In exemplary embodiments of the present invention systems and methods are provided to implement and facilitate cross-fading, interstitials and other effects/processing of two or more media elements in a personalized media delivery service so that each client or user has a consistent high quality experience. The effects or crossfade processing can occur on the broadcast, publisher or server-side, but can still be personalized to a specific user, thus still allowing a personalized experience for each individual user, in a manner where the processing burden is minimized on the downstream side or client device. This approach enables a consistent user experience, independent of client device capabilities, both static and dynamic. The cross-fade can be implemented after decoding the relevant chunks of each component clip, processing, recoding and rechunking, or, in a preferred embodiment, the cross-fade or other effect can be implemented on the relevant chunks to the effect in the compressed domain, thus obviating any loss of quality by re-encoding. A large scale personalized content delivery service can be implemented by limiting the processing to essentially the first and last chunks of any file, since there is no need to processing the full clip. In exemplary embodiments of the present invention this type of processing can easily be accommodated in cloud computing technology, where the first and last files may be conveniently extracted and processed within the cloud to meet the required load. Processing may also be done locally, for example, by the broadcaster, with sufficient processing power to manage peak load.

Systems and methods for mixing music are disclosed. Audio mix information is received from a plurality of users. Mix rules are determined from the audio mix information from the plurality of users, wherein the mix rules include a first mix rule associated with a first audio item. The first mix rule relates to an overlap of the first audio item with another audio item. The first mix rule is made available to one or more clients. In some implementations, making the first mix rule available to the one or more clients includes transmitting, to the first client, information enabling the first client to playback a transition between the first audio item and the second audio item in accordance with the first mix rule.

Digital signal processing and machine learning techniques can be employed in a vocal capture and performance social network to computationally generate vocal pitch tracks from a collection of vocal performances captured against a common temporal baseline such as a backing track or an original performance by a popularizing artist. In this way, crowd-sourced pitch tracks may be generated and distributed for use in subsequent karaoke-style vocal audio captures or other applications. Large numbers of performances of a song can be used to generate a pitch track. Computationally determined pitch trackings from individual audio signal encodings of the crowd-sourced vocal performance set are aggregated and processed as an observation sequence of a trained Hidden Markov Model (HMM) or other statistical model to produce an output pitch track.

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.

Systems and methods are provided to implement and facilitate cross-fading, interstitials and other effects/processing of two or more media elements in a personalized media delivery service to experience consistent high quality. The effects or crossfade processing may occur on the broadcast/publisher/server-side, but may be personalized to a specific user, allowing a personalized experience for each user, where the processing burden is minimized on the downstream side/client device. This approach enables a consistent user experience, independent of client device capabilities. A large-scale personalized content delivery service may be implemented by limiting the processing to the first and last chunks of any file. In exemplary embodiments, this type of processing may easily be accommodated in cloud computing technology, where first and last files are extracted and processed within the cloud to meet the required load. Processing may be done locally, by the broadcaster, with sufficient processing power to manage peak load.

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.

The present application provides techniques for displaying music score segments in target music videos. The techniques comprise determining a digital music score corresponding to a piece of music comprised in a target music video; determining a segment of the digital music score corresponding to a current playing progress of the target music video based at least in part on a playing progress of the target music video; generating an image of a music score segment corresponding to the segment of the digital music score based on a predetermined condition; and presenting the image on a corresponding interface of playing the target music video.

A music composition, editing, and playback system and method provides a user interface design based on geometric interpretation of music theory replacing traditional modern music notation with geometric shapes including chords represented by polygons that are colored with colors or hues.

A music composition, editing, and playback system and method provides a user interface design based on geometric interpretation of music theory replacing traditional modern music notation with geometric shapes including chords represented by polygons that are colored with colors or hues.

Vocal musical performances may be captured and continuously pitch-corrected at a mobile device for mixing and rendering with backing tracks in ways that create compelling user experiences. In some cases, the vocal performances of individual users are captured in the context of a karaoke-style presentation of lyrics in correspondence with audible renderings of a backing track. Such performances can be pitch-corrected in real-time at the mobile device in accord with pitch correction settings. In some cases, such pitch correction settings code a particular key or scale for the vocal performance or for portions thereof. In some cases, pitch correction settings include a score-coded melody sequence of note targets supplied with, or for association with, the lyrics and/or backing track. In some cases, pitch correction settings are dynamically variable based on gestures captured at a user interface.