Signal to the Underground

Category: ai

Reading Time: 7 min read

Tags: Music, AI, Creative, Production

Signal to the Underground is not just another electronic music track – it's a bold experiment at the intersection of human creativity and artificial intelligence. This behind-the-scenes exposé takes you into the studio (and into the code) where an AI became a collaborator in music production. I'll explore how AI tools were used to generate beats and melodies, how the producer guided and edited the AI's output, and how the synergy between man and machine gave birth to a unique sonic experience. The journey of creating Signal to the Underground offers a glimpse of the future of music production, where algorithms jam alongside artists.

Concept and Inspiration

The idea for Signal to the Underground emerged from a simple question: What if an AI could capture the gritty vibe of underground techno and help produce a track conveying that atmosphere? The producer, an avid fan of 90s rave and modern industrial techno, sought to channel "warehouse energy" – heavy bass, hypnotic rhythms, coded messages in sound – and saw AI as a new kind of instrument to experiment with those themes. The title itself hints at a dual meaning: a literal signal (audio waveform) to the underground club scene, and a metaphorical nod to a coded message beneath the surface (perhaps AI's "voice" coming through music).

AI Tools and Setup

Several AI-driven tools were enlisted as co-creators:

Generative AI for Melodies

I used Google Magenta's MusicVAE and OpenAI's MuseNet/Jukebox models to generate raw musical ideas. Specifically, MusicVAE was fed a prompt of a simple minor-key arpeggio (played by the human producer) and asked to interpolate and generate variations. The aim was to get a palette of haunting melodic riffs reminiscent of early 2000s trance leads, but with a dark twist. The output MIDI clips had surprising twists – some boring or off-key, but a few were gems that wouldn't have been conceived normally.

AI Drum Pattern Generator

Rhythm is king in underground music. I tried an AI prototype that uses a neural network trained on thousands of techno drum loops (per the tool's documentation). By inputting desired intensity and swing parameters, it generated drum patterns (kick, hi-hat, snare arrangements). One of the main beats in Signal to the Underground actually came from this generator – it nailed a driving ≈130 BPM four-on-the-floor kick (typical for underground techno) with offbeat hi-hats and a syncopated percussion line that added groove.

Style Transfer for Sound Design

I experimented with an AI audio style transfer technique: take an existing simple synth pad sound and "style transfer" it with characteristics of an industrial noise texture. The result was an evolving pad sound that had melodic structure but textural grit (like a pad made of rusty metal). This technique was inspired by research on neural style transfer that does style transfer on images, applied to audio timbres.

AI-assisted Mixing/Mastering

I leveraged LANDR, an AI mastering service, to do a quick master of demo versions. It uses AI to apply EQ, compression, and limiting tailored to the track. While final mastering was eventually done by a human engineer (for fine control), LANDR provided a solid reference to understand how the track could sound polished, and its suggestions guided some mix tweaks (e.g., it identified a slight bass heaviness and reduced it).

The studio setup combined these AI tools (running on a PC with a good GPU for faster neural net processing) with a traditional DAW (Ableton Live). The workflow was iterative: generate material with AI, import into DAW, evaluate and tweak, then perhaps feed some edited material back into the AI for further variation.

The Creative Process: Human + AI Collaboration

This wasn't a case of "press a button, get a finished song." Rather, it was a feedback loop between human and AI:

1. Seeding the AI

The producer started with a mood board – a selection of reference tracks (for vibe), a scale/key choice (A minor), and some basic beats. Some of these were fed into AI models. For instance, short MIDI sequences were input to MusicVAE to let it elaborate. Think of it as giving the AI a motif and asking "Can you riff on this theme?".

2. AI Generation and Curation

The AI outputs were then auditioned. The cheats here: the producer's trained ear and vision. Not all AI generations were good. But some had spark – a weird syncopation, an unexpected chord that added color. For example, one AI melody line introduced a ♭5 passing tone which gave a haunting feel; it was a "wrong" note that felt right in context, something a human might avoid consciously but that gave the melody character. These happy accidents are where AI shines as a creative partner – it doesn't follow the usual rules, so it can propose the novel.

