Music data memory includes pieces of music within a group and other pieces of music outside the group. The next piece to be played is automatically determined by random table among pieces within the group. Favorite or newest piece is weighted to be more frequently played in the group. Piece in music data memory is automatically included into the group by random table. Newly downloaded piece into music data memory is included into the group by priority. Most frequently played piece is excluded from the group in place of newly included piece. Favorite or newest piece may be an exception of exclusion. Next piece is capable of being played in tempo similar to that of preceding piece by means of tempo-adjusted or piece replacement or repetition of the same piece for the purpose of continued baby cradling in synchronism with the same tempo of succeeding pieces.

A method of generating a musical score file for one or more target musical instruments with a score generation component based on input audio data. The score generation component finds candidate musical notes within the input audio data using a frequency analysis to identify segments that share substantially the same audio frequency, and finds a best match for those candidate musical notes in audio data associated with target musical instruments in a sound database. Note, chord, and/or rhythm information is saved to a musical score file along with a page description header describing print settings. The generated musical score file can then be printed as sheet music or audibly played back over speakers.

A method of generating a transcript file in a selected presentation format from input audio data with a transcription component. The transcription component divides the input audio data into individual sound tokens. The transcription component then identifies transcription text for subsets of the sound tokens by finding a best match for the subset in sound samples in a sound database. The transcription component then creates a transcript file and formats the transcription text in the transcript file according to a presentation format that corresponds to a sound type of the transcription text.

A method for generating voice identifications for MIDI (Musical Instrument Digital Interface) note signals. The method provides voice identification for every note in MIDI signals, which makes music learning intuitive and easier. The method can be used with any MIDI instruments as a separate unit, or a part of such instruments. Solfege is used as voice identification system since it is widely used in music education. However, any such system can be used or newly devised by preparing a different set of patches.

A system and computer-implemented method for generating music content includes a music notation data store having a collection of notation data files and an audio data store having a collection of audio data files, each data file in the notation and audio data stores including associated music characteristic metadata. One or more computer processor is arranged to receive user music preference inputs from a user interface and to search the notation and audio data stores to identify a plurality of data files corresponding to one or more user preference input. The processor randomly selects at least one notation file and at least one audio file from the identified notation and audio files and generates a music instance file by combining the selected notation and audio files for playback to the user.

Disclosed are systems, methods and devices for improving audio processing and playing multiple sounds using fewer resources. Various implementations may create audio files, in some cases from a single sample of a type of sound, where the files represent specific quantities of that type of sound, such as 10 footsteps or 1000 claps. Given a desired target number of sounds to play, various implementations determine an appropriate combination of the employable audio files to represent the desired number of sounds, and play or render the combination as an approximation of the desired target number of sounds. By using single audio files that represent and sound like multiple instances of a specified type of sound, the various implementations use significantly fewer computing and audio resources than playing an equivalent number of audio files each of which contains a single instance of the specified type of sound.

In some implementations, a computing device can be configured to present a graphical user interface that enables the dynamic authoring of music by a user of the computing device. The computing device can present a grid of cells, where each cell represents a music segment. The cells can be configured to playback the music segment once or playback the music segment in a repeating loop. The user can select (e.g., touch) one or more cells in the grid to cause the corresponding music segment to play. While playing selected cells, the user can provide input selecting various musical effects to apply to the playing music. The user can record a dynamically selected sequence of cells and musical effects to create an original musical product.

A system and method for operating and performing a remotely networked virtual musical instrument. A client transmits musical control data to a remote server over the network, encompassing a digital music engine and digitally sampled virtual musical instruments. In return, the client consumes, synchronizes, and mixes the combined server playback stream from the network of the fully expressive and interactive musical performance with zero audible latency.

A computer implemented method is provided for generating a prediction of a next musical note by a computer having at least a processor and a memory. A computer processor system is also provided for generating a prediction of a next musical note. The method includes storing sequential musical notes in the memory. The method further includes dividing, by the processor, the sequential musical notes into sections of a given length based on a Generative Theory of Tonal Music. The method also includes generating, by the processor, the prediction of the next musical note based upon a music model, the sections, and the sequential musical notes stored in the memory. The given length is determined based on one or more conditions.

A device for reading waveform data of a musical tone from a primary storage device and transferring the read waveform data to a secondary storage device for tone reproduction includes a processor configured to perform: retrieving, for each waveform of a plurality of waveforms that represent a musical tone stored in the primary storage device, segment group information from the primary storage device; retrieving the plurality of waveforms that represent the musical tone from the primary storage device, the waveform group retrieval process retrieving a waveform or waveforms, among the plurality of waveforms, that have the same segment group information as a group; and writing, as a single group, the waveform or waveforms, among the plurality of waveforms, that have the same segment group information onto one of prescribed storage segments that are storage regions of prescribed sizes in the secondary storage device.