Coder Legion - Recent posts tagged music

AI in Music — The Revolution No One in the Industry Can Ignore

Tue, 01 Sep 2026 13:23:28 +0000

AI has moved from being a futuristic idea to something musicians, producers, labels, and listeners encounter in everyday music experiences. Whether it is generating a musical idea, cleaning vocals, recommending a new artist, or analyzing listener behavior, artificial intelligence is quietly changing how the industry works. The difficult question is no longer whether AI belongs in music. It is how deeply it will reshape creative work, business models, and the relationship between artists and their audiences, and whether the industry can adapt without sacrificing the human qualities that make music matter.

What Is AI Changing in the Music Industry?

AI is changing the music industry by automating technical tasks, supporting music creation, improving audio production, personalizing music discovery, and helping businesses make data-driven decisions.

Its influence can be seen across almost every stage of the music lifecycle:

Creation: AI can generate melodies, chord progressions, rhythms, lyrics, and musical concepts.
Production: AI can assist with audio cleanup, mixing, mastering, restoration, and stem separation.
Discovery: Recommendation systems help listeners find songs and artists they may enjoy.
Personalization: Streaming platforms use machine learning to tailor listening experiences.
Marketing: AI can identify audience patterns and content trends.
Analytics: Music businesses can analyze large datasets to understand listeners and catalog performance.

The most important change is that AI is becoming part of the workflow rather than remaining a separate technology experiment.

Why Is AI Becoming So Important in Music?

Music is both a creative industry and a data-heavy business.

Every song generates information. People stream, skip, save, share, search, playlist, and recommend music. Artists create large catalogs of audio and visual content. Labels manage rights and royalties. Streaming services process enormous amounts of listening activity.

AI is particularly useful when there is a combination of large amounts of data, repetitive processes, and complex patterns.

That makes music a natural environment for machine learning.

At the creative level, AI can provide ideas. At the production level, it can automate technical processes. At the business level, it can identify patterns across audiences and catalogs.

This broad usefulness explains why the technology is difficult for the industry to ignore.

How AI Is Changing Music Creation

AI-generated music receives the most attention because it directly challenges traditional ideas about composition.

AI systems can analyze musical patterns and produce new combinations of melodies, harmonies, rhythms, instruments, and arrangements. Depending on the tool, users can provide a text description, musical reference, or other input and receive a generated result.

For musicians, one practical use is overcoming creative blocks.

A songwriter might have an unfinished idea and use AI to explore possible directions. A producer could test different arrangements before choosing one to develop manually. A content creator may need a particular musical atmosphere and use AI-generated material as an initial concept.

The important distinction is between generation and artistic direction.

AI can produce possibilities, but a musician still has to decide whether those possibilities communicate the intended emotion or story.

That human selection process can be just as important as the initial generation.

AI Is Making Music Production Faster

Music production contains many repetitive technical tasks, and this is an area where AI can provide immediate practical value.

AI-powered tools can assist with background-noise removal, vocal isolation, audio restoration, automatic leveling, mastering, and other forms of audio processing.

For example, separating vocals from an existing recording can be extremely time-consuming when done manually. AI-based source separation can provide a faster starting point.

This does not mean producers become unnecessary.

Production decisions often depend on context. A producer needs to understand how a sound fits into the arrangement, whether an artifact is acceptable, and how technical changes affect a track's emotional character.

AI can speed up parts of the process, but human listening and judgment remain essential.

How AI Is Changing Music Discovery

For listeners, one of AI’s biggest impacts may be something they barely notice: deciding what music appears next.

Streaming platforms can analyze listening behavior, including songs played, skipped, saved, searched for, or added to playlists. Machine learning systems use these signals to identify patterns and generate recommendations.

This helps solve a major problem created by digital music: there is too much of it.

A listener cannot realistically explore every new release manually. Recommendation algorithms narrow that enormous catalog into a smaller selection that is more likely to match individual preferences.

This can also benefit emerging artists.

If an artist’s music shares characteristics with tracks that a listener already enjoys, recommendation systems may help introduce that artist to a relevant audience.

But there is a trade-off. Algorithms optimized for engagement may repeatedly recommend familiar styles. That can make music discovery more convenient while reducing exposure to unexpected genres or unconventional artists.

AI Is Transforming Music Marketing

The music industry has always depended on understanding audiences, but AI can analyze audience behavior at a much larger scale.

Artists and music companies can examine information such as listener locations, engagement patterns, content performance, streaming behavior, and audience growth.

Instead of simply asking whether a song is performing well, teams can investigate more specific questions:

Where is the song gaining traction?
Which audience groups are responding?
Which content is driving engagement?
When are listeners most active?
Which markets show growing interest?
How are listeners discovering the artist?

AI can help identify patterns that might otherwise remain hidden in large datasets.

However, data should inform creative strategy rather than dictate it.

A song can perform unexpectedly well for reasons that are not immediately visible in the numbers. Human interpretation is still needed to turn analytics into meaningful decisions.

The Growing Role of AI in Music Technology

The increasing use of AI is also changing the digital products built around music.

Music platforms and technology teams can incorporate machine learning into recommendation systems, intelligent search, playlist creation, audio analysis, artist analytics, and personalized experiences.

For example, a specialized music application agency may use AI capabilities to develop a platform that analyzes audio, recommends content, organizes large music libraries, or provides personalized experiences.

