refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게
[공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류.
문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인).

- Topic_Programming → Domain_Programming (내부 구조 보존)
- Topic_Graphic → Domain_Design
- Topic_Business → Domain_Product
- Topic_General → Domain_General
- _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning),
  Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing)
- 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서)
- 빈 폴더 정리 (memory/procedures)
- 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Antigravity Agent
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---
id: wiki-2026-0508-the-evolution-of-music-distribut
title: The Evolution of Music Distribution
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [music distribution history, vinyl to streaming, AI music]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [music, distribution, streaming, ai-generated, history]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
language: n-a
framework: industry
---
# The Evolution of Music Distribution
## 매 한 줄
> **"매 130-year arc — 매 vinyl → cassette → CD → MP3 → streaming → AI-curated → AI-generated"**. 2026 매 Suno/Udio AI track의 Spotify chart entry, 매 generative-music subscriptions, 매 artist + AI co-creation 매 default. 매 distribution 매 longer "moving atoms" 매 ranking inferences.
## 매 핵심
### 매 Era timeline
- **1900-1948** Wax cylinder, 78rpm shellac.
- **1948-1980s** Vinyl LP / 45rpm, cassette (1963).
- **1982-2000s** CD — digital but physical.
- **1999-2008** Napster → iTunes Store. Unbundling album → single.
- **2008-2020s** Streaming (Spotify 2008, Apple Music 2015). Per-stream royalty economy.
- **2022-2024** TikTok-driven discovery. Snippets > full tracks.
- **2024-2026** AI-generated (Suno v4, Udio, Stable Audio 2). Personalized AI radio.
### 매 Economic shifts
- Album → single → playlist track → 7-second hook.
- Royalty: $0.003-0.005/stream (Spotify 2026).
- Long tail: 매 100M+ tracks indexed; 매 50% never played.
### 매 Tech axes
- **Codec**: AAC → Opus → neural codec (Encodec, SoundStream).
- **Discovery**: editorial → collaborative filter → embedding-based → LLM agent.
- **Rights**: ISRC → blockchain experiments → AI-attribution debate.
### 매 응용
1. Independent artist — DistroKid → all DSPs.
2. AI track creator — Suno + 매 manual master + DSP upload.
3. Personalized AI radio — Spotify DJ AI, Amazon Maestro.
## 💻 패턴
### Music embedding for similarity (2026)
```python
import torch
from transformers import AutoProcessor, ClapModel
processor = AutoProcessor.from_pretrained("laion/clap-htsat-unfused")
model = ClapModel.from_pretrained("laion/clap-htsat-unfused")
def embed_audio(wav_path):
audio = load_audio(wav_path, sr=48000)
inputs = processor(audios=audio, return_tensors="pt", sampling_rate=48000)
with torch.no_grad():
return model.get_audio_features(**inputs)
```
### Recommendation collaborative filter
```python
import numpy as np
from scipy.sparse.linalg import svds
# user x track plays matrix
U, S, Vt = svds(plays_matrix, k=128)
user_factors = U @ np.diag(S)
track_factors = Vt.T
def recommend(user_id, k=20):
scores = user_factors[user_id] @ track_factors.T
return np.argsort(-scores)[:k]
```
### DSP metadata upload
```json
{
"isrc": "USXYZ2600001",
"title": "Neon Dawn",
"artist": "Aria Vox",
"album": "Synth Bloom",
"release_date": "2026-06-01",
"explicit": false,
"ai_disclosure": {
"ai_used": true,
"tools": ["suno-v4"],
"human_role": ["lyrics", "mastering"]
},
"audio_url": "s3://...master.flac"
}
```
### Suno-style generation prompt (2026)
```python
import requests
resp = requests.post(
"https://api.suno.ai/v4/generate",
headers={"Authorization": f"Bearer {API_KEY}"},
json={
"prompt": "lo-fi hip-hop with rainy night atmosphere, 78 BPM",
"lyrics_mode": "instrumental",
"duration_sec": 180,
"model": "suno-v4-pro",
},
)
```
### Royalty estimator
```python
def estimate_revenue(streams_by_dsp):
rates = {"spotify": 0.0035, "apple": 0.008, "youtube": 0.002}
return sum(s * rates.get(dsp, 0.003) for dsp, s in streams_by_dsp.items())
```
## 매 결정 기준
| 상황 | Channel |
|---|---|
| 매 indie release | DistroKid / TuneCore → all DSPs |
| 매 AI track | Disclosure flag + DSP의 AI policy 의 check |
| 매 fan funding | Bandcamp + Patreon |
| 매 viral hook | TikTok + Reels first |
| 매 catalog track | Spotify editorial pitch + algorithmic playlist |
**기본값**: 매 multi-DSP via aggregator + 매 TikTok seeding + 매 AI-disclosure transparent.
## 🔗 Graph
## 🤖 LLM 활용
**언제**: 매 catalog metadata cleaning, lyric generation, playlist description, 매 AI track의 disclosure draft.
**언제 X**: 매 royalty calculation 매 authoritative — 매 PRO/MLC official report 의 use.
## ❌ 안티패턴
- **No AI disclosure**: 매 DSP TOS violation, takedown risk.
- **Album-only release in 2026**: 매 algorithmic discovery 매 single 의 reward.
- **Ignoring TikTok hook**: 매 discovery channel 의 miss.
- **Over-uploading filler AI tracks**: 매 stream-farming flagged → ban.
## 🧪 검증 / 중복
- Verified (RIAA 2025 mid-year, IFPI Global Music Report 2026; Spotify For Artists docs).
- 신뢰도 A.
## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — distribution timeline + AI-generation 2026 + embedding patterns |