Abstract: A skip behavior analyzer is part of a media delivery system that allows for unbiased A/B testing of a plurality of versions of a song. The media delivery system stores a plurality of versions of a song and randomly selects, for each requesting device, a version of the song to associate with that device. Each time the device requests the song, thereafter, the media delivery system will provide the same version of the song for consistency. The media delivery system then gathers song play and skip information, calculates the differences in distribution of the skip behavior, and provides the skip information to allow a music composer to better determine which version of a song is more popular and why that is so.

Abstract: The present disclosure relates to a method for generating a new chord sequence. The method comprises obtaining known chord sequences (S), wherein each known chord sequence has a respective known transition (T) between each pair of consecutive chords (C) in the known chord sequence. The method also comprises, among the chords of the known sequences, determining one or more new allowed transitions (N). The method also comprises, among the chords of the known chord sequences, generating a new chord sequence (L) based on there being a respective known or new allowed transition between each two consecutive chords in the new chord sequence, and the new chord sequence being loopable by there also being a known or new allowed transition between a first chord and a last chord in the new chord.

Abstract: This disclosure relates to editing an audio file of a time stream having a plurality of tones T. The stream is cut at a first time point of the stream, producing a first cut A cutting the stream into a first stream and a second stream, whereby each tone which extends across the first cut, is cut into a first part Ta which is in the first stream and a second part Tb which is in the second stream. For each of the tones extending across the first cut, a respective memory space is allocated to each of the first part and the second part, each of the memory spaces storing an original state of the tone. The first stream is allocated with a further stream, comprising adjusting the first part of one of the tones based on the information stored in the memory space allocated to said first part.

Abstract: The present disclosure relates to a method of automatically preparing a MIDI file based on a target MIDI file comprising respective note information about each of a plurality of target notes and a source MIDI file comprising respective note information about each of a plurality of source notes. Each note information comprises pitch information defining a pitch of the note. The method comprises ranking the plurality of target notes based on the pitch of each target note. The method also comprises, for each of the ranked target notes, removing the pitch information from the note information of the target note. The method also comprises, for each of the ranked target notes, replacing the removed pitch information with pitch information of a corresponding source note, whereby the target note has the same pitch as the corresponding source note, forming a plurality of new notes of a new MIDI file.

Abstract: A system is provided for editing an audio file. The system displays, on an electronic device, a piano roll. The system receives a user input to cut a segment of the piano roll. The segment of the piano roll includes a respective tone that extends across both sides of the segment of the piano roll, such that the respective tone includes: a first portion of the respective tone that precedes the segment of the piano roll; and a second portion of the respective tone that follows the segment of the piano roll. In response to the user input to cut the segment of the piano roll, the system cuts the segment from the piano roll and, without user intervention, concatenate the first portion of the respective tone with the second portion of the respective tone.

Abstract: A computing device comprising a display screen, the computing device being configured to decompose a media recording into a plurality of media recording salient events, apply each of the media recording salient events to a reinforcement model, display on the display screen (i) a mapping of the plurality of media recording salient events and (ii) for at least one of the plurality of media recording salient events, at least one selectable next best action, the computing device further configured to replace at least one of the plurality of media recording salient events with at least one selectable next best action to create a derivative media recording including at least one replacement media recording action.

Abstract: Methods, systems and computer program products are provided for determining acoustic feature vectors of query and target items in a first vector space, and mapping the acoustic feature vectors to a second vector space having a lower dimension. The distribution of vectors in the second vector space can then be used to identify items from the same songs, and/or items that are complementary. A mapping function is trained using a machine learning algorithm, such that complementary audio items are closer in the second vector space than the first, according to a given distance metric.

Abstract: The present disclosure relates to a method of automatically preparing a MIDI file based on a target MIDI file comprising respective note information about each of a plurality of target notes and a source MIDI file comprising respective note information about each of a plurality of source notes. Each note information comprises pitch information defining a pitch of the note. The method comprises ranking the plurality of target notes based on the pitch of each target note. The method also comprises, for each of the ranked target notes, removing the pitch information from the note information of the target note. The method also comprises, for each of the ranked target notes, replacing the removed pitch information with pitch information of a corresponding source note, whereby the target note has the same pitch as the corresponding source note, forming a plurality of new notes of a new MIDI file.

Abstract: An electronic device segments a first and second MIDI files into pluralities of source segments and target segments. For each of a plurality of consecutive pairs of first and second target segments, the electronic device identifies a first source segment corresponding to the first target segment of the consecutive pair and identifies a second source segment corresponding to the second target segment of the consecutive pair, where the first and second source segments are identified by determining that the first and second source segments are harmonically conformant to the corresponding first and second target segments, and determining that a transition between the first and second source segments is graphically conformant to a transition between a consecutive pair of source segments. The electronic device generates a third MIDI file using the identified first and second source segments for each of the plurality of consecutive pairs of first and second target segments.

