An electronic device having a circuitry configured to perform audio source separation on an audio input signal to obtain a separated source and configured to perform audio dubbing on the separated source based on replacement conditions to obtain a personalized separated source.

An electronic device having a circuitry configured to perform audio source separation on an audio input signal to obtain a vocals signal and an accompaniment signal and to perform a confidence analysis on a user's voice signal based on the vocals signal to provide guidance to the user.

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.

Scalable similarity-based generation of compatible music mixes. Music clips are projected in a pitch interval space for computing musical compatibility between the clips as distances or similarities in the pitch interval space. The distance or similarity between clips reflects the degree to which clips are harmonically compatible. The distance or similarity in the pitch interval space between a candidate music clip and a partial mix can be used to determine if the candidate music clip is harmonically compatible with the partial mix. An indexable feature space may be both beats-per-minute (BPM)-agnostic and musical key-agnostic such that harmonic compatibility can be quickly determined among potentially millions of music clips. A graphical user interface-based user application allows users to easily discover combinations of clips from a library that result in a perceptually high-quality mix that is highly consonant and pleasant-sounding and reflects the principles of musical harmony.

A method is provided of aligning pitch data with audio data in a computing device, the method comprising the computer implemented steps of compiling a plurality of pitch data related to an audio file, each pitch data including information about at least one distinct pitch which is capable of being used by an electronic device to emulate said pitch, said plurality of pitch data compiled in a chronological order relating to said audio file, and arranging the compiled pitch data with the corresponding audio file containing audio data having at least one chord change, wherein the pitch data is offset from the audio data by a predetermined time margin. Further, an audio file is provided, stored on a non-transitory computer readable medium, having pitch data corresponding to and offset from chord changes in audio data by a predetermined time margin advance, and a non-transitory computer readable medium is provided, having stored thereon a set of computer executable instructions.

A method and system may provide for interactive song generation. In one aspect, a computer system may present options for selecting a background track. The computer system may generate suggested lyrics based on parameters entered by the user. User interface elements allow the computer system to receive input of lyrics. As the user inputs lyrics, the computer system may update its suggestions of lyrics based on the previously input lyrics. In addition, the computer system may generate proposed melodies to go with the lyrics and the background track. The user may select from among the melodies created for each portion of lyrics. The computer system may optionally generate a computer-synthesized vocal(s) or capture a vocal track of a human voice singing the song. The background track, lyrics, melodies, and vocals may be combined to produce a complete song without requiring musical training or experience by the user.

A method for processing music audio data, including providing input audio data representing a first piece of music comprising a mixture of musical timbres. The method also includes decomposing the input audio data to generate at least first-timbre decomposed data representing a first timbre selected from the musical timbres of the first piece of music, and second-timbre decomposed data representing a second timbre selected from the musical timbres of the first piece of music. The method also includes applying a transport control to obtain transport controlled first-timbre decomposed data. The method also includes recombining audio data obtained from the transport controlled first-timbre decomposed data with audio data obtained from the second-timbre decomposed data to obtain recombined audio data.

A method including decomposing a magnitude part of a signal spectrum of a mixture signal into spectral components, each spectral component including a frequency part and a time activation part; and clustering the spectral components to obtain one or more clusters of spectral components, wherein the clustering of the spectral components is computed in the time domain.

Described is a technology by which a user hums, sings or otherwise plays a user-provided rendition of a ringtone (or ringback tone) through a mobile telephone to a ringtone search service (e.g., a WAP, interactive voice response or SMS-based search platform). The service matches features of the user's rendition against features of actual ringtones to determine one or more matching candidate ringtones for downloading. Features may include pitch contours (up or down), pitch intervals and durations of notes. Matching candidates may be ranked based on the determined similarity, possibly in conjunction with weighting criterion such as the popularity of the ringtone and/or the importance of the matched part. The candidate set may be augmented with other ringtones independent of the matching, such as the most popular ones downloaded by other users, ringtones from similar artists, and so forth.

An artificial intelligence (AI) method includes generating a first musical interaction behavioral model. The first musical interaction behavioral model causes an interactive electronic device to perform a first set of musical operations and a first set of motional operations. The AI method further includes receiving user inputs received in response to the performance of the first set of musical operations and the first set of motional operations and determining a user learning progression level based on the user inputs. In response to determining that the user learning progression level is above a threshold, the AI method includes generating a second musical interaction behavioral model. The second musical interaction behavioral model causes the interactive electronic device to perform a second set of musical operations and a second set of motional operations. The AI method further includes performing the second set of musical operations and the second set of motional operations.