Abstract: 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.

Abstract: A sound editing device includes at least one processor that is configured to execute a first receiving unit configured to receive a first audio signal, a second receiving unit configured to receive a second audio signal, and an estimation unit configured to estimate effect information that reflects an effect to be applied to the first audio signal, from the first and second audio signals, by using a trained model indicating an input-output relationship between first and second input audio signals and output effect information that reflects an effect to be applied to the first input audio signal.

Abstract: An information processing device includes at least one processor configured to execute a plurality of modules including an input module into which natural language that includes an adjective is configured to be input by a user, and a timbre estimation module configured to output timbre data based on the natural language input by the user, by using a trained model configured to output the timbre data from the adjective.

Abstract: A musical piece structure analysis method includes acquiring an acoustic signal of a musical piece, extracting a first feature amount indicating changes in tone from the acoustic signal of the musical piece, extracting a second feature amount indicating changes in chords from the acoustic signal of the musical piece, outputting a first boundary likelihood indicating likelihood of a constituent boundary of the musical piece from the first feature amount using a first learning model, outputting a second boundary likelihood indicating likelihood of the constituent boundary of the musical piece from the second feature amount using a second learning model, identifying the constituent boundary of the musical piece by performing weighted synthesis of the first boundary likelihood and the second boundary likelihood, and dividing the acoustic signal of the musical piece into a plurality of sections at the constituent boundary that has been identified.

Abstract: A musical analysis device comprises an electronic controller including at least one processor. The electronic controller is configured to execute a plurality of modules including a detection module configured to detect, using a detection model through a model training process with machine learning, bar lines based on audio information indicating a musical piece, and a correction module configured to correct positions of the bar lines detected by the detection module based on a reference meter identified from the musical piece.

Abstract: A chord identification method selects from among a plurality of chord identifiers a chord identifier that corresponds to an attribute of a piece of music represented by an audio signal, where the plurality of chord identifiers corresponds to respective ones of a plurality of attributes relating to pieces of music; and identifies a chord for the audio signal by applying a feature amount of the audio signal to the selected chord identifier.

Abstract: 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.

Abstract: 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.

Abstract: A chord estimation apparatus estimates a first chord from an audio signal, and estimates a second chord by inputting the estimated first chord to a trained model that has learned a chord modification tendency.

Abstract: A chord identification method selects from among a plurality of chord identifiers a chord identifier that corresponds to an attribute of a piece of music represented by an audio signal, where the plurality of chord identifiers corresponds to respective ones of a plurality of attributes relating to pieces of music; and identifies a chord for the audio signal by applying a feature amount of the audio signal to the selected chord identifier.

Abstract: A chord estimation apparatus estimates a first chord from an audio signal, and estimates a second chord by inputting the estimated first chord to a trained model that has learned a chord modification tendency.

Abstract: A music analysis apparatus calculates a similarity index based on an edit distance between a designated sequence of notes designated by a user and a reference sequence of notes of a reference piece of music. The edit distance is calculated by setting a substitution cost between a first note in the designated sequence of notes and a second note in the reference sequence of notes to a first value when one of the first note and the second note corresponds to any of a plurality of notes contained in a tolerance interval containing the other of the first note and the second note, and by setting the substitution cost to a second value different from the first value when one of the first note and the second note does not correspond to any of the plurality of notes contained in the tolerance interval.

Abstract: A note sequence analysis apparatus calculates a similarity index based on similarity between a designated sequence of notes designated by a user and a reference sequence of notes for each of a plurality of reference pieces of music, then selects a reference piece of music from among the plurality of reference pieces of music based on the similarity index calculated for each of the plurality of reference pieces of music, and specifies an evaluation index of the designated sequence of notes based on the similarity index calculated for the selected reference piece of music.

Abstract: A music analysis apparatus has a feature extractor and an analysis processor. The feature extractor generates a time series of feature values from a sequence of notes which is designated as an object of analysis. The analysis processor computes an evaluation index value which indicates a probability that the designated sequence of notes is present in each of a plurality of reference music pieces by applying a probabilistic model to the time series of the feature values generated from the designated sequence of notes. The probabilistic model is generated by machine learning of the plurality of reference music pieces using time series of feature values obtained from the reference music pieces.

Abstract: A server apparatus includes a processor, configured to acquire object specifying information for specifying an object in a reality space captured by a first portable terminal, to acquire condition information including an external condition of the first portable terminal, and to acquire content information associated with the object specifying information and the condition information by referring to a database. The database stores, in association with one another, content information representing a content displayed by being superimposed on an object in a reality space captured by a second portable terminal, object information for identifying the object captured by the second portable terminal, and condition information representing a condition for displaying, in the first portable terminal, the content represented by the content information. The processor of the server apparatus is also configured to transmit the content information to the first portable terminal.

Abstract: A note sequence analysis apparatus calculates a similarity index based on similarity between a designated sequence of notes designated by a user and a reference sequence of notes for each of a plurality of reference pieces of music, then selects a reference piece of music from among the plurality of reference pieces of music based on the similarity index calculated for each of the plurality of reference pieces of music, and specifies an evaluation index of the designated sequence of notes based on the similarity index calculated for the selected reference piece of music.

Abstract: A music analysis apparatus calculates a similarity index based on an edit distance between a designated sequence of notes designated by a user and a reference sequence of notes of a reference piece of music. The edit distance is calculated by setting a substitution cost between a first note in the designated sequence of notes and a second note in the reference sequence of notes to a first value when one of the first note and the second note corresponds to any of a plurality of notes contained in a tolerance interval containing the other of the first note and the second note, and by setting the substitution cost to a second value different from the first value when one of the first note and the second note does not correspond to any of the plurality of notes contained in the tolerance interval.

Abstract: A music analysis apparatus has a feature extractor and an analysis processor. The feature extractor generates a time series of feature values from a sequence of notes which is designated as an object of analysis. The analysis processor computes an evaluation index value which indicates a probability that the designated sequence of notes is present in each of a plurality of reference music pieces by applying a probabilistic model to the time series of the feature values generated from the designated sequence of notes. The probabilistic model is generated by machine learning of the plurality of reference music pieces using time series of feature values obtained from the reference music pieces.