The important question is not whether a product uses AI. The real question is whether technology improves the experience.

An AI recommendation system should make discovery more useful. An automated audio feature should reduce unnecessary work. An analytics system should make information easier to understand.

Adding AI without a clear user benefit can make a product more complicated rather than more useful.

What Are the Biggest Benefits of AI in Music?

AI offers several significant benefits to the music industry:

Faster workflows

Automation can reduce the time required for repetitive production and editing tasks.

Lower technical barriers

Some AI tools make advanced capabilities more accessible to independent creators.

More experimentation

Musicians can explore ideas and variations without manually building every possibility.

Better personalization

Listeners can receive recommendations based on their individual behavior.

More useful data analysis

Music businesses can process large amounts of audience and catalog information more efficiently.

New creative possibilities

AI can introduce approaches to composition, sound design, and interaction that were difficult or impractical with traditional tools.

The overall benefit is not simply speed. AI can expand the number of creative and business decisions people can explore.

What Are the Biggest Risks?

The same technology creating new opportunities is also producing difficult problems.

Artists and rights holders want to know which materials were used to train AI systems and whether creators should receive permission, attribution, or compensation when their work contributes to AI development.

Voice cloning is another major issue. AI can reproduce recognizable characteristics of a singer’s voice, creating legitimate creative possibilities but also opening the door to unauthorized use.

There are also questions around ownership.

If a person generates a musical composition with substantial AI assistance, determining the ownership and legal status of the resulting work may depend on the specific circumstances and applicable laws.

Then there is the question of authenticity.

Some listeners may enjoy AI-generated music, while others may value knowing that a song represents a human performance and creative experience.

The industry will have to navigate these differences rather than assuming every listener or artist wants the same thing.

Will AI Replace Musicians?

AI is unlikely to make human musicians irrelevant.

Music is not simply an arrangement of frequencies. It can represent memories, identity, emotion, culture, personal experiences, and social connection.

AI can generate patterns, but musicians provide intention.

A singer decides how a lyric should feel. A producer decides when a track should remain imperfect. A songwriter decides which personal experience belongs in a song.

Those decisions are difficult to automate.

Instead, AI is more likely to change what musicians do.

Artists may spend less time on certain technical tasks and more time on creative direction. Producers may use AI to accelerate workflows. Marketing teams may use AI to understand audiences. Listeners may rely on AI to navigate enormous music libraries.

The result could be a more collaborative relationship between humans and technology.

What Does the Future of AI in Music Look Like?

The next stage of AI in music will likely move beyond simple content generation.

AI could become more integrated into real-time production, adaptive soundtracks, personalized listening, intelligent music search, interactive performances, virtual instruments, and artist analytics.

The technology may also become less visible.

Instead of opening a separate AI tool, musicians may encounter intelligent features directly inside their existing production environments. Listeners may interact with recommendation systems without thinking about the machine learning behind them.

At the same time, legal and ethical standards will become increasingly important.

The industry will need clearer approaches to copyright, licensing, consent, attribution, compensation, and digital identity.

Conclusion

AI is not simply another piece of music software. It is changing how the industry approaches creation, production, discovery, marketing, and data analysis.

Its biggest impact may come from the combination of these capabilities. A musician can use AI to explore an idea, improve a recording, understand an audience, and reach listeners through increasingly personalized platforms.

But the technology also creates legitimate concerns about copyright, ownership, voice cloning, compensation, and authenticity.

That makes the future less about AI versus musicians and more about how musicians choose to work with AI.

The industry cannot afford to ignore the technology, but it should not adopt it blindly either. The strongest path forward is likely to use AI where it genuinely improves the creative or listening experience while keeping human judgment, artistic identity, and responsible use at the center.

FAQs

1. How is AI revolutionizing the music industry?

AI is revolutionizing music by assisting with composition, audio production, music discovery, personalization, marketing, and data analysis. It helps automate repetitive tasks while giving artists and music businesses new ways to create, analyze, and distribute music.

2. Can AI-generated music replace human musicians?

AI can generate increasingly sophisticated music, but it does not replace the emotional experience, artistic intent, cultural context, and personal expression provided by human musicians. AI is more likely to change creative workflows than eliminate musicians.

3. How is AI used in music production?

AI is used for tasks such as noise reduction, vocal separation, audio restoration, mixing assistance, mastering, and other forms of audio processing. These capabilities can reduce repetitive work and speed up production.

4. What are the biggest concerns about AI in music?

The biggest concerns include copyright, training data, ownership, artist compensation, unauthorized voice cloning, consent, and the authenticity of AI-generated or AI-assisted music.

5. What will AI do to the future of music?

AI will become more integrated into music creation, production, discovery, personalization, and performance. Human musicians are expected to remain central, while AI increasingly handles technical, repetitive, and data-intensive tasks.

The Grammar of Music

Mon, 08 Jun 2026 03:00:52 +0000

The Grammar of Music

The tempered lie is what makes the grammar speakable.

In 1722, Johann Sebastian Bach completed Das wohltemperierte Clavier — The Well-Tempered Clavier. Twenty-four preludes and fugues, one in each of the twelve major and twelve minor keys. It was the first comprehensive demonstration that a single keyboard could play in every key without retuning.

