Jamahook Sound Assistant
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Jamahook Sound Assistant is an AI-enhanced plugin crafted for music producers to swiftly locate sounds that are harmonically and rhythmically in sync with their projects. By integrating seamlessly with digital audio workstations (DAWs), it enables users to align loops and samples from their own local audio collection as well as a vast cloud-based loop library. This tool is ideal for producers seeking to optimize their creative workflow, rediscover overlooked audio materials, and effortlessly craft unique tracks without exiting their DAW setup. The blend of local and cloud-based matching capabilities, coupled with the AI's proficiency in categorizing and suggesting pertinent audio elements, makes Jamahook Sound Assistant an invaluable asset for boosting creativity and productivity in music production.
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EVITA is an AI-powered singing coach, voice enthusiast, and performer. It aids users in exploring their repertoire, characters, and musicals by creating personalized vocal exercises and more. Users can find new songs suited to their specific requirements, delve into the psyche of their characters, enhance their voices, craft new lyrics, and utilize effective vocal coaching tools. Additionally, it offers a vocal warmup generator, answers to urgent questions, a database of vocal exercises, song and character analysis, a song finder, a hero's journey, internal monologue creation, songwriting, tongue twisters, a Shakespearean translator, and a summary of lessons with action plans.
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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.