An automatic accompaniment apparatus including a determiner that determines a current position in a music piece in progress, a selector that selects an accompaniment element data set to be used out of a plurality of accompaniment element data sets every time the determined current position arrives at a predetermined switching position, an accompaniment data generator that generates accompaniment data indicating automatic accompaniment based on the selected accompaniment element data set, a calculator that calculates time information corresponding to a time required until the determined current position arrives at a next switching position, and a display controller that controls a display to display arrival advance notice information indicating the calculated time information.

In order to help music players without sufficient musical knowledge to adapt original music pieces but still keep the original style, the present invention provides an interactive system and the accompanying method for creating music by substituting audio tracks. The interactive system includes a database of musical elements that comprises tonality, tempo, beat, timbre, texture, chord, and pitch, a database of music that contains multiple original music pieces, and a processor. As a result, players without strong knowledge in music theories can create adapted a music piece that matches the style of the original one.

A method of making audio music selection and creating a mixtape, comprising importing song files from a song repository; sorting and filtering the song files based on selection criteria; and creating the mixtape from the song files sorting and filtering results. The sorting and filtering of the song files comprise: spectral analyzing each of the song files to extract low level acoustic feature parameters of the song file; from the low level acoustic feature parameter values, determining the high level acoustic feature parameters of the analyzed song file; determining a similarity score of each of the analyzed song files by comparing the acoustic feature parameter values of the analyzed song file against desired acoustic feature parameter values determined from the selection criteria; and sorting the analyzed song files according to their similarity scores; and filtering out the analyzed song files with first similarity scores lower than a filter threshold.

It comprises new systematic procedures for filtering synchronization data (raw data: tap times and sound/musical beat reference times) in order to obtain accurate and reliable measures of synchronization accuracy and variability (or consistency). The procedures detailed are used to provide reliable synchronization data that can be used to compute measures of synchronization accuracy and variability based on linear statistics or circular statistics. These procedures are particularly appropriate for analyzing synchronization performance in individuals with rhythmic disorders.

A property vector representing extractable measurable properties, such as musical properties, of a file is mapped to semantic properties for the file. This is achieved by using artificial neural networks ANNs in which weights and biases are trained to align a distance dissimilarity measure in property space for pairwise comparative files back towards a corresponding semantic distance dissimilarity measure in semantic space for those same files. The result is that, once optimised, the ANNs can process any file, parsed with those properties, to identify other files sharing common traits reflective of emotional-perception, thereby rendering a more liable and true-to-life result of similarity/dissimilarity. This contrasts with simply training a neural network to consider extractable measurable properties that, in isolation, do not provide a reliable contextual relationship into the real-world.

A method of generating a musical score file for one or more target musical instruments with a score generation component based on input audio data. The score generation component finds candidate musical notes within the input audio data using a frequency analysis to identify segments that share substantially the same audio frequency, and finds a best match for those candidate musical notes in audio data associated with target musical instruments in a sound database. Note, chord, and/or rhythm information is saved to a musical score file along with a page description header describing print settings. The generated musical score file can then be printed as sheet music or audibly played back over speakers.

Disclosed herein is a method for creating a preview track. The method includes: acquiring a plurality of tracks; extracting rhythm data from each of the tracks; determining a plurality of extracted parts each corresponding to the respective tracks based on the extracted rhythm data; cutting out the extracted parts from the respective tracks; and connecting the extracted parts with one another to create a preview track.

Systems and methods for simplifying music rhythms based on multiple criteria are provided herein. A user inputs a musical selection, or chooses one from an existing library of musical selections, and inputs a player proficiency level. The user then receives a music notation selection that has been adjusted to display rhythms that are appropriate for that player's proficiency level. The system is capable of receiving information and adjusting music notation arrangements in real time.

A content selection system and method for identifying and organizing moods in content using objectively measured scores for rhythm, texture and pitch (RTP) and clustered into six mood classifications based on an objective analysis of the measured scores. Digitized representations of the content may also be identified and organized based on the content's frequency data, three-dimensional shapes derived from the digitized representations, and colors derived from the frequency data. Each piece of content may be identified by at least a mood shape, but may also be identified by a mood color and/or a mood based on the clustered RTP scores and/or the digitized representation. Users of the selection system may be able to view the moods identified in the different manners, or combinations of two or three mood identifying manners and select and organize content based on the identified moods.

According to embodiments of the present invention, a musical performance evaluation method utilizes a musical composition database stored on a server containing musical encodings of all notes to be performed in a musical composition. A student user performing requested musical composition for evaluation may use a client application from a client computing device to receive audio input from the performance and compare identified frequencies to intended frequencies, and identify deviations from pitch, rhythm, and tempo throughout the performance. Deviations may be identified by switching between several pitch detection algorithms based upon a type of note (single note, chord, or plucked string) expected to be played by a student user in real time. Factors for deducting from a performance pitch score and a performance rhythm score are determined and used to calculate a performance pitch score and a performance rhythm score. Error data and scores generated may be transmitted to a server and stored in a historical session database. A student user and an instructor user may review the historical session database to determine the student's progress in musical education.