The title is a statement of method. Well-tempered. Tempered, as in tuned out of true. The book only worked because Bach's keyboard had been deliberately de-tuned — each fifth flattened by about two cents from its physically correct 3:2 ratio. One part in a thousand, inaudible as pitch. What the small lie bought was free movement through all twenty-four keys.

It had to be a lie. A true perfect fifth — the third harmonic of a vibrating string, the first non-octave overtone, the most consonant interval in the universe — does not close a twelve-step loop. Stack twelve true fifths and you overshoot seven octaves by what sixteenth-century theorists called the Pythagorean comma: about 23.46 cents, a quarter of a semitone, audible enough to ruin a chord (Barbour, Tuning and Temperament, 1951). The circle of fifths does not exist in physics. It exists because keyboard builders, in the century before Bach, distributed the comma evenly across twelve intervals and made modulation possible.

This essay is about what happens when a piece of mathematics — a small act of group theory performed at a keyboard — becomes the foundation for grammar.

Two questions, one tradition

When we ask how music "works," we are usually asking at least two questions without distinguishing them.

The first is about vocabulary. Which notes exist. Which are close to which. Which substitutions are allowed. This is the layer governed by the circle of fifths — in algebraic terms, the cyclic group ℤ/12ℤ, the integers modulo twelve under addition.

The second is about syntax. Which sequences of chords are coherent. How a recurring theme relates to its return eight minutes later. How a development section resolves to a recapitulation. This is the layer governed by something that looks a great deal like a formal grammar — the same kind of object Noam Chomsky proposed in 1956 to describe natural language.

The load-bearing argument of this essay is that the two layers are not independent. In language, syntax and lexicon are largely orthogonal: you can replace every noun in English with a nonsense word and still produce grammatical sentences. In music, you cannot. Tonal syntax depends on the algebra underneath it in a way English syntax does not depend on its phoneme set. The circle of fifths is not decoration; it is a load-bearing element of the grammar itself.

The algebra under the notes

The mathematician John D. Cook put the circle of fifths cleanly in a 2009 blog post: a fifth is seven chromatic steps, and since 7 is relatively prime to 12, it follows that 7 generates ℤ/12ℤ. Start on any pitch class, repeatedly add seven semitones, and you will visit every pitch class — all twelve — before returning to your starting point.

This works because gcd(7, 12) = 1. The fifth doesn't generate the chromatic scale because of anything special about fifths. It generates it because of something special about 7 and 12. Cook's observation, which he credits to elementary number theory: stepping by m through n notes covers all of them if and only if m and n are coprime.

Which integers less than 12 are coprime with 12? Only four: 1, 5, 7, and 11. Four intervals. Four circles. The circle of semitones (step 1). The circle of fourths (step 5). The circle of fifths (step 7). The circle of major sevenths (step 11). Everything else fails.

There is no circle of thirds. Ascending by major thirds (four semitones) produces a three-cycle — the augmented triad, C–E–G♯–C. Ascending by minor thirds (three semitones) produces a four-cycle — the diminished seventh, C–E♭–F♯–A–C. These are subgroups of ℤ/12ℤ, corresponding to the divisors of 12. John Coltrane built Giant Steps (1960) around the three-cycle of major thirds — the B/G/E♭ key axis — and the reason the piece sounds disorienting even to trained ears is that it bypasses the fifth-based tonal lattice the rest of Western music is built on. The piece is, in a precise algebraic sense, inside the subgroup.

The fifth won out over the fourth and the semitone and the major seventh as the canonical generator for a non-mathematical reason. The 3:2 frequency ratio is the most consonant interval other than unison or octave; two pitches a true fifth apart share more overtones than any other pair. So the fifth was the interval that happened to be both a group generator of ℤ/12ℤ and the most physically stable sound short of silence. A fortunate coincidence.

The ladder above the notes

In 1956, Noam Chomsky published "Three Models for the Description of Language," arguing that grammars describing natural language sit in a hierarchy of increasing expressive power. Four levels, from simplest to most general.

Type 3, regular languages. Generated by finite-state machines. Fixed patterns, basic repetition, Markov-like sequences. Cannot count.

Type 2, context-free. Pushdown automata. Balanced nested structures: arithmetic expressions, parentheses, aⁿbⁿ.

Type 1, context-sensitive. Linear-bounded automata. Cross-serial patterns — three or more sequences matched in count across a whole string.

Type 0, unrestricted. Anything computable. A full Turing machine.

Each class properly contains the one below. The formal class of your grammar constrains what structures you can express — a regular grammar cannot generate aⁿbⁿ, and no amount of patching will fix that.

The empirical question for linguistics was: where does natural language actually sit? Most of English looked context-free. But in 1984 Riny Huybregts, studying Dutch, and in 1985 Stuart Shieber, studying Swiss German, produced the same argument from different constructions. Certain embedded clauses require cross-serial dependencies — the kind of aⁿbⁿcⁿ pattern that is provably outside context-free. Shieber's canonical example from Swiss German: Jan säit das mer em Hans es huus hälfed aastriiche, "Jan says that we helped Hans paint the house," in which three noun phrases and three verbs must be matched in order across the sentence (Shieber, Linguistics and Philosophy 8(3), 1985).

