WhatTheBeat
Tags
Pricing model
Upvote
0
The WhatTheBeat tool enables users to understand the meanings behind their favorite songs by artists like Drake, Eminem, A.R. Rahman, Justin Bieber, Michael Jackson, and Taylor Swift, through the use of artificial intelligence.
Similar neural networks:
0
Loudly is an AI-driven music creation platform that allows users to produce, customize, and explore music specifically suited to their needs. It offers the capability to instantly generate original, high-quality tracks, discover music through AI-driven suggestions, remix songs, and access a collection of royalty-free music and sounds adaptable for various projects. Content creators, startups, small and medium enterprises, filmmakers, and multimedia artists can utilize Loudly to enrich their digital projects with tailored soundtracks, streamline their creative processes, and make their content distinctive without concerns over copyright issues. The platform's user-friendly nature and ability to effortlessly create unique music make it a compelling option for anyone seeking to enhance their videos, applications, or other multimedia content with professional-grade audio.
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.
0
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.