Systems, devices, and methods for segmenting musical compositions are described. Discrete, musically-coherent segments (such as intro, verse, chorus, bridge, solo, and the like) of a musical composition are identified. Distance measures are used to evaluate whether each bar of a musical composition is more like the bars that directly precede it or more like the bars that directly succeed it, and each respective series of musically similar bars is assigned to the same respective segment. Large changes in the distance measure(s) between adjacent bars may be used to identify boundaries between abutting musical segments. Computer systems and computer program products for implementing segmentation are also described. The results of segmentation may advantageously be applied in computer-based composition of music and musical variations, as well as in other applications involving labelling, characterizing, or otherwise processing music.

The present application discloses and describes neurostimulation systems and methods that include, among other features, (i) neural stimulation through audio with dynamic modulation characteristics, (ii) audio content serving and creation based on modulation characteristics, (iii) extending audio tracks while avoiding audio discontinuities, and (iv) non-auditory neurostimulation and methods, including non-auditory neurostimulation for anesthesia recovery.

A device to determine a number of beats per bar from a music data includes at least one processor configured to calculate a weighted average beat level waveform from a first beat level waveform obtained for a first frequency band and a second beat level waveform obtained for a second frequency band; calculate autocorrelation on the weighted average beat level waveform by varying an amount of a shift interval for the autocorrelation; determine a plurality of the shift intervals at which correlation values of the autocorrelation are n highest, where n is a positive integer greater than or equal to 2; and determine the number of beats per bar based on the determined plurality of the shift intervals at which the correlation values of the autocorrelation are n highest.

Systems, devices, and methods for segmenting musical compositions are described. Discrete, musically-coherent segments (such as intro, verse, chorus, bridge, solo, and the like) of a musical composition are identified. Distance measures are used to evaluate whether each bar of a musical composition is more like the bars that directly precede it or more like the bars that directly succeed it, and each respective series of musically similar bars is assigned to the same respective segment. Large changes in the distance measure(s) between adjacent bars may be used to identify boundaries between abutting musical segments.

Computer systems and computer program products for implementing segmentation are also described. The results of segmentation may advantageously be applied in computer-based composition of music and musical variations, as well as in other applications involving labeling, characterizing, or otherwise processing music.

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.

A method of converting a frame of a voice sample to a singing frame includes obtaining a pitch value of the frame; obtaining formant information of the frame using the pitch value; obtaining aperiodicity information of the frame using the pitch value; obtaining a tonic pitch and chord pitches; using the formant information, the aperiodicity information, the tonic pitch, and the chord pitches to obtain the singing frame; and outputting or saving the singing frame.

A chord processing device includes a memory storing instructions, and a processor configured to implement the stored instructions to execute a plurality of tasks, including: a receiving task that receives a first chord consisting of plural notes, an analysis task that determines whether the first chord c is a subject chord, and a converting task that, in a case where the analysis task determines that the first chord is the subject chord, converts the first chord into a second chord that relates to the first chord a case where the first chord satisfies a prescribed chord-related condition relating to the first and second chords, while not converting the first chord in a case where the first chord does not satisfy the prescribed chord-related condition.

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.

Embodiments cover identifying an unidentified media content item as a cover of a known media content item using lyrical contents. In an example, a processing device receives an unidentified media content item and determines lyrical content associated with the unidentified media content item. The processing device then determines a lyrical similarity between the lyrical content associated with the unidentified media content item and additional lyrical content associated with a known media content item of a plurality of known media content items. The processing device then identifies the unidentified media content item as a cover of the known media content item based at least in part on the lyrical similarity, resulting in an identified cover-media content item.

Embodiments cover identifying an unidentified media content item as a cover of a known media content item using lyrical contents. In an example, a processing device receives an unidentified media content item and determines lyrical content associated with the unidentified media content item. The processing device then determines a lyrical similarity between the lyrical content associated with the unidentified media content item and additional lyrical content associated with a known media content item of a plurality of known media content items. The processing device then identifies the unidentified media content item as a cover of the known media content item based at least in part on the lyrical similarity, resulting in an identified cover-media content item.