So natural language sits just above context-free, in what Aravind Joshi coined in 1985 as the "mildly context-sensitive" band — strictly more expressive than Type 2, but strictly less than full Type 1, parsable in polynomial time. That specific slot is the empirical home of human language grammar.

Where music sits

Music sits there too. Probably.

The placement of tonal music on the Chomsky hierarchy is not a theorem. It is an empirical claim about how harmony behaves, argued over for forty years. The first formal attempt was Fred Lerdahl and Ray Jackendoff's 1983 book A Generative Theory of Tonal Music, a development of Leonard Bernstein's 1973 Norton Lectures at Harvard, in which Bernstein — aware of Chomsky's program — argued that music must have something like a generative grammar of its own. Lerdahl and Jackendoff credit Bernstein explicitly. Their book slightly cheats, though: it presents well-formedness rules and preference rules rather than a proper generative grammar with derivation trees. It set up the problem without quite finishing it.

The finishing came later. Mark Steedman, in a 1984 Music Perception paper, wrote a context-free grammar for jazz chord progressions including the 12-bar blues. Martin Rohrmeier, in 2011, published "Towards a generative syntax of tonal harmony" in the Journal of Mathematics and Music, producing a phrase-structure grammar for diatonic tonal music that could model recursive prolongation — the phenomenon where a tonic chord is elaborated by its own dominant, which is elaborated by its own secondary dominant, nested arbitrarily deep. Rohrmeier argues explicitly against Markov models: the structure of harmonic progressions exceeds the simplicity of Markovian transition tables.

The consensus, consolidated in Rohrmeier and Marcus Pearce's 2018 chapter in the Springer Handbook of Systematic Musicology, is that tonal music's generative structure is at or near mildly context-sensitive — the same formal class as natural language. Jonah Katz and David Pesetsky went further in a 2011 manuscript, "The Identity Thesis for Language and Music," arguing that all formal differences between the two reduce to differences in their atomic units. The strong claim is controversial. The weaker claim — shared formal class — is close to settled.

If that is right, then the answer to "how does music work?" requires two things on top of each other. A cyclic group of twelve pitch classes, with the circle of fifths defining adjacency. And a mildly-context-sensitive grammar, with production rules that move around the circle in specific, constrained ways.

The entanglement

Here is the asymmetry that makes music structurally strange.

In language, syntax and lexicon are largely independent. English grammar can be applied to any vocabulary. The blicket gorped the dax parses as subject-verb-object despite none of those words existing. Grammar operates on syntactic categories, not on the specific phonemes the categories contain. Swap every noun in English for a randomly generated word and you still have English syntactically. This is why "Jabberwocky" scans. This is why a child can learn her language's grammar before she knows most of its vocabulary.

In tonal music, this decoupling fails. Remove any pitch class from the twelve and the grammar collapses.

Specifically: the rules of tonal harmony use distance around the circle of fifths as their fundamental geometric prior. A modulation from C major to G major is grammatically cheap because the two keys are adjacent on the circle (one step). A modulation from C major to F♯ major is grammatically expensive because the keys are maximally distant (six steps), and tonal grammar has specific machinery for negotiating that distance — pivot chords, enharmonic reinterpretation, common-tone modulation. These rules do not exist in the abstract. They exist as operations on ℤ/12ℤ. Reduce the scale to eleven tones and the algebra breaks: 11 is prime, its cyclic group has no non-trivial subgroups, the adjacency geometry the grammar depends on simply does not hold.

This is why tonal music cannot be written with seven of the twelve pitch classes. A diatonic seven-note scale can express one key. It cannot modulate, because modulation is movement through the circle's adjacency, and the circle depends on all twelve. Language's grammar is largely orthogonal to its phoneme inventory. Music's grammar is entangled with its pitch-class group.

One sentence to carry the essay: the circle of fifths is not below the grammar; it is part of the grammar, and changing it changes what the grammar can say.

What this explains

The entanglement explains several things that otherwise look like stylistic preferences.

It explains why atonality feels like a different art form rather than different music. Arnold Schoenberg's twelve-tone technique, formalized around 1923, does not merely shift compositional taste — it abandons the ℤ/12ℤ adjacency geometry by treating all twelve pitch classes as equivalent. The grammar does not have a broken prior; it has no prior. The result is a compositional system with different formal properties, not a different dialect.

It explains why non-Western musical traditions sound "different" in a way that is not merely ornamental. Indian classical music uses twenty-two shrutis rather than twelve equal pitches; Arabic maqam uses quarter-tones; Indonesian gamelan uses non-octave-periodic scales. These are not the same algebra with different surface decoration — they are different pitch-class groups that support different harmonic grammars. Every tradition has a pitch-class algebra paired with a hierarchical grammar. The specific ℤ/12ℤ does not generalize. The argument of this essay is scoped to twelve-tone equal temperament, the system Bach ratified in 1722.

It explains something about modern AI as well. Google's Music Transformer (Huang et al., ICLR 2019, arXiv:1809.04281) showed that attention-based models trained on MIDI implicitly learn relative-pitch structure that mirrors the circle's geometry. A 2024 arXiv paper (Moyo & Chiurunge, arXiv:2403.00790) goes the other way, proposing the musical circle of fifths as an explicit geometric scaffold for structuring neural activation spaces. Either way, the geometry shows up. You can give the model the prior or let it rediscover the prior, but you cannot skip it.

