Website that cuts, converts or merges a file. Supports multiple audio formats.
ABC, developed by Chris Walshaw, is a format designed to notate music using plain text. It was originally designed for folk tunes of Western European origin which can be written on one staff, but has since been extended to support the notation of complete, classical music scores.
Since its introduction at the end of 1991, ABC has become very popular. Programs on many operating systems use ABC as an input and/or output format. There are programs which produce printed sheet music or allow for computer performances, search in tune databases, or that analyze tunes in some way.
The aim of this project is to promote the ABC language by maintaining the ABC standard and a set of software and source code that manipulate and present music written in ABC.
An internet repository for permanent storage of quality music modules from the tracking and demo scene. The Mod Archive began collecting music modules back in 1996. Since then, it has grown and become one of the largest and oldest collections online, thanks to the artists that contributed to The Mod Archive and the Public Domain in general.
Online database that compares the performance of common sample rate converters.
Algorithms and Interactive Tools for Exploring Music Composition, Analysis, and Interdisciplinary Learning.
This web site has interactive tools that provide a unique learning experience for users, regardless of their musical training. Students of music composition can explore algorithmic composition, while others can create musical representations of models for the purpose of aural interpretation and analysis. Here, the algorithmic process is used in a creative context so that users can convert sequences of numbers into sounds.
We’ve created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music, but instead discovered patterns of harmony, rhythm, and style by learning to predict the next token in hundreds of thousands of MIDI files. MuseNet uses the same general-purpose unsupervised technology as GPT-2, a large-scale transformer model trained to predict the next token in a sequence, whether audio or text.