Making machines that make music – Strangeloop 2018

Another iteration of the talk, for presentation at Strangeloop 2018.

Transcript

1. Making machines that make music ीहर ीरामन् नलेो Srihari Sriraman nilenso
2. Why I do this I sing, I do computers Bleeding edge research A long time hobby Re-discovery
3. Structure of this talk Universality, Carnatic music A timeline of 4 phases Bridging the gap between machines and humans Understand domain, create abstractions Synthesise ⟷ Generate ⟷ Transcribe Reflect on melody insights
4. This is what it sounds like [play synthesised music]
5. Prominent tonic Resolves tension from musical movement Pervasive pentatonic scales Common in a lot of cultures: celtic, chinese, african, english, indian, etc 7 note scales, 12 semitones Latin, Chinese, Indian cultures have similar solfege Emotive melodies Lullabies, sing-alongs, march songs, festival songs, hymns Universality Natural phenomena observed around the world
6. Music is Math / Science / Art
7. Universality
8. Universality Natural phenomenons Prominent tonic Pervasive pentatonic scales 7 note scales, 12 semitones Emotive melodies Tonic | आधार षज - Resolves tension from musical movement. - Everyone knows when a piece of music returns to the tonic.
9. Pentatonic | औडुवम् Common in a lot of cultures: celtic, chinese, african, english, indian, etc hps://en.wikipedia.org/wiki/Pentatonic_scale#Pervasiveness Popular Bobby McFerrin demonstration of the power of the pentatonic scale hps://www.youtube.com/watch?v=ne6tB2KiZuk Universality Natural phenomenons Prominent tonic Pervasive pentatonic scales 7 note scales, 12 semitones Emotive melodies
10. Universality Natural phenomenons Prominent tonic Pervasive pentatonic scales 7 note scales, 12 semitones Emotive melodies 7 notes | स राः Latin: do re mi fa sol la si Chinese: shàng chě gōng fán liù wù yi Indian: sa ri ga ma pa da ni
11. Universality Natural phenomenons Prominent tonic Pervasive pentatonic scales 7 note scales, 12 semitones Emotive melodies Emotive melodies | रस Happy sing-alongs, lullabies, march songs, harvest songs, festival songs, hymns, funeral songs sound similar across cultures.
12. Carnatic music शाीय संगीतम्
13. Kalyani, Extempore MS Gopalakrishnan, Violin
14. Tanpura Veena Mridangam
15. Khamas, Thillana Abhishek Raghuram
16. Classical, art music - Not Tribal | Folk | Religious | Popular - Intent is artistic - Music itself is the sole focus Carnatic Music South Indian Classical Music Classical, art music Ancient, traditional music Melodic music Extempore, and compositions
17. Ancient, traditional music – Earliest treatise ना शा (Natya Shastra) at 500BC – लण (Lakshana Grantha): Rich tradition of Sanskrit grammar texts – Oral tradition, trained by exposure Carnatic Music South Indian Classical Music Classical, art music Ancient, traditional music Melodic music Extempore, and compositions
18. Carnatic Music South Indian Classical Music Classical, art music Ancient, traditional music Melodic music Extempore, and compositions Digitisation hype cycle
19. Carnatic Music South Indian Classical Music Classical, art music Ancient, traditional music Melodic music Extempore, and compositions Carnatic music Digitisation hype cycle
20. Melodic music - Has lile place for harmony - Emphasis on notes in succession - Explores melody through ragas and gamakas Carnatic Music South Indian Classical Music Classical, art music Ancient, traditional music Melodic music Extempore, and compositions
21. Extempore, compositions - Similar to jazz music - Renditions of century old compositions - Improvisations are generally around compositions Carnatic Music South Indian Classical Music Classical, art music Ancient, traditional music Melodic music Extempore, and compositions
22. Model fundamental abstractions Render prescriptive notation Model and synthesise gamaka Phase #1