Abstract: An electronic device receives a source audio file from a user of a digital audio workstation and a target MIDI file, the target MIDI file comprising digital representations for a series of notes. The electronic device generates a series of sounds from the target MIDI file, each respective sound in the series of sounds corresponding to a respective note in the series of notes. The electronic device divides the source audio file into a plurality of segments. For each sound in the series of sounds, the electronic device matches a segment from the plurality of segments to the sound based on a weighted combination of features identified for the corresponding sound. The electronic device generates an audio file in which the series of sounds from the target MIDI file are replaced with the matched segment corresponding to each sound.

Abstract: A training set for use during content generation is generated by applying a first machine learning process P1 to a first finite sequence s wherein s has a length Ls, to generate a first statistical model M(s). The first statistical model M(s) is sampled using a first sampling process G to generate a second finite sequence t wherein t has a length Lt. A second machine learning process P2 is applied to the second finite sequence t to generate a second statistical model M(t), wherein no substring of the second finite sequence t of length d is identical to a substring of the first finite sequence s, wherein d is a predetermined number of elements in a sequence.

Abstract: A device for automatically generating a real time accompaniment of input music data includes a music input that receives music data. A music analyzer analyzes received music data to obtain a music data description including one or more characteristics of the analyzed music data. A query generator generates a query to a music database including music patterns and associated metadata including one or more characteristics of the music patterns, the query being generated from the music data description and from an accompaniment description describing preferences of the real time accompaniment and/or music rules describing general rules of music. A query interface queries the music database using a generated query and receives a music pattern selected from the music database by use of the query. A music output outputs the received music pattern.

Abstract: Methods, systems and computer program products are provided for determining acoustic feature vectors of query and target items in a first vector space, and mapping the acoustic feature vectors to a second vector space having a lower dimension. The distribution of vectors in the second vector space can then be used to identify items from the same songs, and/or items that are complementary. A mapping function is trained using a machine learning algorithm, such that complementary audio items are closer in the second vector space than the first, according to a given distance metric.

Abstract: A computing device comprising a display screen, the computing device being configured to decompose a media recording into a plurality of media recording salient events, apply each of the media recording salient events to a reinforcement model, display on the display screen (i) a mapping of the plurality of media recording salient events and (ii) for at least one of the plurality of media recording salient events, at least one selectable next best action, the computing device further configured to replace at least one of the plurality of media recording salient events with at least one selectable next best action to create a derivative media recording including at least one replacement media recording action.

Abstract: A skip behavior analyzer is part of a media delivery system that allows for unbiased A/B testing of a plurality of versions of a song. The media delivery system stores a plurality of versions of a song and randomly selects, for each requesting device, a version of the song to associate with that device. Each time the device requests the song, thereafter, the media delivery system will provide the same version of the song for consistency. The media delivery system then gathers song play and skip information, calculates the differences in distribution of the skip behavior, and provides the skip information to allow a music composer to better determine which version of a song is more popular and why that is so.

Abstract: A computing device comprising a display screen, the computing device being configured to decompose a media recording into a plurality of media recording salient events, apply each of the media recording salient events to a reinforcement model, display on the display screen (i) a mapping of the plurality of media recording salient events and (ii) for at least one of the plurality of media recording salient events, at least one selectable next best action, the computing device further configured to replace at least one of the plurality of media recording salient events with at least one selectable next best action to create a derivative media recording including at least one replacement media recording action.

Abstract: Methods, systems and computer program products are provided for testing a lead sheet for plagiarism. A test lead sheet receiving having a plurality of passages is received at receiving a plagiarism detector. A set of annotations describing a level of plagiarism of a plurality of elements (e.g., chord sequence, subsequences, melodic fragments (i.e., notes), rhythm, harmony, etc.) of the test lead sheet in relation to the preexisting lead sheets are generated and output via an output device.

Abstract: A system is provided for editing an audio file. The system displays, on an electronic device, a piano roll. The system receives a user input to cut a segment of the piano roll. The segment of the piano roll includes a respective tone that extends across both sides of the segment of the piano roll, such that the respective tone includes: a first portion of the respective tone that precedes the segment of the piano roll; and a second portion of the respective tone that follows the segment of the piano roll. In response to the user input to cut the segment of the piano roll, the system cuts the segment from the piano roll and, without user intervention, concatenate the first portion of the respective tone with the second portion of the respective tone.

Abstract: A skip behavior analyzer is part of a media delivery system that allows for unbiased A/B testing of a plurality of versions of a song. The media delivery system stores a plurality of versions of a song and randomly selects, for each requesting device, a version of the song to associate with that device. Each time the device requests the song, thereafter, the media delivery system will provide the same version of the song for consistency. The media delivery system then gathers song play and skip information, calculates the differences in distribution of the skip behavior, and provides the skip information to allow a music composer to better determine which version of a song is more popular and why that is so.

Abstract: Methods, systems and computer program products are provided for identifying an audio stem. Audio stems (t1, . . . , tN) are stored on a stem database and songs (S1, . . . , SP) made with at least a subset of the plurality of the audio stems (t1, . . . , tN) are stored on a song database. At least partially composed song (S*) having a predetermined number of pre-selected stems (k) are received. In turn, a probability vector (or relevance value or ranking) is produced for each stem (t1, . . . , tN) to be complementary to the at least partially composed song (S*).