Implementations of the disclosure describe systems and methods that leverage machine learning to automate the process of creating music and effects mixes from original sound mixes including domestic dialogue. In some implementations, a method includes: receiving a sound mix including human dialogue; extracting metadata from the sound mix, where the extracted metadata categorizes the sound mix; extracting content feature data from the sound mix, the extracted content feature data including an identification of the human dialogue and instances or times the human dialogue occurs within the sound mix; automatically calculating, with a trained model, content feature data of a music and effects (M&E) sound mix using at least the extracted metadata and the extracted content feature data of the sound mix; and deriving the M&E sound mix using at least the calculated content feature data.

A music composition system for composing music segments comprises: a computer interface comprising at least one external input for receiving from an external device a request for a musical composition; a controller configured to determine based on a request received at the external input a plurality of musical parts for the musical composition; and a composition engine configured to generate, for each of the determined musical parts, at least one musical segment in digital musical notation format, the musical segments configured to cooperate musically when performed simultaneously. The computer interface comprises at least one external output configured to output a response to the request, the request comprising or indicating each of the musical segments in digital musical notation format for rendering into audio data at the external device.

A first device includes one or more processors and memory storing one or more programs configured to be executed by the one or more processors. The one or more programs include instructions for receiving, from a second device, audio mix information for a first audio item and receiving, from the second device, an indication that the first audio item is to be mixed with a second audio item distinct from the first audio item. In response to the indication, the one or more programs include instructions for transmitting to the second device an audio stream including the first audio item and the second audio item mixed in accordance with the audio mix information.

A method for constructing an adaptive media file comprising a plurality of audio components configured to be used to form an audio output arranged to have a controllable tempo, the method comprising providing first audio data associated with a first audio component of the plurality of audio components, setting a playback tempo range of the first audio data, providing second audio data associated with the first audio component, setting a playback tempo range of the second audio data, wherein the tempo range of the second audio data is different to the tempo range of the first audio data, and associating the first audio data, the second audio data and the respective playback tempo ranges.

Broadly speaking, embodiments of the present invention provide systems, methods and apparatus for cellular compositions/generating music in real-time using cells (i.e. short musical motifs), where the cellular compositions are dependent on user data.

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.

A system and method for generating an audio output file are provided. The method includes using one or more processors to perform steps of: receiving audio tracks, each audio track created according to audio parameters; separating each audio track into at least one selectable audio block, each audio block including audio content from a musical instrument involved in creating the audio track; assigning a unique identifier to each audio block; using the unique identifiers to select audio blocks, and generating the audio output by combining the audio blocks. The present invention prevents the use of the same combination of audio blocks in the generation of audio output to ensure that the audio output files generated a sufficiently unique. Also provided are audio file recording, editing and mixing modules enabling a user to have full creative control over mix and other parameters to modify as desired the audio file generated.

Due to discrepancies in musical timing signatures, the invention assesses whether a recorded displacement, expressed in terms of beats and fractions, between exit and entry points for a potential musical splice or cut, corresponds to permit a seamless music splicing of different musical sections. Assessment is achieved by establishing a third time base of pulses having a length dependent upon a lowest common multiple of fractions within respective bars for different sections, with the bars of the respective sections then partitioned into an equal number of fixed length pulses. A coefficient aligns different time signatures; it is a ratio between pulses within the different sections. The coefficient identifies corresponding locations of a cut point, related to a suitable anacrusis, in terms of respectively an aligned bar, beat, quaver and fraction in differing time signatures. The coefficient ensures that the time anacrusis in one time signature is interchangeable with others.

An intuitive music composition game platform with various modes of operation in a single reader system and a music mash up collectable card game and method using cards with tags and unique identifications. A single reading platform includes a cover for card storage and for supporting a smart device platform for reading many Near Field communication (NFC) embedded cards, with stacking features and colored light indicated input lanes selection for user identification and selection. Various game modes include individual, studio mix, party modes game play features in music mash up collectable card games, which may be used together with or independent of accessory devices as controllers, or smart device user interfaces with Bluetooth or Wi-Fi for communicating user selection and operation.