Smart appliances communicate over a local area network. A selected sound palette includes multiple soundfile groups that each include multiple soundfiles. Each soundfile within a given sound palette has a first common sound attribute, each soundfile within a given soundfile group has a second common sound attribute, each soundfile within a given soundfile group has a variation of the second common sound attribute that is unique among the soundfile groups, and each soundfile within a given soundfile group has a unique variation of a third common sound attribute. One soundfile group may be assigned to each of the appliances, and one soundfile within each soundfile group may be assigned to an alert type. The assigned soundfile group is sent to the appliance with identification of the alert type to which each soundfile is assigned, such that the appliance may use an assigned soundfile for audible alerts.

Novel tools and techniques are provided for generating music, and, more particularly, to methods, systems, and apparatuses for generating music associated with a state contained within a communication, for a user interface for generating music associated with a state, for generated music to reflect movement between states, and for generating music to guide a user toward a desired state. In various embodiments, a computing system might analyze a communication of a user to determine at least one state contained within the communication. The communication may be a sensor communication, a biometric/health communication, a voice communication, a numerical communication, a textual communication, a picture/video communication, etc. Based on the determined at least one state, the computing system might generate music associated with the state or continuously control the generation of music to guide a user toward a desired state. A user interface for generating music may also be provided.

A system and method for synchronizing an audio or MIDI file with a video file are provided. The method includes receiving a first audio or MIDI file, receiving a video file, and operating an audio synchronization module to perform steps of synchronizing the first audio or MIDI file with the video file, marking an event in the video file at a point on a timeline, detecting a first musical key for the event, retrieving a musical stinger or swell from a library, in which the musical stinger or swell is a second audio or MIDI file and is tagged with a second musical key, and the second musical key is relevant to the first musical key, and placing the musical stinger or swell at the point of the timeline marked for the event.

Techniques are provided for generating an output file. One technique involves the steps of generating audio or MIDI content blocks from one or more musical performances; receiving an input file having audio or MIDI music content; generating a harmonic chord map for the input file; using the harmonic chord map to automatically select a subset of the audio or MIDI content blocks, and generating the output file by combining the selected subset of content blocks and the input file. This technique may enable the creation of unique and new musical accompaniments by re-purposing audio or MIDI content from back catalogs and/or out-takes of musical works. The new arrangement may be provided in multiple music styles, genres, or moods and may contain performances from multiple musical instruments, which may be pre-recorded from live instrument performances and/or of MIDI generated musical content.

A computer-implemented method of training an artificial neural network (ANN) by generating one or more learned parameters for use during a subsequent inference phase of the trained ANN, comprises providing training data representing first and second input signals, the second input signal exhibiting one or more transformations relative to the first signal selected from a set of transformations; using the ANN and in response to the one or more parameters, generating a magnitude and phase representation of each of the first and second input signals; and training the one or more parameters, in dependence upon a constraint which causes the magnitude representation of the first input signal and the magnitude representation of the second input signal to tend to become more similar to one another, the training step comprising: detecting an error signal; and updating the one or more parameters in dependence upon the error signal.

According to an embodiment, there is provided a system and method for automatic AI-based song construction based on ideas of a user. In some embodiments, an embodiment is provided with a database that contains harmony templates which can be used by the user to augment the playback of a given music work. Various embodiments of the instant invention also benefit from a combination of expert knowledge resident in an expert engine which contains rules for musically correct song generation and machine learning in an AI-based audio loop selection engine for the selection of compatible audio loops from a database of audio loops.

Smart appliances communicate over a local area network. A selected sound palette includes multiple soundfile groups that each include multiple soundfiles. Each soundfile within a given sound palette has a first common sound attribute, each soundfile within a given soundfile group has a second common sound attribute, each soundfile within a given soundfile group has a variation of the second common sound attribute that is unique among the soundfile groups, and each soundfile within a given soundfile group has a unique variation of a third common sound attribute. One soundfile group may be assigned to each of the appliances, and one soundfile within each soundfile group may be assigned to an alert type. The assigned soundfile group is sent to the appliance with identification of the alert type to which each soundfile is assigned, such that the appliance may use an assigned soundfile for audible alerts.

A system providing a music service platform includes a music controller device, a client device, and a server. The music controller device receives musical data indicative of a music performance by an individual and generates metadata characterizing the musical data. The client device includes a software application that receives the musical data and the metadata from the music controller device and presents a graphical user interface for display on the client device. The graphical user interface includes information related to the musical data and the metadata. The server receives the musical data and the metadata, generates one or more analytics associated with the musical data, and performs one or more data aggregation techniques on the musical data.

An information processing device (100) according to the present disclosure is an information processing device that controls a first application (20) and a second application (20) that functions as a plug-in that extends functions of the first application. The first application includes a control unit (161) that controls operation of the second application in the first application, and the second application includes a selection unit (166) that selects setting information for controlling a composition function based on machine learning, and a transmission/reception unit (167) that transmits the setting information to an external server (200) that executes a composition function based on machine learning and receives music data composed by the external server through a network.

A waveform analysis method utilizes a convolutional neural network (CNN) to differentiate waveform data based on applying one or more pre-trained weights to pre-processed spectrograms sampled uniformly from the waveform data. The CNN outputs, in real-time, the level of confidence that any number of potential custom tags accurately describe the sampled waveform data. These outputs can vary across a single musical track. When these outputs are packaged with the track's metadata, a comprehensive searchable database can be formed which provides quantifiable means of differentiating qualitative features of music.