Where the analogy breaks

An honest mapping has to mark its edges. Three divergences, ordered worst to least.

First, the Chomsky-hierarchy placement is a working hypothesis, not a theorem. Rohrmeier's grammar covers diatonic tonal progressions; it does not cleanly cover post-tonal composition, extended jazz substitutions, or micro-tonal repertoire. "At or near mildly context-sensitive" is the current best fit, not the last word.

Second, music and language are processed by partially different neural systems. Aniruddh Patel's Music, Language, and the Brain (Oxford, 2008) documents real dissociations — amusia patients who retain full linguistic ability, aphasic patients who retain full musical ability. Shared formal class does not mean shared substrate. The rhyme is formal, not anatomical.

Third, music's grammar is more permissive. Ungrammatical English sentences feel broken in a way that unusual chord progressions rarely do. Music's semantic demands are looser — a progression does not need to refer to anything — which gives its grammar more headroom before a violation becomes a violation.

The first two are honest limits. The third sharpens the main claim: music has a grammar whose rules are couched in its algebra, and because it does not need to refer, it can play near the edge of its own rules more openly than language can.

Close

Bach's 1722 decision — to write in all twenty-four keys, to demonstrate that the tempered keyboard could carry any modulation — was not ultimately a musical decision. It was an algebraic one. He was showing that ℤ/12ℤ could be treated as a closed system, that the circle could in fact close, that the grammar could move freely around it. Three centuries later, Western music from Beethoven through jazz through pop is a mildly-context-sensitive grammar operating on a cyclic group of twelve pitch classes. The two layers — the algebra below, the hierarchy above — are the same object viewed from two angles.

For anyone building systems with rules and vocabularies and long-range dependencies — language models, parsers, chord engines, protocol designers — the practical lesson is worth holding onto. It is not enough to ask what rules your grammar has. You also have to ask what algebraic structure your tokens live in, and whether your rules depend on that structure in ways you haven't named. If they do, you cannot change the token set without quietly changing the grammar. Most systems inherit their algebras by accident — a character set, an identifier space, a feature dictionary — and build rules on top that covertly exploit the inheritance. When something later forces you to change the underlying set, the grammar above fails in ways that look like bugs but are really the algebra speaking.

In music the problem was solved three hundred years ago by a deliberate act of mistuning. Every fifth on the keyboard was bent two cents flat so the circle would close. The tempered lie is what makes the grammar speakable. Everything since has rested on it.

Sources: Bach, Das wohltemperierte Clavier Book I, 1722; Barbour, Tuning and Temperament, MSC Press, 1951; Chomsky, "Three Models for the Description of Language," IRE Trans. Info. Theory, 1956; Cook, johndcook.com 2009; Huybregts 1984; Shieber, Linguistics and Philosophy 8(3), 1985 (doi:10.1007/BF00630917); Joshi, "Tree Adjoining Grammars," 1985; Lerdahl & Jackendoff, A Generative Theory of Tonal Music, MIT Press, 1983; Steedman, Music Perception 2(1), 1984 (doi:10.2307/40285282); Rohrmeier, Journal of Mathematics and Music 5(1), 2011 (doi:10.1080/17459737.2011.573676); Rohrmeier & Pearce, in Springer Handbook of Systematic Musicology, 2018 (doi:10.1007/978-3-662-55004-5_25); Katz & Pesetsky, lingbuzz/000959, 2011; Patel, Music, Language, and the Brain, Oxford UP, 2008; Huang et al., ICLR 2019 (arXiv:1809.04281); Moyo & Chiurunge, 2024 (arXiv:2403.00790).

Tokens inherit algebras. So do agents. The essay's lesson — your rules quietly depend on the algebraic structure your tokens live in — is the same problem the agent economy is about to discover. Agent identifiers, rating tokens, capability claims: each carries an implicit algebra. If two agents can be silently re-counted, renamed, or duplicated, the trust grammar above fails in ways that look like bugs but are really the algebra speaking. The Agent Rating Protocol fixes the algebra by anchoring every rating to a public provenance chain. See a live chain · Verify an agent's rating · pip install agent-rating-protocol

Originally published at vibeagentmaking.com.

How to Fix Samsung Galaxy Book4 Pro Speakers on Linux

Fri, 22 May 2026 14:57:38 +0000

The Problem

If you have a Samsung Galaxy Book4 Pro running Linux, you've probably noticed that the internal speakers don't work. The headphone jack works fine, but the built-in speakers are completely silent — regardless of your distro or kernel version.

This post documents what actually works after hours of research through forums, repositories, and documentation.

Why It Happens

The Galaxy Book4 Pro uses the Realtek ALC298 codec combined with MAX98390 smart amplifiers connected via I2C bus. The Linux kernel detects the codec correctly, but never initializes the MAX98390 amplifier — resulting in completely silent speakers.

On Windows, Samsung installs a proprietary DSP driver that automatically initializes and controls this amp. On Linux, that driver simply doesn't exist.

Diagnosis

First, confirm your hardware is affected:

cat /proc/asound/card0/codec#0 | grep -i "codec\|subsystem"

If you see Codec: Realtek ALC298 and a Subsystem ID starting with 0x144d (Samsung), you're in the right place.

