Abstract: Techniques are disclosed relating to adjusting, via a user interface, parameters (e.g., the gain) of musical phrases in generative music content. A computing system may select a set of musical phrases to include in generative music content. The computing system may determine respective gain values of multiple selected musical phrases. The computing system may mix the selected musical phrases based on the determined gain values to generate output music content. The computing system may cause display of an interface that visually indicates the selected musical phrases and their determined gain values relative to other selected musical phrases. The computing system may receive user gain input that indicates to adjust a gain value of one of the selected musical phrases. The computing system may adjust the mix of the selected musical phrases based on the user input.

Abstract: Techniques are disclosed relating to automatically generate new music content based on image representations of audio files. A computer system generate image representations of audio files. The image representations may be generated, for example, based on data in the audio files and MIDI representations of the audio files. Audio files for combination may then be selected based on analysis of the image representations. For example, image-based machine learning algorithms may be implemented to assess the image representations and select music for combining.

Abstract: Techniques are disclosed relating to implementing user-created controls to modify music content. A music generator system may be configured to automatically generate output music content by selecting and combining audio tracks based on various parameters. Users may create their own control elements that the music generator system may train (e.g., using AI techniques) to generate output music content according to a user's intended functionality of a user-created control element.

Abstract: Techniques are disclosed relating to implementing audio techniques for real-time audio generation. For example, a music generator system may generate new music content from playback music content based on different parameter representations of an audio signal. In some cases, an audio signal can be represented by both a graph of the signal (e.g., an audio signal graph) relative to time and a graph of the signal relative to beats (e.g., a signal graph). The signal graph is invariant to tempo, which allows for tempo invariant modification of audio parameters of the music content in addition to tempo variant modifications based on the audio signal graph.

Abstract: Techniques are disclosed relating to implementing audio techniques for real-time audio generation. For example, a music generator system may generate new music content from playback music content based on different parameter representations of an audio signal. In some cases, an audio signal can be represented by both a graph of the signal (e.g., an audio signal graph) relative to time and a graph of the signal relative to beats (e.g., a signal graph). The signal graph is invariant to tempo, which allows for tempo invariant modification of audio parameters of the music content in addition to tempo variant modifications based on the audio signal graph.

Abstract: Various methods for representing audio suitable for use in variational audio encoding are disclosed. A method comprises maintaining, by a computing system, state information for multiple resonator models with different resonant frequencies. The method further comprises iteratively performing a number of different operations, by the computing system for multiple respective samples in a set of audio samples in the time domain. These operations include updating the state information for the multiple resonator models based on the sample amplitude. The operations also include determining respective resonator amplitudes and phases for the updated multiple resonator models and storing, respective resonator amplitude and change-in-phase information for the sample.

Abstract: Techniques are disclosed relating to automatically generate new music content. In some embodiments, a computing system receivers user input specifying a user-defined music control element. The computing system may train a machine learning model to change both composition and performance parameters based on user adjustments to the user-defined music control element. In embodiments in which composition and performance subsystems are on different devices, one device may transmit configuration information to another device, where the configuration information specifies how to adjust parameters based on user input to the user-defined music control element. Disclosed techniques may facilitate centralized learning for human-like music production while allowing individualized customization for individual users. Further, disclosed techniques may allow artists to define their own abstract music controls and make those controls available to end-users.

Abstract: Techniques are disclosed relating to automatically generating new music content based on image representations of audio files. A music generation system includes a music generation subsystem and a music classification subsystem. The music generation subsystem may generate output music content according to music parameters that define policy for generating music. The classification subsystem may be used to classify whether music is generated by the music generation subsystem or is professionally produced music content. The music generation subsystem may implement an algorithm that is reinforced by prediction output from the music classification subsystem. Reinforcement may include tuning the music parameters to generate more human-like music content.

Abstract: Techniques are disclosed relating to implementing user-created controls to modify music content. A music generator system may be configured to automatically generate output music content by selecting and combining audio tracks based on various parameters. Users may create their own control elements that the music generator system may train (e.g., using AI techniques) to generate output music content according to a user's intended functionality of a user-created control element.

Abstract: Techniques are disclosed relating to automatically generate new music content based on image representations of audio files. A computer system generate image representations of audio files. The image representations may be generated, for example, based on data in the audio files and MIDI representations of the audio files. Audio files for combination may then be selected based on analysis of the image representations. For example, image-based machine learning algorithms may be implemented to assess the image representations and select music for combining.

Abstract: Techniques are disclosed relating to implementing audio techniques for real-time audio generation. For example, a music generator system may generate new music content from playback music content based on different parameter representations of an audio signal. In some cases, an audio signal can be represented by both a graph of the signal (e.g., an audio signal graph) relative to time and a graph of the signal relative to beats (e.g., a signal graph). The signal graph is invariant to tempo, which allows for tempo invariant modification of audio parameters of the music content in addition to tempo variant modifications based on the audio signal graph.

Abstract: Techniques are disclosed relating to tracking contributions to composed music content. In some embodiments, a computer system determines playback data for a music content mix, where the playback data indicates characteristics of playback of the music content mix and the music content mix includes a determined combination of multiple audio tracks. In some embodiments, the system records, in an electronic block-chain ledger data structure, information specifying individual playback data for one or more of the multiple audio tracks in the music content mix. The information specifying individual playback data for an individual audio track may include usage data for the individual audio track and signature information associated with the individual audio track.