How to Make AI Music (Right Now) – A Practical Guide — LiliDi Blog
Explore how to make AI music in 2024. This guide covers practical tools, techniques, and realistic expectations for generating music with artificial intelligen…
By lilidi editorial
How to Make AI Music (Right Now) – A Practical Guide The buzz around AI generated music is undeniable. From viral jingles to intriguing soundscapes, artificial intelligence is democratizing music creation in ways few imagined a decade ago. But how do you actually make AI music? Is it as simple as typing a prompt, or are there nuances? This guide cuts through the hype to give you a pragmatic, actionable overview of generating music with AI. We will explore current methods, practical tools, and set realistic expectations for anyone looking to experiment with AI in their musical endeavors. Forget the sensational headlines; let us focus on what you can actually achieve today. Understanding the Landscape of AI Music Generation Before diving into specific tools, it is crucial to grasp the different ways AI can "make" music. It is not a monolithic process. Generally, AI music generation falls
into a few key categories: Algorithmic Composition: This involves AI systems learning musical patterns, structures, and theories to compose original pieces. Think of it as AI understanding music theory and then applying it. Style Transfer/Inpainting: Here, AI takes existing audio and modifies it to a new style (e.g., turning a classical piano piece into a jazz rendition) or fills in missing parts of a recording. Text to Music Generation: The most user friendly approach, where you provide a text prompt (e.g., "upbeat synthwave track with a driving beat") and the AI generates audio based on that description. This is often what people envision when they ask "how to make AI music." Melody/Rhythm Generation from User Input: Some tools allow you to hum a melody, tap a rhythm, or provide basic notation, and the AI will expand upon it, harmonize it, or generate accompanying tracks. Each method
has its strengths and limitations, and understanding these will help you choose the right tool for your specific goal. Practical Tools and Platforms for AI Music Creation The market for AI music tools is rapidly evolving. Here are some categories and specific examples that demonstrate the current state of the art. 1. Text to Music Generators These platforms are the easiest entry point for most users. You describe the music you want, and the AI attempts to create it. Success often depends on the specificity and clarity of your prompt. Example: Stability AI (Stable Audio): A powerful tool that generates high quality audio from text descriptions. It is excellent for creating specific sound effects, short musical loops, or instrumental tracks. You can specify genre, mood, instrumentation, and even sound characteristics like "reverb" or "delay." The generated audio is often impressive, though
generating full, complex songs with a coherent narrative remains challenging. How you use it: Log in, enter your text prompt (e.g., "a melancholic piano piece with a subtle orchestral swell, slow tempo, cinematic atmosphere"), and generate. You can often adjust parameters like length. Example: Google (MusicLM limited access): While not widely available for direct user generation currently, MusicLM showcased incredible capabilities in generating complex musical pieces from very detailed prompts, even incorporating concepts like "a space themed arcade game music." Keep an eye on advancements from major players like Google. 2. AI Assisted Digital Audio Workstations (DAWs) and Plugins These tools integrate AI capabilities directly into your existing music production workflow, often assisting with composition, mixing, or mastering. Example: AIVA (Artificial Intelligence Virtual Artist): This
platform allows you to generate music in various styles and then provides controls to customize the composition. You can select themes, moods, and even influence parameters like instrumentation and melodic complexity. It is more sophisticated for full track generation than simple text to music tools, offering a deeper level of creative control. How you use it: Choose a genre or mood, let AIVA generate a basic track, and then use its editing interface to modify themes, add variations, or export different stems. Example: Amper Music (Acquired by Shutterstock): Similar to AIVA, Amper Studio enables users to create custom music by selecting parameters like mood, tempo, and instrumentation. It focuses on providing royalty free music for creators, making it useful for content creators who need custom scores without complex licensing. AI Mastering Tools (e.g., iZotope Ozone, LANDR): While not
generating music from scratch, these AI powered tools analyze your mixed tracks and apply intelligent mastering processes to achieve a professional sound. They "listen" to your music and make educated adjustments to EQ, compression, and limiting. 3. Generative Music Apps and Websites These are often simpler, more experimental tools designed for quick generation or creative exploration. Example: Mubert: Offers a unique approach by generating royalty free music tailored to specific moods, activities, or durations. You select keywords or tags (e.g., "workout," "focus," "sleep," "meditation") and it streams endlessly evolving music. It is less about creating a specific track and more about generating an ambient soundscape for a particular scenario. How you use it: Select your desired mood or activity, and Mubert generates continuous, unique audio streams. Setting Realistic Expectations for
AI Music This is perhaps the most critical section. While AI is powerful, it is not a magic wand. Understanding its limitations prevents frustration. Consistency and Narrative: Generating a full length song with a coherent structure, emotional arc, and lyrical narrative (if applicable) using purely AI is still a significant challenge. AI excels at short loops, atmospheric pieces, or generating variations on a theme. Artistic Intent: AI does not "understand" human emotion or artistic intent in the same way a human composer does. It processes patterns. While it can mimic emotional sounds, the underlying creative spark is still human driven. Quality vs. Customization: Tools that prioritize ease of use (like simple text to music) often trade off deep customization. More control usually means a steeper learning curve or integration into a DAW. The "AI Sound": Many AI generated tracks,
especially from less sophisticated models, can sound somewhat generic or "algorithmic." Developing prompts and refining outputs to overcome this requires practice and a discerning ear. Tips for Maximizing Your AI Music Generation Experience To get the best results when you make AI music, consider these tips: Be Specific in Your Prompts: Instead of "cool music," try "an energetic electronic track with a driving bassline, retro arcade synth melodies, and a fast tempo." Use descriptive adjectives for mood, genre, instrumentation, and even specific effects. Iterate and Refine: The first output is rarely perfect. Experiment with different prompts, adjust parameters, and generate multiple versions. Think of it as sculpting. Combine AI with Human Creativity: AI is a powerful assistant, not a replacement. Use AI to generate initial ideas, loops, or textures, and then bring them into a DAW to
arrange, mix, and add human elements like vocals or live instrumentation. Understand Copyright and Licensing: Before using AI generated music commercially, always check the platform