23. Fundamental abstractions आधार षज, र, राग
24. Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo Adhara Shadja आधार षज | Tonic, pitch - Choice of the artist based on voice - Does not change for every song - Swaras are relative to this
25. (def adhaara-shadja {:.a 57 :.a# 58 :.b 59 :c 60 :c# 61 :db 61 :d 62 :d# 63 :eb 63 :e 64 :f 65 :f# 66 :gb 66 :g 67 :g# 68 :ab 68 :a 69 :a# 70 :bb 70 :b 71 :c. 72}) Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
26. - The 12 semitones in an octave - Solfege is sung, and used as notation - Elements of a raga Swara र | Note Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
27. Sa Ri Ga Ma Pa Da Sa Ni
28. Sa Ri Ga Ma Pa Da Sa Ni S R G M P D S N R1 R2 R3 G1 G2 G3 M1 M2 D1 D2 D3 N1 N2 N3 Notation Pronunciation Variations
29. (def madhya-sthayi-sthanams {:s 0 :r1 1 :r2 2 :r3 3 :g1 2 :g2 3 :g3 4 :m1 5 :m2 6 :p 7 :d1 8 :d2 9 :d3 10 :n1 9 :n2 10 :n3 11}) Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
30. (def sthayis {:mandra {:position :before :dots "." :difference -12} :madhya {:position :none :dots "" :difference 0} :thara {:position :after :dots "." :difference 12}}) Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
31. Prescriptive Notation s,,r g,p, d,s., n,d, p,dp mgrs rsnd s,,, Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
32. Raga राग | Melodic framework Kaapi, Extempore TM Krishna
33. Raga राग / Melodic framework - Melodic modes with added specialities - Grammar that a composition adheres to - Evokes a certain set of emotions Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
34. ragavardhini.ragams> (count ragams) 5280 ragavardhini.ragams> (into {} (take 2 ragams)) {:mayamalavagowla {:num 15 :arohanam [:s :r1 :g3 :m1 :p :d1 :n3 :s.] :avarohanam [:s. :n3 :d1 :p :m1 :g3 :r1 :s]} :gaula {:arohanam [:s :r1 :m1 :p :n3 :s.] :avarohanam [:s. :n3 :p :m1 :r1 :g3 :m1 :r1 :s] :parent-mela-name :mayamalavagowla :parent-mela-num 15}} Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
35. Scale | आरोह, अवरोह – Example: – Aarohanam: 1 2 3 5 6 8 – Avarohanam: 8 7 6 5 4 3 2 1 – They form a skeleton of the raga – Can be asymmetric, and non-linear Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
36. (:ragam (search/search-ragam “goula")) {:arohanam (:s :r1 :m1 :p :n3 :s.) :avarohanam (:s. :n3 :p :m1 :r1 :g3 :m1 :r1 :s) :name :gaula :parent-mela-name :mayamalavagowla :parent-mela-num 15} (play-arohanam-and-avarohanam (:ragam (search/search-ragam “goula"))) Lookup and play the scale Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
37. (play-phrase (phrase [:s :r2 :g3 :p :m1 :g3 :r2 :s] [ 1 1 1 1 1 1 2 4] (:lower kalams))) (play-phrase (phrase (:mechakalyani r/ragams) [:m :d :n :g :m :d :r :g :m :g :m :d :n :s.] [ 1 1 2 1 1 2 1 1 4 1 1 1 1 4] (:middle kalams))) Play a phrase in a raga Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
38. (play-notation (:bilahari r/ragams) "s,,r g,p, d,s., n,d, p,dp mgrs rs .n .d s,,,") (play-notation (:mayamalavagowla r/ragams) "s,,r g,p, d,s., n,d, p,dp mgrs rs .n .d s,,,") Play prescriptive notation Fundamentals आधार षज, र, राग Adhara Shadja / Tonic Swara / Note Raga / Melodic Framework Demo
39. Etymology in Sanskrit सक ् क ृ तम् (correctly done) = सं ृ तम् Specifies grammar for creating new words Words can be composed (समास / Samāsa) Elegant solution to the naming problem in soware
40. मेव राजते इत र - that which shines on its own - In the context of grammar, “swara” means vowel. A consonant in Sanskrit cannot be pronounced without a vowel. Etymology Independent Resonant Swara र | Note
41. ः: अनुरणनाक: तो रयत - that which shines on its own - resonant, unlike spoken words Sangita Ratnakara, Sarngadeva, 12 CE Swara र | Note
