Kliga

Introduction to Kliga

Kliga is a privacy-first, web-based toolkit designed for creators, audio engineers, producers, and music professionals who require fast, reliable media processing capabilities. It addresses two growing industry challenges: the increasing volume of AI-generated music on streaming platforms—which poses transparency and revenue concerns for human artists—and the need for accessible, high-quality audio and video post-production tools without local software installation.

The platform operates entirely in the browser, with no client-side data retention or transmission beyond what is necessary to process user-submitted media. Its interface prioritizes usability while delivering studio-grade functionality across mastering, analysis, editing, and conversion tasks.

Key Takeaways

• Detects AI-generated audio with 99.9% reported accuracy, supporting analysis via file upload, URL (including Spotify and YouTube), or direct streaming integration
• Provides one-click audio mastering using 12 studio-curated presets, with live A/B comparison and real-time preview
• Offers precise audio editing capabilities including cutting, merging, and format conversion (MP3, WAV, FLAC, etc.)
• Includes background noise removal with volume enhancement, preserving audio clarity without compromising privacy
• Supports screen recording, clip trimming, compilation, video compression (up to 90% size reduction), and format conversion—all processed client-side or in secure cloud environments
• All tools emphasize privacy: sensitive operations such as noise removal and video conversion are described as occurring locally or with data never stored on servers
• Versioned tool releases (e.g., v2.0) indicate iterative improvements to underlying models and interfaces

How Kliga Works

Kliga functions as a suite of independent, web-accessible tools—each with its own dedicated endpoint (e.g., /mastering, /ai-music-detector). Users interact with each tool through a streamlined interface that accepts input (audio/video files, URLs, or recorded media), applies domain-specific processing (e.g., machine learning inference for AI detection, digital signal processing for mastering), and returns results without requiring downloads or installations.

The AI Song Detector employs trained models attuned to artifacts common across multiple AI music generation frameworks. Input sources include uploaded audio files, public URLs from Spotify or YouTube (via supported extraction methods), or direct streaming links. Audio Mastering applies spectral shaping, dynamic range control, and loudness normalization based on genre- and use-case-optimized presets. Other tools like the Audio Editor and Background Noise Remover rely on Web Audio API–based or WASM-accelerated signal processing pipelines, enabling low-latency, client-side operation where feasible.

Core Benefits and Applications

Music professionals use Kliga to audit incoming submissions or catalog entries for AI origin, supporting rights management and licensing decisions. Independent podcasters apply one-click mastering to ensure consistent loudness and tonal balance across episodes before distribution. Content creators leverage screen recording and clip compilation for tutorial or review video production, then compress or convert outputs for platform-specific requirements (e.g., Instagram Reels, YouTube Shorts). Educators and journalists utilize AI detection to verify audio authenticity in reporting contexts. All tools support workflows where installing desktop software is impractical—such as shared workstations, temporary devices, or regulated environments with strict software policies.