Muck Rack is an AI communications platform that helps PR teams monitor coverage, find journalists, manage outreach, and report on earned media impact. The platform gives teams visibility across news, social platforms, industry voices, podcasts, newsletters, broadcast, print, and generative AI models. Muck Rack helps communications teams understand what is happening, why it matters, and how to respond with targeted media relations workflows. Its media database allows users to identify journalists, creators, outlets, and influencers shaping public conversations and AI-generated answers. Generative Pulse helps brands track visibility across AI platforms and uncover the sources influencing AI responses. Built for PR teams, brands, agencies, journalists, and media professionals, Muck Rack helps improve monitoring, pitching, reporting, and PR measurement.
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LALAL.AI is a next-generation audio separation service powered by advanced AI technology. With a suite of innovative tools - Stem Splitter, Voice Cleaner, Voice Changer, Voice Cloner, VST Plugin, LALAL.AI enables users to take their audio content to the next level.
Stem Splitter
The core service of LALAL.AI allows users to extract individual vocals or instruments from audio tracks. Supported instruments include: drums, bass, piano, guitar (electric and acoustic), synthesizer, and string and wind instruments
Voice Cleaner
A powerful tool for extracting clean, clear vocals
Voice Changer
Modify the sound of a person's voice
Voice Cloner
Create custom voices
Echo & Reverb Remover
Remove unwanted echo and reverb from vocals, voice recordings, songs, and videos, all in popular audio and video formats
Lead & Back Vocal Splitter
Use state-of-the-art AI technology to precisely separate lead and backing vocal
VST Plugin
Extract stems inside your favorite DAW
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Seed-Music
Seed-Music is a unified framework for high-quality and controlled music generation and editing, capable of producing vocal and instrumental works from multimodal inputs such as lyrics, style descriptions, sheet music, audio references, or voice prompts, and of supporting post-production editing of existing tracks by allowing direct modification of melodies, timbres, lyrics, or instruments. It combines autoregressive language modeling with diffusion approaches and a three-stage pipeline comprising representation learning (which encodes raw audio into intermediate representations, including audio tokens, symbolic music tokens, and vocoder latents), generation (which transforms these multimodal inputs into music representations), and rendering (which converts those representations into high-fidelity audio). The system supports lead-sheet to song conversion, singing synthesis, voice conversion, audio continuation, style transfer, and fine-grained control over music structure.
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