3. Editing and Arranging

The chosen AI-generated elements were then edited. The producer might truncate a melody, change a few notes that clashed, or adjust velocity (loudness of notes) to make it groove better. The AI drum pattern was layered with some human-programmed variations (fills, additional percussion hits) to give it more human-like flow. Arrangement – deciding how the track progresses over time – was done by the human, as AI isn't great at large-scale structure yet.

4. Sound Design and Effects

This is where human producers still held the reins firmly. Using synthesizers, the producer turned the AI-generated MIDI melodies into actual sounds. For Signal to the Underground, an analog-style synth plugin was used for the main bassline (with a rolling filter movement). The AI-melody was split across two contrasting sounds: a sharp acid 303-like synth for the higher notes and a distorted reese bass for lower notes. Then effects like reverb and delay were applied (sometimes automated to swell on transitions). One cool trick: an AI algorithm was used to autonomously modulate a filter cutoff in sync with the beat – effectively an AI LFO that learned the track's energy pattern and modulated accordingly, creating subtle build-ups that responded to the music.

5. AI as an Assistant, not the Architect

Throughout, the producer kept a clear vision. The AI offered ideas – a collaborator that would propose many riffs and loops tirelessly. But it was ultimately the human's taste shaping the track. As Holly Herndon (an artist known for AI music) has shown, AI can generate unique vocal or harmonic elements as part of the palette, but the human artist curates and integrates them. Here, AI did not decide the track's theme, length, or final mix – those artistic decisions remained human. Instead, AI was like a very imaginative session player who is sometimes brilliant, sometimes off-key, always without ego and available 24/7.

Challenges Faced

Working with AI isn't plug-and-play magic; we hit several challenges:

Quality Control

For every great 4-bar loop AI gave, there were roughly 10 nonsensical ones in my tests. I had to sift through a lot of output. It's akin to a photographer taking 100 shots to get 5 good ones. Patience and an understanding of the AI's quirks were necessary. At times it felt like guiding an eager but clumsy apprentice.

Technical Constraints

Generating audio with something like OpenAI's Jukebox (which can produce raw audio in a certain style) was very computationally heavy and time-consuming. I attempted it for generating a few seconds of "vinyl crackle with hidden morse code" effect – a neat idea to embed a coded signal in the track – but getting Jukebox to output precise messages in audio proved infeasible. I fell back to a simpler solution: generate a Morse code pattern ourselves and blend it subtly into the hi-hats (an easter egg for the keen listener).

Maintaining Human Feel

Pure AI-generated stuff can sometimes sound too mechanical or perfectly grid-aligned. To counter this, we intentionally humanized elements. For instance, after using AI for drum pattern, we added slight timing swings and random velocity changes. The goal was to avoid the track feeling like it was stamped out by a machine devoid of groove. Interestingly, some AI tools like IBM Watson Beat aim to inject "human feel" into music. I manually did that via DAW editing.

Overfitting to Influence

One worry was that AI might generate something too similar to existing music (since it's trained on existing songs). I had to ensure we weren't inadvertently plagiarizing via the AI. All melodies were double-checked to not identically match known tunes (we even ran portions through a melody search tool to be safe). Everything cleared – the melodies were original to the best of my knowledge, albeit in a familiar style.

The Final Track

Signal to the Underground starts with a distant droning pad (the style-transferred sound mentioned earlier) and a faint code-like sequence of beeps (the Morse-like hi-hat pattern). As the beat kicks in, you immediately feel that underground techno energy – a four-on-floor thump with layered percussion that was co-created with AI. A bassline enters, pulsing on off-beats, giving that rolling sensation.