Also confirm the amp is never initialized:

sudo dmesg | grep -i "max98\|cs35\|speaker\|amp"

If there are no lines about MAX98390, the amp was never woken up.

The Fix

Thanks to the amazing work by @Andycodeman and the open source community, there's a DKMS driver that solves the problem. The fix installs the MAX98390 driver, creates a systemd service that initializes the amp on every boot, and works automatically after restarting.

Before installing, verify:

# Secure Boot must be disabled
mokutil --sb-state

Installation (Ubuntu/Zorin/Mint/Pop!_OS):

curl -sL https://github.com/Andycodeman/samsung-galaxy-book-linux-fixes/archive/refs/heads/main.tar.gz | tar xz && cd samsung-galaxy-book-linux-fixes-main/speaker-fix && sudo ./install.sh

Test without rebooting:

sudo systemctl start max98390-hda-i2c-setup.service

If sound comes out, reboot to confirm it persists:

sudo reboot

Improving Sound Quality with EasyEffects

The driver initializes the amp, but the sound is more "raw" than on Windows (where Samsung uses proprietary DSP). To improve it, install EasyEffects:

sudo apt install -y easyeffects calf-plugins

In the Effects → Output tab, add:

Equalizer — boost mids (1kHz~4kHz) and reduce excessive bass (32Hz~250Hz)
Limiter with threshold at -3dB — prevents distortion on peaks
Compressor with 4:1 ratio and 5ms attack — softens hard transients

Set EasyEffects to run in the background: ⋯ → Launch as a Service

Tested On

Model	Distro	Kernel	Status
Galaxy Book4 Pro (NP940XGK)	Zorin OS 18.1	6.17.0-23-generic	✅ Working

Credits

Andycodeman — DKMS packaging, install scripts, dynamic I2C detection
Kevin Cuperus — Original MAX98390 driver code (upstream PR #5616)
Google Redrix (Chromebook) — DSP firmware blobs extracted from same MAX98390 hardware

Repository: https://github.com/Andycodeman/samsung-galaxy-book-linux-fixes

Cross-Platform Music App Development Using Flutter and React Native

Thu, 14 May 2026 07:43:33 +0000

Quick Overview

Flutter (Dart) and React Native (JavaScript) are the top frameworks
for launching music apps on iOS and Android from a single codebase.
Both support essential music features, such as streaming, background
playback, waveform visualization, and media controls, through
well-maintained plugin ecosystems.
Audio performance, low latency, and real-time processing are the
main technical challenges in cross-platform music development.
The right framework depends on your team's skills, the app's
complexity, and performance requirements; there is no
one-size-fits-all solution.
Choosing a cross-platform approach can reduce development time and
costs by 30 to 50 percent compared to building separate native iOS
and Android apps.

You have a great idea for a music app. It could be a tool for collaborative playlists, a practice companion for musicians, or a complete streaming experience. You've done your research and have a development plan. Then reality hits: do you build it for iOS first, Android, or both at the same time? If you choose both, does that mean you need two separate teams, two separate codebases, and twice the budget?

This is the exact problem that has led developers to cross-platform frameworks. Right now, Flutter and React Native are the two main players in this field. This article explains how each framework approaches music app development, the trade-offs involved, and how to make the best choice for your project.

Why Cross-Platform Is a Smart Choice for Music Apps

Before exploring frameworks, note that cross-platform development offers benefits for complex music apps involving real-time audio, background playback, hardware controls, Bluetooth switching, and low-latency features. While native development historically excels here, the gap with cross-platform has narrowed.

Platforms like Flutter and React Native have developed to the point where most music app experiences feel just like native ones in everyday use. And for teams working with music app development services, the ability to ship one codebase to two platforms without sacrificing quality is a genuine competitive advantage.

The economics are clear: one cross-platform team costs less than two native teams, with streamlined maintenance and simultaneous feature releases. For startups and mid-sized companies, this advantage can determine a project's viability.

Flutter in Music App Development: Essential Insights

Flutter, supported by Google, uses Dart and the Skia rendering engine, now replaced by Impeller. Instead of relying on the host platform's UI components, Flutter renders every pixel itself. This approach benefits music apps.

Visual consistency: Waveform displays, equalizer animations, album
art carousels, and custom playback controls look the same on iOS and
Android, eliminating UI rendering frustration across platforms.
Audio handling: Since Flutter lacks a built-in audio engine, you'll
need packages like just_audio, audio_service, and flutter_sound.
just_audio supports streaming, gapless playback, pitch shifting, and
audio effects, suitable for most music apps. audio_service manages
background playback and media notifications, essential for any music
app.
Performance: Flutter's compiled Dart code and rendering pipeline
ensure smooth animations and a responsive UI, but can lag during
heavy platform-channel communication. Handling real-time native audio
processing may cause delays due to Dart-native exchange.
Ideal use cases: They include streaming, music discovery, podcast,
music education, and DJ mixing apps that don't need very low-latency
audio (under 10 ms).

React Native for Music App Development: Essential Insights

React Native, maintained by Meta, uses JavaScript or TypeScript and connects to native components. The newer "New Architecture" adopts JSI instead of the old bridge, significantly narrowing the performance gap with native development.

