Musenet (OpenAI)
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MuseNet, developed by OpenAI, is a sophisticated neural network capable of creating 4-minute musical pieces using 10 different instruments and blending styles ranging from country to Mozart to the Beatles. It operates with the same versatile unsupervised technology as GPT-2, a vast transformer model designed to forecast the next token in a sequence, applicable to both audio and text. The model learns from MIDI file data and can produce samples in a selected style by beginning with a prompt. It utilizes multiple embeddings, including positional, timing, and structural embeddings, to provide the model with additional context.
Similar neural networks:
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LALAL.AI is an advanced service for vocal removal and music source separation, enabling users to isolate vocals, accompaniment, and a variety of instruments from any audio or video file through high-quality stem splitting driven by AI technology. Users can buy packages with varying minute allocations for file splitting and download complete stems. Additionally, the service provides an API for businesses to incorporate the technology into their projects.
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Cyanite is a powerful music search and tagging platform utilizing artificial intelligence to analyze millions of songs and classify them in a short time, enabling users to provide the appropriate music content for any scenario. It features tagging, audio-based similarity search, keyword search, song recommendations, and data visualization to assist users in locating the required music efficiently. Additionally, it includes keyword cleaning to identify errors in manual tagging.