The JewelMusic Engineering Blog

Transform theoretical knowledge into production-ready code. Build, evaluate, and deploy a complete music source separation system using modern tools and best practices.

Demystify the mathematical foundations powering modern audio separation. From Fourier transforms to complex phase relationships, discover how mathematical elegance enables AI to understand music.

Trace the remarkable journey of neural architectures in audio separation, from computer vision's U-Net to NLP's Transformers. Discover how cross-domain innovations revolutionized music AI.

Bridge the gap between research and production with ONNX Runtime. Learn how to convert, optimize, and deploy Spleeter and Demucs models for real-world applications with up to 2x performance gains.

A comprehensive technical comparison between two titans of music source separation. Explore their architectural differences, performance trade-offs, and discover when to use each approach in production.

Explore the journey from the 1957 Illiac Suite to modern Transformers and Diffusion models, understanding how each paradigm shift has revolutionized music generation.

A deep technical dive into the architectural choices of leading music generation models, comparing autoregressive transformers, latent diffusion, and hierarchical approaches.

Analyzing the ecosystem split between open-source models like MusicGen and proprietary systems like Suno, including legal implications and training data controversies.

A practical guide to fine-tuning MusicGen with LoRA, plus exploration of emerging trends in granular control, long-form composition, and real-time interactivity.

Explore the journey from deterministic rule-based systems to sophisticated deep learning models in automated lyric creation, tracing the path from CFGs to Transformers.

Deep dive into cutting-edge models like LYRA, SongMASS, and SongComposer that synchronize lyrics with melodies, solving the data scarcity challenge.

Understanding prosody, phoneme mapping, and the technical challenges of creating truly singable AI-generated lyrics with perfect audio-visual synchronization.

Practical guide to fine-tuning LLMs, leveraging open-source models on Hugging Face, and implementing music-conditioned generation with real code examples.

Explore the 70-year journey from algorithmic composition to modern AI copilots, tracing the technological breakthroughs that revolutionized music creation.

Deep dive into the technical foundations of AI music generation, from MIDI representation to cutting-edge diffusion models and neural audio codecs.

A comprehensive comparison of open-source models like MusicGen and commercial platforms like Suno, analyzing their architectures, capabilities, and legal implications.

Practical guide to training your own AI music models, from LoRA fine-tuning to integrating AI copilots into professional workflows.

Deep dive into MSS technology, from early NMF approaches to state-of-the-art waveform models like Demucs. Compare SDR benchmarks and architectural innovations.

Explore AMT challenges from polyphonic transcription to the annotation bottleneck. Learn about Google's Onset and Frames model and modern deep learning approaches.

Complete guide to integrating MSS, AMT, and generation models into a cohesive music copilot system. Covers Hugging Face models, datasets, and training pipelines.

Examining copyright lawsuits against Suno and Udio, training data controversies, cultural appropriation risks, and the evolving definition of musical authorship.

Explore the mathematical foundations of digital music - from the Nyquist-Shannon sampling theorem to the Short-Time Fourier Transform. Learn how continuous sound waves become discrete digital signals.

Deep dive into Independent Component Analysis (ICA) and Non-Negative Matrix Factorization (NMF) - powerful techniques for blind source separation, feature extraction, and understanding the latent structure of music.

Master the probabilistic frameworks that power chord recognition, beat tracking, and musical structure analysis. Learn how HMMs capture temporal dependencies and GMMs model complex distributions in music.

Explore the neural revolution in MIR: from convolutional networks processing spectrograms to attention mechanisms understanding musical structure. Learn how modern architectures are transforming music analysis.

From noise reduction algorithms to spatial audio rendering, discover how mathematical MIR techniques power the audio products we use daily. Learn about commercial implementations and emerging market opportunities.

Explore the bifurcation between transformer-based generative models and ethical AI platforms, analyzing how architectural choices define market positioning and creative capabilities.

Deep dive into cloud-based DSP platforms like LALAL.AI, Adobe Podcast, and Descript. Learn how neural networks are democratizing professional audio processing through API-first architectures.

Explore the three-tiered performance stack enabling professional music production in browsers. From Web Audio API to WebAssembly and Audio Worklets, understand how BandLab and Soundation achieve native performance.

Analyze the fundamental divide between platforms using licensed content versus those trained on vast datasets. Explore legal battles, market implications, and the future of AI music ownership.

Comprehensive analysis of how audio AI companies build developer platforms. From ElevenLabs' comprehensive SDKs to Descript's open-source contributions, understand the strategic value of developer ecosystems.

Explore how distinct lines between music generation and audio processing are dissolving. Learn about hybrid platforms offering integrated end-to-end workflows that combine AI composition, DSP enhancement, and collaborative production.

Deep dive into architectural decisions that determine platform scalability. Compare BandLab's sophisticated microservices with Soundraw's monolithic approach, understanding how architecture shapes innovation velocity.

Explore the complex technical challenges of enabling real-time multi-user music production. From WebRTC implementations to CRDTs, understand how platforms solve distributed systems problems.

From ElevenLabs' API-first approach to Suno's controversial free tier, analyze the diverse business models emerging in AI audio. Understand unit economics, pricing strategies, and sustainable growth paths.

Discover how WebAssembly, model compression, and browser capabilities are enabling sophisticated audio AI to run locally. Learn about zero-latency processing, complete privacy, and offline functionality.

Explore FFmpeg's modular architecture, from libavutil's foundation to libavcodec's codec implementations. Understand how the six core libraries work together to power multimedia processing.

Deep dive into the mathematical foundations of modern video codecs. From DCT transforms to motion estimation, understand how complex algorithms achieve 1000:1 compression ratios.

Explore how human auditory perception drives audio codec design. From masking phenomena to MDCT transforms, understand the psychoacoustic models powering modern audio compression.

Learn how FFmpeg runs natively in browsers through WebAssembly. From Emscripten compilation to Web Workers integration, understand the architecture enabling client-side media processing.

Master the complexities of multimedia containers and streaming protocols. From ISOBMFF structure to adaptive bitrate streaming, understand how modern media delivery works.