42. Raga / राग – colour: hue, tint, dye – emotion: desire, interest, anger, melancholy, joy, delight, yearning – More than a scale: phraseology / रागवाचक Raga राग / Melodic framework
43. More than a scale – A class of ragas has a phraseology / रागवाचक – Learning a raga is akin to learning a language by exposure (say a mother tongue) – A raga is absorbed Etymology Color, Emotion More than a scale Raga राग / Melodic framework
44. So far.. ..we’ve managed to play some prescriptive notation within the context of a raga
45. But.. ..that doesn’t sound like Carnatic music
46. Enter Melographs Me Machine
47. Enter Melographs Me Machine
48. Gamaka
49. छाया ुरा याम् रो य गमये What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR Gamaka गमक / Ornamentation Movement of a note from it’s pitch towards another so that the second passes like shadow over it. Sangita samayasaara, Parsvadeva, 12CE
50. - Embellishment, ornamentation - Shake, push, glide, flick - Pervasive in Carnatic music - Every swaram is a continuum What is it? Gamaka गमक / Ornamentation What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR
51. Sphuritam Jaaru What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR Gamaka गमक / Ornamentation
52. Kampitam Orikai What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR Gamaka गमक / Ornamentation
53. Prescriptive vs Descriptive What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR Gamaka गमक / Ornamentation
54. Prior art Gaayaka | Subramanian, 2009 | S, N D | N S R G | ((P S,,)) , ((S , S>>> S)) -((D. S. D)) ((S , S>> S))- S R ((G<< G , ,)) - Database of phrases - < and > increase and decrease pitch - ( and ) are speed factors; deeper is faster What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR Gamaka गमक / Ornamentation
55. PASR | Srikumar, 2013 - Pitch, Aack, Sustain, Release - Vector specifies the PASR vars for each note [["^pa:2" [[4 0 0 0] [2 2 0 0] [7 2 4 0]]] ["^ma1:2" [[5 0 8 0]]] ["^ga3:2" [[5 0 6 0] [4 0.5 0 0.5] [5 1 0 0]]] ["^ga3" [[5 0 0 0.5] [4 1 1 1] [5 0.5 0 0]]] ["ma1" [[5 0 0 0.5] [4 1 1 1] [5 0.5 0 0]]] ["^ri2:2" [[5 0 0 0.5] [4 1 1 1] [5 0.5 0 0.5] [2 1 1 1] [4 0.5 0 0]]] ["^ga3" [[4 0 0 0.5] [2 1 1 1] [[5 0.2] 0.5 0 0]]] ["ri2" [[[5 0.2] 0 0 0.5] [2 1 1 1] [4 0.5 0 0]]] ["^sa:4" [[0 0 16 0]]]] - Prior art Gamaka गमक / Ornamentation What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR
56. SSP | Sangita Samradaya Pradarshini संगीत सादाय दषन What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR Gamaka गमक / Ornamentation
57. Gamaka गमक / Ornamentation What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR
58. - Frequency envelopes (g-inst sphuritam-inst (envelope [f f lf f f] [ d4 d1 d1 d4] :welch)) (g-inst jaru-inst (envelope [pf f f] [ d9 d1] :welch)) Synthesis Gamaka गमक / Ornamentation What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR
59. Back to this… Me Machine (play-phrase (string->phrase (:mohana r/ragams) "^g, ~r, ^s ^r ^g ^r" 1))
60. Rendering PASR… Gamaka गमक / Ornamentation What is it? Kinds of gamaka Prescriptive vs Descriptive Prior art Synthesis Rendering PASR
61. Melody Insights #1 Prescriptive notation is insuicient for reproduction Raga as a framework for creating melodies is powerful Melographs make melodies tangible for observation Gamaka synthesis can be mathematically modelled as a curve
62. Generate a stream of Carnatic music Try to convert existing music into music data Study music data, look for paerns Infer characteristics of raga from music data Phase #2
63. Generation A naive approach
64. (defn gen-durations [] (let [m 16 n 8 sum (- m n)] (->> (repeatedly n #(rand-nth (range (inc sum)))) (cons sum) sort adjacent-differences (map inc)))) (defn random-swarams [swarams num] (repeatedly num #(rand-nth swarams))) (defn random-durations [jathi num] ;; [1, 2, 1, 1, 4, 1, 1, 2, 2, 4] (take num (flatten (repeatedly gen-durations)))) Generate random durations Pick swarams from a raga Generation A naive approach
