WavNav
Visual sample discovery for huge audio libraries
What it does
WavNav helps you explore large sample folders by mapping sounds into a visual space so similar samples sit near each other. You can filter by key/BPM/text, preview quickly, and use audio-based similarity search by dropping in a sound. Built for producers and sound designers on macOS and Windows.
Does the same job
all alternatives →- ITI trained an AI model on 120M+ songs from iTunes2023 · maroofy.com · ▲753
Hey HN! I just shipped a project I’ve been working on called Maroofy: https://maroofy.com You can search for any song, and it’ll use the song’s audio to find other similar-sounding music. Demo: https://twitter.com/subby_tech/status/1621293770779287554 How does it work? I’ve indexed ~120M+ songs from the iTunes catalog with a custom AI audio model that I built for understanding music. My model analyzes raw music audio as input and produces embedding vectors as output. I then store the embedding vectors for all songs into a vector database, and use semantic…
- MAMusic Audio Search Engine Using OpenAI's Embeddings on GPT Descriptions2023 · muzic-sage.vercel.app · ▲92
I am excited to announce a new tool for music producers and audio enthusiasts - a music audio search engine. With just a simple description of the groove you're looking for, our semantic search engine will output the most similar audio in seconds. I used the Freesound.org API to upload over 3,000 grooves to MongoDB, and combined all the relevant data such as tags, title, description, BPM, etc. into OpenAI's Text-Davinci to generate a unique description of each sound. I then embedded these descriptions using the Ada Embeddings Model and inserted them into Pinecone DB vector database, making…
- IVI've built a spectrogram analyzer web app2023 · webfft.net · ▲244
- HAHiFiScan, a Python app to optimize your loudspeakers2022 · github.com · ▲260
- OSOssia score – A sequencer for audio-visual artists2025 · github.com · ▲95
- FAFree AI-based music demixing in the browser2023 · sevag.xyz · ▲190
Hi all, I've spent some time working on music demixing or music source separation algorithms, which take in a mixed song and output estimates of isolated components (e.g. vocals, drums, bass, other). I took a popular PyTorch model with good performance (Open-Unmix, UMX-L weights), reimplemented the inference steps in C++, and compiled it to WebAssembly for a free client-side music demixer.
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
