A chord information extraction device includes an acquirer, a score type determiner, an extraction region determiner and a chord information extractor. The acquirer acquires score image data representing a reference score. The score type determiner determines a score type of the reference score from among a plurality of predetermined score types based on the acquired score image data. The extraction region determiner determines a chord extraction region in the reference score based on extraction region information that defines a relationship between a plurality of score types and a chord extraction region from which chord information is to be extracted. The chord information extractor extracts the chord information from the acquired score image data based on the determined chord extraction region.

Methods, systems and computer program products are provided for testing a lead sheet for plagiarism. A test lead sheet receiving having a plurality of passages is received at receiving a plagiarism detector. A set of annotations describing a level of plagiarism of a plurality of elements (e.g., chord sequence, subsequences, melodic fragments (i.e., notes), rhythm, harmony, etc.) of the test lead sheet in relation to the preexisting lead sheets are generated and output via an output device.

A method implemented by a determination engine is provided. The determination engine receives a media dataset comprising target piece music information, target piece audience information, corpus music information, corpus audience information, and corpus preference data. The determination engine determines a subset of the corpus music and preference information and determines at least one surprise factor of the subset of the corpus music and preference information across features at one of a plurality of orders. The determination engine learns a model that estimates a likelihood that time-varying surprise trends across the features achieves a preference level. The determination engine determines at least one surprise factor of the target piece music information across the features at the one of the plurality of orders and predicts, using the model, preference information using the time-varying surprise trends for the target piece music information across the features.

Described herein are techniques for adjusting notes of a first musical piece based on chord data of a second musical piece. A first musical piece is accessed, wherein the first musical piece comprises a plurality of notes. Chord data associated with a second musical piece is accessed. One or more of the plurality of notes are compared to the chord data. An aspect of the one or more of the plurality of notes is changed based on the comparison.

An accompaniment sound generating device includes a specifier, an accompaniment sound generator, and an accompaniment sound outputter. The specifier specifies a plurality of musical performance parts for which accompaniment sounds are generated based on an input musical performance sound. The accompaniment sound generator generates the accompaniment sounds that belong to the plurality of specified musical performance parts for each musical performance sound. The accompaniment sound outputter outputs the accompaniment sounds generated for the plurality of musical performance parts with timing for generating the accompaniment sounds aligned with timing for generating musical performance sounds.

An apparatus for analyzing audio includes a feature acquirer, an output apparatus, at least one memory, and at least one processor. The at least one processor is configured to execute a program stored in the at least one memory. The at least one processor is also configured to cause the feature acquirer to acquire a series of feature amounts of an audio signal. The at least one processor is also configured to generate boundary data by inputting the acquired series of feature amounts into a boundary estimation model that has learned relationships between: (i) a series of feature amounts, and (ii) boundary data representative of boundaries, each of the boundaries being between consecutive periods in each of which a chord is continuous.

An audio analysis method is realized by a computer and includes generating key information which represents a key, by inputting a time series of a feature amount of an audio signal into a learned model that has learned a relationship between keys and time series of feature amounts of audio signals.

A method and apparatus for music generation may include steps of receiving any length of input; recognizing pitches and rhythm of the input; generating a first segment of a full music; generating segments other than the first segment to complete the full music; generating connecting notes, chords and beats of the segments of the full music and handling anacrusis; and generating instrument accompaniment for the full music, and comprise a music generating system to realize the steps of music generation.

A system and method for estimating the popularity of song by calculating the (absolute and/or contrastive) harmonic surprise of each song in a corpus of music data, determining the popularity of each song in the corpus (e.g., based on a music chart, downloads, online streams), determining correlations between harmonic surprise and popularity, and estimating the popularity of an individual song based on the (absolute and/or contrastive) harmonic surprise of the individual song and the correlations between harmonic surprise and popularity.

In an electronic musical instrument that can output stored lyrics of a song in accordance with keyboard operations by a user, a processor determines whether a melody should be advanced or not while multiple keys of a keyboard are pressed by the user using prescribed criteria, if the processor determines that the melody should be advanced, the processor advances the lyric in response to the user's multiple key operation and if the processor determines that the melody should not be advanced, the processor does not advance the lyric in response to the user's multiple key operation.