In some implementations, a user can play a music track from a dynamically generated playlist. The user can provide input indicating that the user likes or dislikes the music track and the playlist can be adjusted based on the user input. In some implementations, information can be presented to the user so that the user can preview changes to the playlist before the changes are made to the playlist. In some implementations, a user can adjust playlist criteria (configuration) by adjusting specific music characteristics. In some implementations, a user can adjust playlist criteria by manipulating a list of representative music tracks. In some implementations, a user can compare the user's playlist to playlists of other users.

A system of playing media content items determines transitions between pairs of media content items by determining desirable locations in which transitions across the pairs of media content items occur. The system uses a plurality of track features of media content items and determines such track features of each media content item associated with each of transition point candidates, such as beat positions, of that media content item. The system determines similarity in the plurality of track features between the transition point candidates of a first media content item and the transition point candidates for a second media content item being played subsequent to the first media content item. The transition points or portions of the first and second media content items are selected from the transition point candidates for the first and second media content items based on the similarity results.

An automated music composition and generation system having an automated music composition and generation engine for receiving, storing and processing musical experience descriptors and time and/or space parameters selected by the system user. The automated music composition and generation engine includes a user taste generation subsystem for automatically (i) determining the musical tastes and preferences of a system user based on user feedback and autonomous piece analysis, (ii) maintaining a system user profile reflecting musical tastes and preferences of each system user, and (iii) using the musical taste and preference information to change or modify the musical experience descriptors provided to the system to produce a digital piece of composed music composition that better reflects the musical tastes and preferences of the system user.

The present disclosure provides a method and a system for realizing intelligent matching and adding of rhythmic tracks. In one example, a method is provided, including: pre-storing a plurality of music elements; generating a rhythmic track library according to the plurality of music elements, the rhythmic track library including a plurality of rhythmic tracks based on the plurality of music elements; detecting a BPM of original music, and calculating a time interval between every two beats based on the detected BPM of the original music; selecting one or more rhythmic tracks from the rhythmic track library, and assigning a time parameter to the one or more rhythmic tracks based on the time interval so as to match the rhythm of the original music; and adding the one or more rhythmic tracks assigned with the time parameter into the original music so as to combine with the original music and play.

Techniques are disclosed relating to determining composition rules, based on existing music content, to automatically generate new music content. In some embodiments, a computer system accesses a set of music content and generates a set of composition rules based on analyzing combinations of multiple loops in the set of music content. In some embodiments, the system generates new music content by selecting loops from a set of loops and combining selected ones of the loops such that multiple ones of the loops overlap in time. In some embodiments, the selecting and combining loops is performed based on the set of composition rules and attributes of loops in the set of loops.

An automatic musical performance device includes: a storage part, storing musical performance patterns; a musical performance part, performing musical performance on the basis of the musical performance patterns stored in the storage part; an input part, to which musical performance information is input a setting part, setting a mode as to whether to switch the musical performance; a selection part, selecting a musical performance pattern estimated to have a maximum likelihood among the musical performance patterns stored in the storage part on the basis of the musical performance information input to the input part when a mode of switching the musical performance by the musical performance part is set by the setting part; and a switching part, switching at least one musical expression of the musical performance pattern played by the musical performance part to a musical expression of the musical performance pattern selected by the selection part.

Techniques are provided for generating an output file. One technique involves the steps of generating audio or MIDI content blocks from one or more musical performances; receiving an input file having audio or MIDI music content; generating a harmonic chord map for the input file; using the harmonic chord map to automatically select a subset of the audio or MIDI content blocks, and generating the output file by combining the selected subset of content blocks and the input file. This technique may enable the creation of unique and new musical accompaniments by re-purposing audio or MIDI content from back catalogs and/or out-takes of musical works. The new arrangement may be provided in multiple music styles, genres, or moods and may contain performances from multiple musical instruments, which may be pre-recorded from live instrument performances and/or of MIDI generated musical content.

Techniques are provided for generating an output file. One technique involves the steps of generating audio or MIDI content blocks from one or more musical performances; receiving an input file having audio or MIDI music content; generating a harmonic chord map for the input file; using the harmonic chord map to automatically select a subset of the audio or MIDI content blocks, and generating the output file by combining the selected subset of content blocks and the input file. This technique may enable the creation of unique and new musical accompaniments by re-purposing audio or MIDI content from back catalogs and/or out-takes of musical works. The new arrangement may be provided in multiple music styles, genres, or moods and may contain performances from multiple musical instruments, which may be pre-recorded from live instrument performances and/or of MIDI generated musical content.

Techniques are provided for generating an output file. One technique involves the steps of generating audio or MIDI content blocks from one or more musical performances; receiving an input file having audio or MIDI music content; generating a harmonic chord map for the input file; using the harmonic chord map to automatically select a subset of the audio or MIDI content blocks, and generating the output file by combining the selected subset of content blocks and the input file. This technique may enable the creation of unique and new musical accompaniments by re-purposing audio or MIDI content from back catalogs and/or out-takes of musical works. The new arrangement may be provided in multiple music styles, genres, or moods and may contain performances from multiple musical instruments, which may be pre-recorded from live instrument performances and/or of MIDI generated musical content.

The present disclosure describes techniques for identifying languages associated with music. Training data may be received, wherein the training data comprise information indicative of audio data representative of a plurality of music samples and metadata associated with the plurality of music samples. The training data further comprises information indicating a language corresponding to each of the plurality of music samples. A machine learning model may be trained to identify a language associated with a piece of music by applying the training data to the machine model until the model reaches a predetermined recognition accuracy. A language associated with the piece of music may be determined using the trained machine learning model.