The present disclosure relates to an interactive music player, the interactive music player adapted to allow a user to control and mix a plurality of simultaneously played audio tracks. The present disclosure also relates to a corresponding method and computer program product.

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.

Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.

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. Effects or crossfade processing can occur on the broadcast, publisher or server-side, but can still be personalized to a specific user, in a manner that minimizes processing on the downstream side or client device. The cross-fade can be implemented after decoding, processing, re-encoding, and rechunking the relevant chunks of each component clip. Alternatively, the cross-fade or other effect can be implemented on the relevant chunks in the compressed domain, thus obviating any loss of quality by re-encoding. A large scale personalized content delivery service can limit the processing to essentially the first and last chunks of any file, there being no need to process the full clip.

A sound control device includes: a reception unit that receives a start instruction indicating a start of output of a sound; a reading unit that reads a control parameter that determines an output mode of the sound, in response to the start instruction being received; and a control unit that causes the sound to be output in a mode according to the read control parameter.

An apparatus may be designed to enable a user to receive, record, display, edit, arrange, re-arrange, play, loop, extend, export and import audio and video data. The audio and video data to be organized as, for example, but not limited to, a song comprised of song parts. The song parts may be comprised of tracks, and each track may be comprised of one or more layers. The various methods and systems disclosed herein incorporate such data segmentation to enable the user to intuitively and hands-free record, arrange, and perform songs comprised of both sequential and parallel tracks. In this way, the looper may enable a musician to record and loop tracks for a song, arrange the tracks into song parts, and during the same session, transition the playback from one song part to another, all the while recording a track on top of the transitioning song parts.

Indications of sample machine learning models which create synthetic content items are provided via programmatic interfaces. A representation of a synthetic content item produced by one of the sample models in response to input obtained from a client of a provider network is presented. In response to a request from the client, a machine learning model is trained to produce additional synthetic content items.

The present disclosure relates to an information processing apparatus, an information processing method, and a program that make it possible to adjust commonness and eccentricity of automatically generated content by likelihood exploration while satisfying reality.

Input content including a sequence of data is encoded to be converted into a latent variable, the latent variable is decoded to reconfigure output content, a loss function is calculated on the basis of a likelihood of the input content which is an input sequence, a gradient of the loss function is lowered to update the latent variable, and the updated latent variable is decoded to reconfigure output content. The present invention can be applied to an automatic content generation device.

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.

The present disclosure describes techniques for identifying music attributes. The described techniques comprises receiving audio data of a piece of music; determining at least one attribute of the piece of music based on the audio data of the piece of music using a model; the model comprising a convolutional neural network and a transformer; the model being pre-trained using training data, wherein the training data comprise labelled data associated with a first plurality of music samples and unlabelled data associated with a second plurality of music samples, the labelled data comprise audio data of the first plurality of music samples and label information indicative of attributes of the first plurality of music samples, and the unlabelled data comprise audio data of the second plurality of music samples.