AudioLyrics
AI-Driven Lyric Extraction and Time-Synced LRC Generation for Modern Music Distribution.
AI-driven spatial reconstruction for transforming legacy mono recordings into immersive stereo soundscapes.
MonoToStereo AI represents a leap in digital signal processing (DSP) by utilizing deep learning architectures, specifically U-Net and Transformer-based models, to perform intelligent spatial reconstruction. Unlike traditional phase-shifting or delay-based stereo widening, this technology analyzes the spectral content of a mono source to identify individual acoustic components and reposition them within a virtual stereo field. It effectively simulates the 'missing' channel by predicting harmonic relationships and environmental reflections that would exist in a true stereo recording. In the 2026 market, it stands as a critical tool for archival restoration, podcasters using single-microphone setups, and music producers looking to add width to vintage stems. The tool’s architecture is optimized for phase coherence, ensuring that the resulting stereo file remains fully collapsible to mono without the comb-filtering artifacts common in legacy widening techniques. By leveraging cloud-based GPU inference, MonoToStereo AI provides professional-grade spatialization that was previously only achievable through manual, multi-day audio engineering workflows.
Uses a convolutional neural network to ensure the L/R channels maintain perfect timing to prevent phase cancellation.
AI-Driven Lyric Extraction and Time-Synced LRC Generation for Modern Music Distribution.
Professional-grade WebAssembly-powered audio optimization and lossless compression.
Professional AI-powered audio finishing for instant, release-ready tracks.
A high-precision, browser-based audio workstation for instant trimming, fading, and format conversion.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Separates frequencies into virtual stems and pans them based on psychoacoustic profiles.
Synthesizes missing spatial reflections based on the original recording's room tone.
Parallel processing of multiple files via AWS/GCP GPU instances.
Real-time visualizer showing the transformation of the audio's stereo width (Lissajous curve).
Allows users to upload reference tracks to 'teach' the AI a specific spatial character.
Ensures the signal chain remains at 32-bit float throughout the AI transformation.
A guest was recorded on a mono smartphone mic, making it sound thin compared to the host's studio mic.
Registry Updated:2/7/2026
Restoring a 1950s mono recording for modern streaming platforms.
Mono nature recordings lack the immersion needed for a VR project.