Abstract: An audio analysis device comprises an electronic controller including at least one processor. The electronic controller is configured to execute a plurality of modules including a signal acquisition module configured to acquire an audio signal representing performance sounds of a musical piece, a signal analysis module configured to calculate, for each of a plurality of music categories, a feature value that includes a degree of certainty that the musical piece belongs to the music category, by analyzing the audio signal, and a music selection module configured to select one or more candidate musical pieces whose feature value is similar to the feature value calculated for the musical piece from among a plurality of candidate musical pieces.

Abstract: An audio analysis system includes at least one memory configured to store instructions; and at least one processor configured to execute the instructions to: receive an instruction indicative of a target timbre; acquire a first audio signal containing a plurality of audio components corresponding to different timbres; and select at least one reference signal from among a plurality of reference signals respectively representative of different pieces of audio based on the target timbre and the first audio signal, in which: the at least one reference signal has an intensity with a temporal change, the temporal change in the intensity of the at least one reference signal is represented by a reference rhythm pattern, the plurality of audio components include audio components corresponding to the target timbre, the audio components corresponding to the target timbre have an intensity with a temporal change, the temporal change in the intensity of the audio components corresponding to the target timbre is represente

Abstract: An information processing system includes at least one memory configured to store instructions and at least one processor configured to implement the instructions to acquire first audio data indicative of audio of a target piece of music, and cause a trained model to output first timbre data indicative of a timbre appropriate for the target piece of music by inputting input data into the trained model, the input data including the first audio data, in which the trained model is trained to learn a relationship between second audio data indicative of audio and second timbre data indicative of a timbre for each reference piece of a plurality of reference pieces of music.

Abstract: A musical element generation support device includes at least one processor configured to receive a musical element sequence including a plurality of musical elements and a blank portion that are arranged in a time series, and generate, by using a learning model, at least one suitable musical element for the blank portion based on a part of the musical elements that is positioned after the blank portion on a time axis in the musical element sequence. The learning model is configured to generate, from one-part musical element, another-part musical element.

Abstract: An audio analysis device comprises an electronic controller including at least one processor. The electronic controller is configured to execute a plurality of modules including a signal acquisition module configured to acquire an audio signal representing performance sounds of a musical piece, a signal analysis module configured to calculate, for each of a plurality of music categories, a feature value that includes a degree of certainty that the musical piece belongs to the music category, by analyzing the audio signal, and a music selection module configured to select one or more candidate musical pieces whose feature value is similar to the feature value calculated for the musical piece from among a plurality of candidate musical pieces.

Abstract: An update method includes acquiring a list of plural pieces of content selected from a library including plural pieces of content, selecting, from the list, first content to be deleted from the list, selecting, from the library, second content to be interchanged with the first content based on details of the list, and interchanging the first content with the second content in the list.

Abstract: An update method includes acquiring a list of plural pieces of content selected from a library including plural pieces of content, selecting, from the list, first content to be deleted from the list, selecting, from the library, second content to be interchanged with the first content based on details of the list, and interchanging the first content with the second content in the list.

Abstract: A sound signal processing method according to an embodiment includes a step of acquiring an input sound signal, a step of acquiring a beat number per unit time period from the input sound signal, a step of normalizing the input sound signal with the beat number per unit time period, a step of calculating a beat spectrum of the normalized input sound signal, and a step of calculating a rhythm similarity between the beat spectrum of the normalized input sound signal and a normalized beat spectrum calculated from a reference sound signal.

Abstract: A sound signal processing method according to an embodiment includes a step of acquiring an input sound signal, a step of acquiring a beat number per unit time period from the input sound signal, a step of normalizing the input sound signal with the beat number per unit time period, a step of calculating a beat spectrum of the normalized input sound signal, and a step of calculating a rhythm similarity between the beat spectrum of the normalized input sound signal and a normalized beat spectrum calculated from a reference sound signal.

Abstract: When a music audio to be analyzed is to be divided into a plurality of possible pattern segments based on estimated beat positions, it is divided in a plurality of different ways according to individual division models obtained by sequentially shifting the leading or first beat position of the possible pattern segments. Such division can provide plural sets of possible pattern segments with each of the sets corresponding to one of the division models. For each of the sets, comparison is made between individual possible pattern segments and individual reference performance patterns. For each of the possible pattern segments of the set, at least one reference performance pattern that matches the possible pattern segment is determined. Then, at least one combination of reference performance patterns is identified for each of the sets of possible pattern segments. One optimal combination is selected from among the identified combinations.

Abstract: A musical-performance analysis device includes: an acquisition section that acquires performance information of a player; a determination section that determines, by comparing the performance information acquired by the acquisition section with reference information indicating a reference of a performance, among performance segments different from one another, a performance segment in which a difference degree between the performance information acquired by the acquisition section and the reference information is large and a performance segment in which the difference degree between the performance information acquired by the acquisition section and the reference information is small; and the specification section that specifies a tendency of the performance on the basis of the difference degree of the performance segment in which the difference degree has been determined to be small by the determination section.

Abstract: The method analyzes, on the basis of the musical score image data, an image note sequence that is a time series of a plurality of notes included in the image of a musical score of a given music piece, acquires, on the basis of a logical musical score data, a logical note sequence that is a time series of a plurality of notes included in the musical score of the given music piece, and then identifies correspondence relationship between the individual notes in the image note sequence and the individual notes in the logical note sequence by comparing the image note sequence and the logical note sequence. Further, the method displays the musical score image of the given music piece on the basis of the musical score image data, and controls a display state of the displayed image of the musical score by use of the identified correspondence relationship.

Abstract: The method analyzes, on the basis of the musical score image data, an image note sequence that is a time series of a plurality of notes included in the image of a musical score of a given music piece, acquires, on the basis of a logical musical score data, a logical note sequence that is a time series of a plurality of notes included in the musical score of the given music piece, and then identifies correspondence relationship between the individual notes in the image note sequence and the individual notes in the logical note sequence by comparing the image note sequence and the logical note sequence. Further, the method displays the musical score image of the given music piece on the basis of the musical score image data, and controls a display state of the displayed image of the musical score by use of the identified correspondence relationship.

Abstract: When a music audio to be analyzed is to be divided into a plurality of possible pattern segments based on estimated beat positions, it is divided in a plurality of different ways according to individual division models obtained by sequentially shifting the leading or first beat position of the possible pattern segments. Such division can provide plural sets of possible pattern segments with each of the sets corresponding to one of the division models. For each of the sets, comparison is made between individual possible pattern segments and individual reference performance patterns. For each of the possible pattern segments of the set, at least one reference performance pattern that matches the possible pattern segment is determined. Then, at least one combination of reference performance patterns is identified for each of the sets of possible pattern segments. One optimal combination is selected from among the identified combinations.