Midway, a melodic motif emerges – one of the AI-crafted melodies – a sequence that feels both nostalgic and alien. It swells and filters up, creating tension. Listeners have described it as "a machine trying to speak emotionally." That is exactly the vibe we aimed for: the AI's voice coming through as a signal coded in music. In the break, this melody is front and center, with heavy reverb, almost like a lone call in a subway tunnel. Then the drop slams back with all elements, now accompanied by a second AI-generated pattern: a syncopated metallic clang rhythm (we got this by using AI to generate percussive hits from non-musical samples – a fun experiment where the AI tried to mimic percussion using sounds like doors slamming and coins dropping, yielding a uniquely textured clang).

AI's Creative Contribution Highlights

- The eerie lead melody in the break – AI-generated suggestion refined by human
- The main drum groove backbone – AI-suggested pattern enhanced by human
- Several background ambient noises – we had an AI model trained on environmental sounds generate some "underground station" noises which we mixed in subtly for atmosphere (e.g., a distant train-like rumble every 16 bars)
- Dynamic arrangement ideas – Interestingly, analyzing the AI outputs influenced arrangement. The AI often output sequences that would crescendo and then abruptly stop. That inspired me to do an unconventional arrangement: instead of a standard outro, the track builds to a climactic point and then suddenly cuts to a final 4 bars of just the Morse beeps and pad – as if the signal faded out unexpectedly. Early listeners found that quite striking.

The Human Touch

Even with AI deeply involved, the human touch was indispensable in:

Emotion and Storytelling

I imbued the track with an emotional arc – something AI doesn't inherently understand. Decisions like "add a subtle major chord in the harmony at the peak to give a bittersweet feel" or "pause the drums here for a breath before the finale" were human calls to ensure the track had a narrative.

Polish and Cohesion

Ensuring all elements felt like they belonged to the same sonic world required tweaking sound parameters and effects that AI didn't manage. A lot of mixing – balancing levels, EQ carving to make space for each part – was manual. The final master by a human gave it the glue and punch for club play, albeit using AI mastering as a reference earlier helped speed this up.

Reflection: Collaboration Between Artist and AI

The production of Signal to the Underground exemplified what many in the industry are finding: AI is a creative partner, not a replacement. It brings a new "band member" into the studio with its own style. As a fellow producer told me privately, "Music producers increasingly view AI as a collaborator, an entity that brings a new dimension to their creative arsenal." During this project, there were moments of genuine surprise and delight at what the AI suggested – ideas that took the track in directions we wouldn't have considered alone. It's akin to jamming with someone who has listened to all music ever made and can spew out riffs in any style, but needs guidance to know which riff fits the vibe.

This raises questions about authorship – is the AI a co-author of the song? From a legal standpoint, currently not (AI output in many jurisdictions can't hold copyright, and we significantly transformed its outputs). Creatively, we'd say the AI was like an instrument that can improvise. I used it like one uses a synthesizer – you play something, it responds with sound – except here you "play" a conceptual prompt and it responds with musical ideas. The end product is undeniably shaped by the human artist's vision, taste, and editing. In fact, one could argue the human role becomes even more curatorial and editorial when using AI: you must discern what in the buffet of AI outputs has artistic merit and then elevate those pieces while discarding the rest.

Conclusion: The Future Sound

Signal to the Underground stands as a testament to the exciting possibilities when AI enters the creative process. The track doesn't sound "AI-made" in any gimmicky sense; it sounds like a rich, textured electronic piece – but behind it, an AI quietly contributed to many elements. For listeners on the dance floor, it's a banging tune with an eerie vibe. For me in the studio, it was a learning experience about how far we can push AI as a creative tool.

As AI technology continues to advance, we anticipate tools will get better at understanding musical context, perhaps even adjusting their outputs based on real-time feedback (e.g., "make it more intense now" commands). One day, AI might handle more of the arrangement or mixing autonomously – though the question of whether that's desirable remains. For now, the equilibrium seems to be: AI expands the palette and can handle tedious tasks (like endless sound design trial-and-error or mastering tweaks), allowing human musicians to focus more on ideation and emotional expression.

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All numbers are illustrative, based on the author's personal studio tests (June 2025).