Disclosed is a learning model generation method executed by a computer, including: striking a percussion instrument with a striking member to emit a musical sound; and conducting machine learning upon receiving an input of the musical sound emitted from the percussion instrument, and generating, based on the machine learning, a learning model for outputting numerical values for setting musical performance parameters for an automatic musical performance of the percussion instrument that is struck when the striking member is driven.

Disclosed herein are computer-implemented method, system, and computer-readable storage-medium embodiments for implementing template-based excerpting and rendering of multimedia performances technologies. An embodiment includes at least one computer processor configured to retrieve a first content instance and corresponding first metadata. The first content instance may include a first plurality of structural elements, with at least one structural element corresponding to at least part of the first metadata. The first content instance may be transformed by a rendering engine running on the at least one computer processor and/or transmitted to a content-playback device.