65. Generate random durations Pick swarams from a raga Generation A naive approach (play-completely-random-phrase :mohana 4 100)
66. Transcription
67. Step 1. Build a massive database by scraping the internet Ragas, lyrics Ragas Compositions, Renditions Get data, study data Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
68. Get data, study data Step 2. Build a user interface to search songs and ragas, so that I can see what’s in the database. Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
69. Get data, study data Step 3. Download renditions of a given raga (->> "bhairavi" (db-search/renditions-in-ragam) (take 50) (download-renditions) hp://github.com/ssrihari/kosha Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
70. Get data, study data Step 4. Transcribe the fundamental frequencies of vocals Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
71. Get data, study data Step 5. Look at melographs, spectrograms, and try to find usable insights about melody. kalyANi-MS-Subbulakshmi-nidhi_cAla_sukhamA-tyAgarAja3.mpeg.wav.pitch.frequencies Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
72. Pitch histograms (defn pitch-histogram [filename] (let [frequencies (f/freqs-from-file filename) data-set (to-dataset frequencies) title (str "pitch histogram for " filename)] (with-data data-set (save (histogram (or plot-axis :freq) :density true :nbins 1200 :title title :x-label "frequency" :y-label "ocurrances") (str filename "-pitch-histogram.png") :width 1200 :height 300)))) Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
73. Pitch histograms (defn pitch-histogram [filename] (let [frequencies (f/freqs-from-file filename) data-set (to-dataset frequencies) title (str "pitch histogram for " filename)] (with-data data-set (save (histogram (or plot-axis :freq) :density true :nbins 1200 :title title :x-label "frequency" :y-label "ocurrances") (str filename "-pitch-histogram.png") :width 1200 :height 300)))) Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
74. kalyANi-MS-Subbulakshmi-nidhi_cAla_sukhamA-tyAgarAja3.mpeg Pitch histograms
75. Pitch histograms
76. MIDI Histogram Normalised MIDI Histogram (overtone.core/hz->midi frequency) (map #(mod % 12) frequencies)
77. Tonic identification Bellur, A., V. Ishwar, X. Serra, and H. A. Murthy (2012) A knowledge based signal processing approach to tonic identification in indian classical music. Bellur, A., and H. A. Murthy (2013) Automatic tonic identification in classical music using melodic characteristics and tuning of the drone. Srihari, S. (2016) * Pick the highest one, it mostly just works. * not really, no Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
78. Tonic identification (defn find-tonic-midi [freqs prominent-note] (let [midi-occurrances (midi-histogram freqs) prominent-midi-diff (->> midi-occurrances normalize-octaves (sort-by second >) ffirst)] (cond (= :s prominent-note) (+ 60 prominent-midi-diff) (= :p prominent-note) (+ 65 prominent-midi-diff) :else (+ 60 prominent-midi-diff)))) Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes
79. Get data, study data Pitch histograms MIDI histograms Tonic identification Swaram histograms Transcription Audio files to notes Tonic identification (defn find-tonic-midi [freqs prominent-note] (let [midi-occurrances (midi-histogram freqs) prominent-midi-diff (->> midi-occurrances normalize-octaves (sort-by second >) ffirst)] (cond (= :s prominent-note) (+ 60 prominent-midi-diff) (= :p prominent-note) (+ 65 prominent-midi-diff) :else (+ 60 prominent-midi-diff))))
80. Swaram Histogram Kalyani S, R2, G3, M2, P, D2, N3, S. S., N3, D2, P, M2, G3, R2, S
81. Kalyani S, R2, G3, M2, P, D2, N3, S. S., N3, D2, P, M2, G3, R2, S Revati S, R1, M1, P, N2, S. S., N2, P, M1, R1, S Mohana S, R2, G3, P, D2, S. S., D2, P, G3, R2, S