The JavaScript ecosystem: If your team already works with React or
JavaScript, the learning curve is much shorter. For React Native app
development, the large npm ecosystem means there are libraries for
almost everything a music app might need, from audio processing to
music-theory tools.
Audio libraries: react-native-track-player is the preferred tool for
streaming and queue management, supporting background playback, lock
screen controls, and CarPlay/Android Auto. react-native-sound is good
for basic audio, while react-native-audio-toolkit manages recording.
For advanced DSP, native modules are used.
Consistency: React Native uses native components, so your app adopts
each platform's look and feel. This works well for standard UI
patterns but can be inconsistent for custom designs.
Ideal use cases: It includes music players, streaming services,
social platforms, learning apps, and apps where React or
JavaScript skills are important.

Flutter vs. React Native for Music Apps

Key Technical Considerations Specific to Music Apps

Regardless of which framework you choose, certain challenges are common in cross-platform music development.

Background audio playback: Both frameworks handle this with dedicated
packages: audio_service for Flutter and react-native-track-player for
React Native, but require careful configuration of platform-specific
entitlements and permissions.
Low-latency audio: For apps requiring latency below ~20ms, such as
real-time tuners or live tools, cross-platform frameworks may reach
their limits, and custom native modules might be necessary regardless
of the framework.
Audio streaming and buffering: Managing an app's state is crucial.
Music streaming apps must handle network issues, adaptive bitrate,
and pre-buffering. Both frameworks support this, but implementation
is complex.
Waveform visualization: It uses significant CPU resources. Flutter
CustomPainter and React Native's canvas options, via
react-native-skia or WebGL, handle this. Optimization is vital,
especially on mid-range Android devices.

Conclusion

Choosing between Flutter and React Native isn't about picking the better tool but the right one for your team, timeline, and features. Flutter offers visual consistency and performance predictability, while React Native excels in ecosystem familiarity and native platform integration. Both can produce high-quality cross-platform music apps indistinguishable from native ones.

Understanding your app's technical needs early is crucial. If real-time audio is essential, evaluate how frameworks handle native modules. For streaming or discovery, either framework works, and cross-platform saves are substantial. The best framework is the one your team confidently uses. Choose it, build effectively, and let the music speak.

FAQs

1. Can Flutter or React Native handle real-time audio processing for music apps?

Both handle standard audio tasks well with plugins, but lack out-of-the-box ultra-low-latency (under 10ms) support. Features like live tuning or MIDI require native code in Swift or Kotlin. Still, both frameworks can stream and play without native tweaks.

2. Which is better for a music streaming app, Flutter or React Native?

Both work well. Flutter provides consistent UI and smooth animations, while React Native's react-native-track-player offers a stronger streaming ecosystem with CarPlay and Android Auto support. React Native teams ship faster; Flutter users may prioritize visual appeal.

3. How do cross-platform music apps handle background playback?

Both frameworks handle background audio, lock screen controls, and media notifications via dedicated packages: audio_service in Flutter and react-native-track-player in React Native. You must manually configure platform permissions and test on real devices, as emulators often poorly handle background audio.

4. Is Flutter or React Native faster to develop a music app with?

Your team's expertise influences the choice. JavaScript developers may prefer React Native for speed, while Flutter's clear structure eases Dart's learning curve and scalability. Both frameworks can cut development time by 30-50% versus native iOS and Android.

5. What are the main limitations of cross-platform frameworks for music apps?

Main limitations include low-latency audio, hardware integration, and delayed access to new APIs. Complex audio features often need native modules. But for most consumer music apps like streaming, playback, and visualization, these issues are generally not significant.

Understanding the ACE-Step Songwriting Guide for Music Creation

Fri, 20 Mar 2026 12:53:19 +0000

What is the ACE-Step Songwriting Guide?

The ACE-Step Songwriting Guide is a specialized skill designed to help users create professional-quality music through a structured approach to songwriting. This guide provides comprehensive knowledge on writing captions, composing lyrics, selecting appropriate musical parameters, and structuring songs effectively before generating them with ACE-Step technology.

Core Components of the Guide

The guide focuses on two primary outputs that work together to create cohesive musical compositions:

1. The Caption: Your Musical Blueprint

The caption serves as the most critical input for generating music. It acts as a detailed description that guides the AI in creating the desired sound. The caption supports multiple formats including simple style words, comma-separated tags, or complex natural language descriptions.

Key dimensions to consider when crafting your caption include:

Style/Genre: pop, rock, jazz, electronic, hip-hop, R&B, folk, classical, lo-fi, synthwave
Emotion/Atmosphere: melancholic, uplifting, energetic, dreamy, dark, nostalgic, euphoric, intimate
Instruments: acoustic guitar, piano, synth pads, 808 drums, strings, brass, electric bass
Timbre Texture: warm, bright, crisp, muddy, airy, punchy, lush, raw, polished
Era Reference: 80s synth-pop, 90s grunge, 2010s EDM, vintage soul, modern trap
Production Style: lo-fi, high-fidelity, live recording, studio-polished, bedroom pop
Vocal Characteristics: female vocal, male vocal, breathy, powerful, falsetto, raspy, choir
Speed/Rhythm: slow tempo, mid-tempo, fast-paced, groovy, driving, laid-back

Effective caption writing follows several principles: be specific rather than vague, combine multiple dimensions for precision, use references effectively, employ texture words, avoid perfection paralysis, and maintain consistency between caption elements.

