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Key takeaways
- Despite significant advances, AI still cannot replicate the deep emotional and cultural aspects of musical composition, as professionals have noted, but it can contribute to deepening our understanding of emotional engagement with music.
We think AI music tools are just gimmicks for social media creators, or that they’re limited to basic beats. But when companies like Google, Meta and Stability AI are pouring resources into generative audio models that can generate complete compositions in seconds, it’s hard to turn them down.
As a pianist and tech founder, I’ve experienced a wide range of music AI tools.
Listen and video as a generator
If you want to create a song from scratch, Suno and YouTube are the two leading platforms at the moment. Both use text prompts to create tracks complete with vocals, instruments and production. Type in “upbeat ’80s synth-pop about summer in Paris” and you’ll get a polished two-minute track in seconds.
Snoo excels in catchy, radio-friendly textures. Udio creates more granular arrangements. In my experience, Snow works best for quick prototyping, while Udio provides better results when you need layered instruments. Both offer free tiers and paid plans for commercial use.
AIVA as Composer
AIVA (Artificial Intelligence Virtual Artist) specializes in orchestral and cinematic music. The platform lets you choose a style such as epic trailer, emotive piano or electronic ambient, then produces a royalty-free composition. Film editors and game developers use it to score projects without hiring a composer.
Soundraw as custom
For creators who need more control, Soundraw offers granular customization. You can adjust the tempo, energy level and instruments after the race. These tools are popular with YouTubers and podcasters who need background music that fits a specific mood without licensing headaches.
Moises and LALAL.AI as separators
AI isn’t just generating music. It is deconstructing it. Moises and LALAL.AI use machine learning to isolate vocals, drums, bass and other sounds from any track. Musicians use these tools to practice with isolated parts, create remixes or create vocals for karaoke. Accuracy has improved dramatically over the past two years.
Static as a voice cloner
Want to sing a cover with your voice? The best workflow combines the two tools. First, use LALAL.AI to isolate and clean up your voice recording, remove background noise and separate your voice from any instrument. Then upload the clean vocal to Jamable, which lets you apply AI voice models to transform your vocals while preserving the original emotion and timing. Musicians use this combination to create demos, test how their sound would sound in different ways, or create covers without the complexity of studio post-production.
The symphony question
Can AI create a symphony? I put this question to Aris Durio, principal solo flute of the Orchester de l’Opéra National de Paris. His answer was unequivocal: not today. I’ve seen the world’s greatest orchestral performers struggle to adapt to AI, whether it’s to create new works or reproduce existing ones. Time seems to run out. The sentence lacks breath. The subtle push and pull between musicians that enlivens a live performance doesn’t translate.
A human symphony comes from lived experience. A musician’s relationship with time, silence, tension, and resolution is shaped by personal, cultural, and emotional history. AI optimizes probabilities from existing data. It is not felt what prompted Beethoven to turn his inner struggle or philosophical conviction into musical form. AI can imitate texture, harmony and stylistic patterns, but it doesn’t decide to communicate something to the world. It takes no conscious creative risks. It is not known why a note should last a fraction of a second longer to produce the correct effect. It is this intention, connected to human experience, that distinguishes a symphony from an organized arrangement of sounds.
AI in concert hall
AI may struggle to replace music, but it can help us analyze our experience of it. Imagine wearing a biometric device during a live symphony, tracking heart rate, skin conductance and breathing patterns. The AI can then identify the exact moments that moved you the most, by associating musical passages with physical responses.
Scientists have shown that physiological responses (heart rate, skin conductance, breathing) can be measured when people listen to music and analyzed in terms of musical composition or emotional experience. For example, a study by Anna M. Czepiel measured heart rate synchrony in audience members and correlated physiological changes to salient events in concert music, showing that dynamic biometric patterns are related to attention and engagement with musical compositions.
This scientific approach could change how we understand emotional engagement with music, helping musicians, conductors and venue design experiences that resonate more deeply. The future of AI may not be created in classical music. This may be a revelation.
Key takeaways
- Despite significant advances, AI still cannot replicate the deep emotional and cultural aspects of musical composition, as professionals have noted, but it can contribute to deepening our understanding of emotional engagement with music.
We think AI music tools are just gimmicks for social media creators, or that they’re limited to basic beats. But when companies like Google, Meta and Stability AI are pouring resources into generative audio models that can generate complete compositions in seconds, it’s hard to turn them down.
As a pianist and tech founder, I’ve experienced a wide range of music AI tools.
