According to an embodiment, there is provided a system and method for automatically generating a complete music work from a partially completed work provided by a user. One approach uses an artificial intelligence (AI) engine that is trained by creating incomplete works from a database of complete works and then instructing the AI to complete the incomplete works. A comparison is made between the completed works and the originals to determine the effectiveness of the training process. After the AI is trained, it is applied to the user's incomplete work to produce a final music item.

The present invention provides a method for processing audio data, comprising the steps of providing a first audio track of mixed input data, said mixed input data representing an audio signal containing a plurality of different timbres, decomposing the mixed input data to obtain decomposed data representing an audio signal containing at least one, but not all, of the plurality of different timbres, providing a second audio track, analyzing audio data, including at least the decomposed data, to determine at least one mixing parameter, and generating an output track based on the at least one mixing parameter, said output track comprising first output data obtained from the first audio track and second output data obtained from the second audio track.

The present invention relates to systems and methods for visual image audio composition. In particular, the present invention provides systems and methods for audio composition from a diversity of visual images and user determined sound database sources.

An adaptive music playback system is disclosed. The system includes a composition system that receives information corresponding to user activity levels. The composition system modifies the composition of a song in response to changes in user activity. The modifications are made according to a set of composition rules to facilitate smooth musical transitions.

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.

A media processor and method of customizing a media composition enables a user to compile a customized media file with selectable tracks and a primary track. Each of the selectable tracks includes a recording of a performer performing a part of a particular composition, such as an instrument or vocal part in a musical piece. The user is provided an option to select one or more of the tracks; and a customized media file is prepared based on the user selected tracks and including the primary track.

Systems and methods for generating a playback-information display during time compression or expansion of an audio signal are provided. The system includes a processor that performs a method including displaying a first remaining playback-time associated with an audio file; adjusting the playback speed of the audio file during playback of the audio file; and, in response to the playback speed being adjusted, automatically displaying a second remaining playback-time associated with the audio file during playback of the audio file.

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.

There are disclosed automatic mixers and methods for creating a surround audio mix. A set of rules may be stored in a rule base. A rule engine may select a subset of the set of rules based, at least in part, on metadata associated with a plurality of stems. A mixing matrix may mix the plurality of stems in accordance with the selected subset of rules to provide three or more output channels.