In this episode of AI Music Unmuted: Real Talk by RealMusic.ai, we sit down with Anita Baumgartner, DJ and Head of Marketing at Soundraw, to explore how AI can expand creative possibilities, remove long-standing barriers for artists, and build new pathways for musicians to make and share their work.

Soundraw, founded by former hip-hop dancer Daigo, has grown into a widely used AI music platform designed for both artists and content creators. What makes it stand out is its focus on original, ethically trained music, flexible beat creation, and usability for musicians at any skill level. Anita shares how quickly skepticism turns into curiosity once artists try the tool, from rappers in New York building beats in minutes to rural creators finally able to produce full tracks without needing access to big-city studios.

The involvement of AI in the music industry is understandably one of the industry’s most heated topics to date. On many levels, it is justified. We do not disagree with much of the discourse. Beyond the sensationalized headlines about AI, tools are emerging to enhance real capabilities for artists of any skill level. Think back to over 40 years ago when the introduction, and subsequent backlash, of MIDI, sampling, and electronic music as a whole shook the music industry.

One of the most exciting and ethical applications of AI in music is the development of virtual musicians trained on the playing and actual styles of real artists. Instead of scraping data without permission, companies are beginning to collaborate directly with musicians, training models on their performances and compensating them for their contributions.

This spring, tech headlines buzzed with news of OpenAI and Anthropic unveiling advances in multi-agent collaboration, AI systems where multiple specialized models work together, each handling a different part of a complex task. While the demos focused on software engineering and research, the same orchestration approach is already starting to transform another world entirely: music creation.

The shift is subtle but profound. We’ve moved past the novelty of “AI writes a song” into a future where AI becomes a deeply personal assistant team, orchestrated, context-aware, and specialized, that supports every stage of a musician’s journey.

Lyric transcription sounds simple: play a song and get the words back in text form. In reality, it is one of the toughest challenges in music AI. Spoken language recognition is hard enough. Add pitch, melody, vibrato, harmonies, and a full mix of instruments, and the task becomes far more complex.

The AI behind lyric transcription combines digital signal processing (DSP) with machine learning (ML) to bridge the gap between raw sound and readable text.

AI assisted mixing tools promise to make your tracks sound “finished” with just a click. They can balance levels, EQ individual tracks, apply compression, and even set stereo width automatically. But what’s actually going on behind the scenes when you hand your mix over?

Traditionally, mixing decisions were either made by humans or guided by fixed, rule-based algorithms. For example, a traditional auto-EQ plugin might boost the highs in a vocal track if it detects dullness, or cut low-end rumble below 80Hz on a guitar. These rules are static. They don’t “learn” from your session, they just respond to predefined triggers.

Many musicians and producers have used a tool that instantly detects pitch, tempo, or key from an audio file. It seems almost magical to drag in a track, and within seconds you know it’s in B minor at 122 BPM. Let’s understand how this works.

Traditionally, software used digital signal processing (DSP) techniques like Fast Fourier Transforms (FFT), autocorrelation, and zero-crossing analysis to estimate frequency and timing. These methods work well in clean, isolated environments,  like analyzing a solo vocal or a metronome click,  but they break down fast with noisy, layered, or complex audio.

That’s where machine learning stepped in.

AI is already reshaping traditional education in meaningful ways. In classrooms and online platforms around the globe, it’s being used to personalize learning paths, adapt in real time to a student’s performance, and provide instant feedback on everything from math problems to grammar structure. For teachers, it’s becoming a way to scale support, track progress more efficiently, and free up time to focus on students who need more one-on-one help.

If you’ve heard anything about AI in music, it probably involves a viral headline, a cloned voice, or someone typing a few words and getting back a full track. That’s what most people think AI music is all about. They see the You Tube videos about text prompts to songs, how AI mixing and mastering is trash. It is still early and evolving, so why not focus on the penitential?

The conversation around AI in music is a mess.

Half the noise is hype:
“Make a hit in 30 seconds with this new tool!”
“AI is going to replace producers, singers, and songwriters!”

The other half is panic:
“Real music is dead.”
“AI will kill creativity.”

If you’ve heard anything about AI in music, it probably involves a viral headline, a cloned voice, or someone typing a few words and getting back a full track. That’s what most people think AI music is all about. They see the You Tube videos about text prompts to songs, how AI mixing and mastering is trash. It is still early and evolving, so why not focus on the penitential?

AI is quickly becoming part of the everyday toolkit in music. Whether you're a beginner working on your first songs or a pro mixing a client’s album, chances are you’ve seen how AI can help speed things up and offer more time for creativity. But for all the power these tools offer, there are still important things they can’t do. And understanding those limits is about keeping your creativity intact.

AI Audio Denoisers are advanced tools that use artificial intelligence (AI) to remove unwanted noise from music recordings, such as background hums, hisses, and other interference. These denoisers are designed to clean up audio tracks while preserving the clarity and integrity of the original music, making them indispensable in professional and home studios.

Artificial Intelligence, Machine Learning, and Deep Learning are reshaping digital music creation, each contributing distinct capabilities and approaches. Although these technologies often overlap, they have unique influences on how music is composed, produced, and experienced.