Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Fundamentals of Music Processing (Chapter 5)
Search
Koga Kobayashi
December 12, 2019
Research
110
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
Fundamentals of Music Processing (Chapter 5)
Koga Kobayashi
December 12, 2019
More Decks by Koga Kobayashi
See All by Koga Kobayashi
第13回 Data-Centric AI勉強会, LLMのファインチューニングデータ
kajyuuen
4
1.9k
基礎数学の公式
kajyuuen
1
190
初等確率論の基礎
kajyuuen
1
200
Deep Markov Model を数式で追う (+ Pyroでの追試)
kajyuuen
0
960
完全なアノテーションが得られない状況下での固有表現抽出
kajyuuen
3
3.7k
SecHack365 北海道会 LT
kajyuuen
0
550
専門用語抽出手法の研究と 抽出アプリケーションの開発
kajyuuen
1
1.3k
Other Decks in Research
See All in Research
Fukui Shibiten 39 - AI Art
butchi
0
180
MM-OVSeg: Multimodal Optical–SAR Fusion for Open-Vocabulary Segmentation in Remote Sensing
satai
3
110
SOTAのさらに先へ:厳しい推論制約下での高性能モデルのPost-Training
analokmaus
0
1.4k
Dual Quadric表現を用いた動的物体追跡とRGB-D・IMU制約の密結合によるオドメトリ推定
nanoshimarobot
0
510
HAKARI-Bench - 実運用視点での情報検索モデル評価ベンチマーク
hotchpotch
1
680
GLIM とMegaParticles:正規分布近似の限界とタイトカップリング&パーティクルフィルタの進展 / GLIM and MegaParticles : Progress of the distribution representation in SLAM
koide3
0
720
LLM の Attention 機構まとめ — 数式・計算量・メモリ
puwaer
8
2.5k
[最先端NLP勉強会2026] Agentic Rubrics as Contextual Verifiers for SWE Agents
rfujii
1
140
論文紹介 "ReSim: Reliable World Simulation for Autonomous Driving"
kogo
0
740
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
shunk031
4
1.2k
敵対生成プロンプト同時探索による内省型プロンプト最適化
kinoue_smarthr
0
370
SAKURAONE:An Open Ethernet-based AI HPC System And Its Observed Workload Dynamicsin a Single-Tenant LLM Development Environment
yuukit
1
540
Featured
See All Featured
VelocityConf: Rendering Performance Case Studies
addyosmani
331
25k
Utilizing Notion as your number one productivity tool
mfonobong
4
550
Distributed Sagas: A Protocol for Coordinating Microservices
