Computer-based systems, devices, and methods for assigning mood labels to musical compositions are described. A mood classifier is trained based on mood-labeled musically-coherent segments of musical compositions and subsequently applied to automatically assign mood labels to musically-coherent segments of musical compositions. In both cases, the musically-coherent segments are generated using automated segmentation algorithms.

Computer-based systems, devices, and methods for generating variations of musical compositions are described. Musical compositions stored in digital media include one or more music data object(s) that encode notes. A first set of notes is characterized and a transformation is applied to replace at least one note in the first set of notes with at least one note in a second set of notes. The transformation may explore or call upon the full range of musical notes available without being constrained by conventions of musicality and harmony. For each particular note in the second set of notes that replaces a note in the first set of notes, whether the particular note is in musical harmony with other notes in the music data object is separately assessed and, if not, the particular note is adjusted to bring it into musical harmony with other notes in the music data object.

Computer-based systems, devices, and methods for generating musical compositions are described. A population of musical compositions stored in digital media are each segmented to produce abridged samples. The samples are analyzed to identify “parent” compositions that best exhibit or evoke a particular desired quality. The parent compositions are cross-bred to generate a set of child compositions which are similarly segmented and analyzed. The child compositions that best exhibit or evoke the particular desired quality are re-cast as parent compositions from which another generation of child compositions are bred. Mutations in the form of musical variations are inserted in at least some iterations and the process is repeated until at least one child composition that satisfies some exit criterion is returned.

Computer-based systems, devices, and methods for generating musical note sequences are described. One or more musical composition(s) stored in digital media include one or more data object(s) that encode notes and/or note sequences. At least one note sequence is processed to form a time-ordered sequence of parallel notes, which is analyzed to determine a k-back probability transition matrix for the at least one note sequence. An attribute, such as a style, of the at least one note sequence is thus encoded and used to generate new note sequences that embody a similar attribute or style. In some implementations, the at least one note sequence may include a concatenated set of note sequences representative of a particular library of musical compositions.

A sound source for an electronic percussion instrument and sound production control method thereof are provided. An electronic drum sound source device performs a weighting operation on four pieces of waveform information (pitch envelope, amplitude envelope, start phase) stored in a waveform table according to the beating conditions (hitting point position, velocity) based on the output from a struck sensor of an electronic drum pad. The electronic drum sound source device creates a sine wave on the basis of the waveform information whereon the weighting operation was performed, and generates musical sounds (sounds of percussion instrument) by synthesizing the sine wave with the waveforms of residual waveform data whereon the weighting operation was performed. The sine wave is not synthesized with waveform data other than the residual waveform data, and thus consistent musical sounds with no phase interference can be reproduced.

A sound source for an electronic percussion instrument and sound production control method thereof are provided. An electronic drum sound source device performs a weighting operation on four pieces of waveform information (pitch envelope, amplitude envelope, start phase) stored in a waveform table according to the beating conditions (hitting point position, velocity) based on the output from a struck sensor of an electronic drum pad. The electronic drum sound source device creates a sine wave on the basis of the waveform information whereon the weighting operation was performed, and generates musical sounds (sounds of percussion instrument) by synthesizing the sine wave with the waveforms of residual waveform data whereon the weighting operation was performed. The sine wave is not synthesized with waveform data other than the residual waveform data, and thus consistent musical sounds with no phase interference can be reproduced.

A computer-implemented method generates a frequency component vector of time series data, by executing a first process and a second process in each unit step. The first process includes: receiving first data; and processing the first data using a first neural network to generate intermediate data. The second process includes: receiving the generated intermediate data; and generating a plurality of component values corresponding to a plurality of frequency bands based on the generated intermediate data such that: a first component value corresponding to a first frequency band is generated using a second neural network based on the generated intermediate data; and a second component value corresponding to a second frequency band different from the first frequency band is generated using the second neural network based on the generated intermediate data and the generated first component value corresponding to the first frequency band.

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.

According to an embodiment, there is provided a system and method for automatic AI controlled loop based song construction. It provides and benefits from a machine learning AI in a audio loop selection engine for the generation of a song structure and for the selection of fitting audio loops from a database of audio loops. In one embodiment, the instant method provides a music generation process that utilizes an AI system that has been trained and validated on a music item database to complete the creation of a music item given an incomplete song that was started but not finished by a user.

According to an embodiment, there is provided a system and method for automatic AI controlled loop based song construction. It provides and benefits from a machine learning AI in a audio loop selection engine for the generation of a song structure and for the selection of fitting audio loops from a database of audio loops. In one embodiment, the instant method provides a music generation process that utilizes an AI system that has been trained and validated on a music item database to complete the creation of a music item given an incomplete song that was started but not finished by a user.