2. The Lyrics: Your Temporal Script

Lyrics serve as the temporal script that controls how music unfolds over time. They carry the actual lyric text, structure tags, vocal style hints, instrumental sections, and energy changes throughout the composition.

The guide provides a comprehensive system of structure tags organized into categories:

Basic Structure: [Intro], [Verse], [Pre-Chorus], [Chorus], [Bridge], [Outro]
Dynamic Sections: [Build], [Drop], [Breakdown]
Instrumental: [Instrumental], [Guitar Solo], [Piano Interlude]
Special: [Fade Out], [Silence]

Combining tags with hyphens allows for finer control, such as [Chorus – anthemic] or [Verse – building energy]. However, complex style descriptions should remain in the caption rather than the tags.

Advanced Lyric Writing Techniques

The guide offers sophisticated lyric writing tips to create professional-quality content:

Maintain 6-10 syllables per line for optimal alignment with musical beats
Use uppercase letters to indicate stronger vocal intensity
Employ parentheses for background vocal parts
Extend vowels carefully for stylistic effects
Separate sections with blank lines for clarity

The guide also warns against common pitfalls that create “AI-flavored” lyrics, such as adjective stacking, rhyme chaos, blurred boundaries between sections, lack of breathing room, and mixed metaphors. Instead, it recommends metaphor discipline with one core metaphor per song.

Music Metadata Parameters

Beyond captions and lyrics, the guide addresses music metadata parameters that can be set manually or left for the AI to infer:

Duration: Set in seconds, calculated based on song structure and tempo
BPM (Beats Per Minute): Ranges from 30-300, with common ranges for different tempos
Key: Musical key such as C Major or A minor, with common keys being most stable
Time Signature: Most commonly 4/4, but can be 3/4 for waltzes or 6/8 for swing
Language: Usually auto-detected from lyrics

The guide provides detailed duration calculation methods, considering intro/outro lengths, instrumental sections, and typical structures like 2 verses + 2 choruses or songs with bridges.

Integration and Consistency

A crucial aspect of the guide is ensuring consistency between all elements. The model works best when there are no conflicts between the caption, lyrics, and parameters. For example, if the caption mentions “piano ballad,” the lyrics should include appropriate piano sections, and the overall structure should support that style.

The guide emphasizes that models are not good at resolving conflicts, so users should maintain consistency throughout their creative choices. This includes matching instruments, emotions, and vocal characteristics across all components.

Practical Applications

This skill is particularly useful when users want to:

Create, write, or plan a song before generating it with ACE-Step
Produce professional-quality music with specific stylistic requirements
Structure complex musical compositions with multiple sections
Control the emotional journey and energy progression of a song
Ensure consistency between lyrical content and musical style

By following the ACE-Step Songwriting Guide, users can create more polished, intentional, and professional musical compositions that align with their creative vision.

Skill can be found at: https://github.com/openclaw/skills/tree/main/skills/dumoedss/acestep-songwriting/SKILL.md

The post Understanding the ACE-Step Songwriting Guide for Music Creation first appeared on Insight Ginie.

Ethni-CITY: The New Travel App

Wed, 18 Mar 2026 15:04:08 +0000

If you're chronically online like I am, you're used to seeing tons of reels of people who travel to all these "authentic" destinations: Sao paulo, Marrakech, Cairo, Bangkok, and Bali. On Instagram or TikTok, it's pretty common to accompany your posts with a soundtrack, and it's the norm to post the latest trending songs to garner likes and reposts.
No shame here.
We all want validation, but so do the local economies we're coming from, and they hardly benefit when our money goes to international hotel chains and global brands. However, there are a few ways we can support locals while still making sure your pics eat, and one of them is by supporting local artists.
With Ethni-CITY, we connect your pics to the local artists of the countries you visit to curate a niche storyboard that puts artists' songs on your posts, helping them monetize their craft, gain recognition, and in turn boost their micro-economy as your pics garner likes.

To build this, I weaved spatial data with cultural analysis.

Using Gemini 3.1 pro preview as the main model, the app performs a deep multimodal analysis of your uploaded photos and then identifies the city and country the picture was taken in by looking for landmarks, cultural motifs, textile patterns and lighting conditions to determine.
I used Cesium JS along with Google Cloud photorealistic tiles, the agent transports you to the location of the photo, helping to tell a centric story.
To ensure the app survives the "Hackathon Hug," I implemented a hierarchical fallback logic:
- Tier 1: gemini-3.1-pro-preview for high-fidelity creative direction.
- Tier 2: gemini-2.0-flash for high-speed fallback.
- Tier 3: gemini-1.5-flash-8b for emergency quota resilience.

CHALLENGES FIXED:

By integrating a heavyweight library like Cesium into NextJS App router environment, posed significant challenges with asset serving and Typescript definitions. I had to automate the movement of Cesium's build assets into the public directory during the pre-build phase to ensure the tiles are rendered correctly in production.

I'm grateful for Gemini for providing the tools to curate this experience and help promote local artists from around the world.

Explore the project on GitHub:[ https://github.com/david-ac1/Ethni-CITY]
Watch the demo:[ https://youtu.be/kdrUzPtRw9Y?si=EzWt6DvbdbLjOk4V]

#GeminiLiveAgentChallenge #GoogleAI #GoogleCloud #Gemini