caitiem20
333
23k
コードの90%をAIが書く世界で何が待っているのか / What awaits us in a world where 90% of the code is written by AI
rkaga
63
45k
More Than Pixels: Becoming A User Experience Designer
marktimemedia
3
500
SERP Conf. Vienna - Web Accessibility: Optimizing for Inclusivity and SEO
sarafernandez
2
1.6k
Bioeconomy Workshop: Dr. Julius Ecuru, Opportunities for a Bioeconomy in West Africa
akademiya2063
PRO
1
270
Abbi's Birthday
coloredviolet
3
9.4k
Six Lessons from altMBA
skipperchong
29
4.5k
Producing Creativity
orderedlist
PRO
348
40k
Designing Dashboards & Data Visualisations in Web Apps
destraynor
232
55k
Exploring the Power of Turbo Streams & Action Cable | RailsConf2023
kevinliebholz
37
6.6k
Transcript
Fundamentals of Music Processing Chapter 5: Chord Recognition খྛ
ᕣՏ εϥΠυʹؚ·ΕΔਤFundamentals of Music ProcessingΑΓҾ༻
Chapter 5: Chord Recognition Chord(Ի) • 3ͭҎ্ͷҟͳΔԻූ͔Βߏ͞ΕΔԻͷ͜ͱ Harmony() • ෳͷԻ͔ΒͳΔܥྻɺԻਐߦ
FM7 G7 Em7 Am Harmony
Chapter 5: Chord Recognition Chord Recognition(Իೝࣝ) • Ի͔ΒԻਐߦΛೝࣝ͢Δٕज़ ԻָϑΝΠϧ͔ΒίʔυේΛࣗಈͰ࡞ग़དྷΔ Իೝ͕ࣝ͏·͍͘͘ͱ…
Chapter 5.3: HMM-Based Chord Recognition Chapter 5.1~5.2 • ಛྔ͔ΒԻΛ͋Δఔਪఆग़དྷΔ ͔͠͠ɺ͜ΕԻҰͭҰ͔ͭ͠ݟ͍ͯͳ͍
Α͘ग़ΔԻܨ͕Γͷڧ͍Իʹண͍ͨ͠ ྫ: I–IV–V–Iਐߦ • FGසग़͠ɺ͍͖ͳΓFmʹߦ͘͜ͱ΄΅ແ͍ HMM(ӅΕϚϧίϑϞσϧ)Λར༻ͯ͠ԻਪఆΛߦ͏
Chapter 5.3: HMM-Based Chord Recognition Ի: , ঢ়ଶ: (for )
ͱͨ͠ͱ͖ɺϚϧίϑੑΛԾఆ͢Δͱ Ͱ͋Δ֬ A := {α1 , α2 , ⋯, αI } s i ∈ [1 : I] sn+1 = αj P[sn+1 = αj |sn = αi , sn−1 = αk , ⋯] = P[sn+1 = αj |P[sn+1 = αj |sn ] ͜͜Ͱ ΛҎԼͷΑ͏ʹఆٛ͢Δɻ(for ) aij i, j ∈ [1 : I] ͜Εঢ়ଶ͕ ͔Β ʹભҠ͢Δ֬ͱߟ͑Δ͜ͱ͕ग़དྷΔ αi αj aij := P[sn+1 = αj |sn = αi ] ∈ [0,1] ·ͣɺϚϧίϑ࿈Λར༻ͨ͠Ի༧ଌʹ͍ͭͯઆ໌͢Δ
Chapter 5.3: HMM-Based Chord Recognition ۩ମྫ; , ͷͱ͖ I =
3 A := {α1 = C, α2 = G, α3 = F} ·ͨ࠷ॳͷঢ়ଶ͕ Ͱ͋Δ֬ΛҎԼͷΑ͏ʹఆٛ͢Δ αi ci := P[s1 = αi ] ∈ [0,1]
Chapter 5.3: HMM-Based Chord Recognition , , ͷͱ͖ ঢ়ଶܥྻ: ʹ͍ͭͯߟ͑Δ
I = 3 A := {α1 = C, α2 = G, α3 = F} C = (c1 , c2 , c3 )T = (0.6,0.2,0.3)T S = (C, C, C, G, G, F, F, C, C) ࠷ॳͷঢ়ଶ͕ Ͱ͋Δ֬ΛҎԼͷΑ͏ʹఆٛ͠ɺ αi ci := P[s1 = αi ] ∈ [0,1] ۩ମྫ
Chapter 5.3: HMM-Based Chord Recognition ભҠ͕֬ҎԼͷͱ͖ ͷΑ͏ͳԻਐߦ͕ى͖Δ֬ S S =
(C, C, C, G, G, F, F, C, C) = c1 ⋅ a11 ⋅ a11 ⋅ a12 ⋅ a22 ⋅ a23 ⋅ a33 ⋅ a31 ⋅ a11 ≈ 1.29 ⋅ 10−4
Chapter 5.3: HMM-Based Chord Recognition ઌఔঢ়ଶܥྻ Λ༻͍ͯɺԻਐߦͷ֬Λܭࢉ͕ͨ͠ ࣮ੈքͰऔΓ͏Δͯ͢ͷ ʹ͍ͭͯܭࢉෆՄೳ S
S ྫ: 10छྨͷԻɺ20͔ͭΒߏ͞ΕΔۂͷ߹ ύλʔϯͷ֬Λܭࢉ͢Δඞཁ͕͋Δ 1020 ͦ͜Ͱ෦ͷঢ়ଶͰͳ͘ɺಛϕΫτϧΛ༻͍ͯ ԻਐߦΛٻΊΔํ๏ͱͯ͠HMMΛར༻͢Δɻ
Chapter 5.3: HMM-Based Chord Recognition
Chapter 5.3: HMM-Based Chord Recognition
Chapter 5.3: HMM-Based Chord Recognition Input: Իσʔλ͔Β؍ଌͨ͠ܥྻ ͔Β ϞσϧΛར༻͠ɺ؍ଌܥྻ Λ࡞ɻ
O = (o1 , ⋯, oN ) B = (β1 , ⋯, βN ) ؍ଌܥྻ ؍ଌγϯϘϧ ͔Βߏ͞ΕΔ B = (β1 , ⋯, βN ) ℬ = {β1 , ⋯, βk } (for k ∈ [1 : K])
Chapter 5.3: HMM-Based Chord Recognition
Chapter 5.3: HMM-Based Chord Recognition Viterbi: ؍ଌܥྻ ͱ ॳظঢ়ଶͷ֬
ੜ֬ͱભҠ͔֬Β༗άϥϑΛ࡞ B = (β1 , ⋯, βN ) C = (c1 , c2 , c3 )T = (0.6,0.2,0.3)T ੜ֬ ભҠ֬
Chapter 5.3: HMM-Based Chord Recognition ੜ֬ ભҠ֬ ॳظঢ়ଶͷ֬ ੜ͞Εͨ༗άϥϑ
Chapter 5.3: HMM-Based Chord Recognition ViterbiΞϧΰϦζϜʹΑͬͯ ࠷Β͍͠ܦ࿏ʹ͍ͭͯܭࢉΛߦ͏
Chapter 5.3: HMM-Based Chord Recognition ੜ͞Εͨ༗άϥϑ ViterbiΞϧΰϦζϜͰ֤ҐஔͰɺ ֤ԻʹͨͲΓண͘·Ͱͷ࠷దίετͱ ͦͷલʹࢸΔ·ͰͷϙΠϯλΛ֮͑ͳ͕ΒܭࢉΛߦ͏
Chapter 5.3: HMM-Based Chord Recognition ੜ͞Εͨ༗άϥϑ ViterbiΞϧΰϦζϜͰ֤ҐஔͰɺ ֤ԻʹͨͲΓண͘·Ͱͷ࠷దίετͱ ͦͷલʹࢸΔ·ͰͷϙΠϯλΛ֮͑ͳ͕ΒܭࢉΛߦ͏
Chapter 5.3: HMM-Based Chord Recognition ੜ͞Εͨ༗άϥϑ ViterbiΞϧΰϦζϜͰ֤ҐஔͰɺ ֤ԻʹͨͲΓண͘·Ͱͷ࠷దίετͱ ͦͷલʹࢸΔ·ͰͷϙΠϯλΛ֮͑ͳ͕ΒܭࢉΛߦ͏
Chapter 5.3: HMM-Based Chord Recognition ੜ͞Εͨ༗άϥϑ ViterbiΞϧΰϦζϜͰ֤ҐஔͰɺ ֤ԻʹͨͲΓண͘·Ͱͷ࠷దίετͱ ͦͷલʹࢸΔ·ͰͷϙΠϯλΛ֮͑ͳ͕ΒܭࢉΛߦ͏
Chapter 5.3: HMM-Based Chord Recognition ੜ͞Εͨ༗άϥϑ ViterbiΞϧΰϦζϜͰ֤ҐஔͰɺ ֤ԻʹͨͲΓண͘·Ͱͷ࠷దίετͱ ͦͷલʹࢸΔ·ͰͷϙΠϯλΛ֮͑ͳ͕ΒܭࢉΛߦ͏
Chapter 5.3: HMM-Based Chord Recognition ੜ͞Εͨ༗άϥϑ ViterbiΞϧΰϦζϜͰ֤ҐஔͰɺ ֤ԻʹͨͲΓண͘·Ͱͷ࠷దίετͱ ͦͷલʹࢸΔ·ͰͷϙΠϯλΛ֮͑ͳ͕ΒܭࢉΛߦ͏
Chapter 5.3: HMM-Based Chord Recognition ࠷ޙʹɺͦͷϙΠϯλΛḷΓ࠷Β͍͠ԻਐߦΛ ٻΊΔ A := {α1
= C, α2 = G, α3 = F} ͷͱ͖ ̂ S = (C, C, C, G, G, F)