82. Generation Not so naive now
83. (defn get-next-swaram [swaram-allocations] (let [r (rand 100)] (->> swaram-allocations (filter (fn [[swaram [b e]]] (< b r e))) first first))) One swaram probabilities One swaram probabilities Two-swaram probabilities One vs two swarams Markov chains Generation
84. Random Swaram Weighted One swaram probabilities One swaram probabilities Two-swaram probabilities One vs two swarams Markov chains Generation
85. Melody Insights #2 Tonic is prominent Sa (tonic), and Pa (5th) have higher and sharper peaks Other note peaks are blunt Probabilities of all swarams in a raga are not the same Probabilities across octaves are not the same
86. Make generative music sound continuous Extract raga grammar, use it in generation How do we detect melodic paerns? Phase #3
87. Generation Continued…
88. Two swaram probabilities {:.s ([:.s 57.35530546623794] [:.r2 6.109324758842444] [:s 5.868167202572347] [:.d2 5.466237942122186] [:.p 5.42604501607717] [:.g3 3.215434083601286]…) :.r2 ([:.s 29.53020134228188] [:.r2 21.0738255033557] [:.g3 14.49664429530201] [:.p 6.577181208053691]…) :.p ([:.p 44.131214761660694] [:.d2 16.04305484366991] [:.g3 8.14966683751922]…) …} One swaram probabilities Two swaram probabilities One vs two swarams Markov chains Generation
89. Two swaram probabilities One swaram probabilities Two swaram probabilities One vs two swarams Markov chains Generation
90. One swaram probabilities Two swaram probabilities One vs two swarams Markov chains Generation (defn swaram-generator [swaram allocations] (lazy-seq (cons swaram (swaram-generator (get-next-swaram swaram allocations) allocations)))) Two swaram probabilities
91. One swaram probabilities Two swaram probabilities One vs two swarams Markov chains Generation One vs two swarams
92. One swaram probabilities Two swaram probabilities One vs two swarams Markov chains Generation Markov chains https://en.wikipedia.org/wiki/Markov_chain#Music https://github.com/rm-hull/markov-chains
93. One swaram probabilities Two swaram probabilities One vs two swarams Markov chains Generation Markov chains https://en.wikipedia.org/wiki/Markov_chain#Music https://github.com/rm-hull/markov-chains
94. Melody Insights #3 Adjacent swarams seem more melodious Sequencing per raga rules makes it sound beer Gamakas at this level give a Carnatic texture Sometimes, the in-between is worse than either extreme Higher order Markov chains do not make it sound beer
95. Back to the drawing board Explore rhythm concepts Explore repetition Try to generate melodies Phase #4
96. Laya, and Tala Temporal discipline
97. ुत: माता लय: पता Shruti is the mother, Laya is the father - Shruti: Pitch fidelity - Laya: Temporal discipline, or Tempo Laya, Tala Temporal discipline Etymology, Meaning Avartana Matra, Akshara, Gati
98. - Laya organizes music in time - A change in tempo has communicative weight Laya | लय Laya, Tala Temporal discipline Etymology, Meaning Avartana Matra, Akshara, Gati
99. TAla is the fundamental principle that binds Gita (song), Vadya (instrument) and Nria (dance). ताळ: तल थठयामत गीतम् वाम् तथानृं यथाालेततम् Sangita Ratnakara, Sarngadeva, 12 CE Laya, Tala Temporal discipline Etymology, Meaning Avartana Matra, Akshara, Gati
100. Tala | ताळ - A recurrent time frame, a cycle of beats - Derived from poetic meter, rather than dance - Hand gestures for conduction Laya, Tala Temporal discipline Etymology, Meaning Avartana Matra, Akshara, Gati
101. Tala Systems - Navasandhi talas (108) - Chapu talas (4) - Suladi Sapta talas (175) Laya, Tala Temporal discipline Etymology, Meaning Avartanam Matra, Akshara, Gati
102. Avartana | आवतन - avartana: 1 cycle - signifies number of beats in 1 cycle - simhanandana tala: 128 beats per cycle - sharaba nandana tala: 79 beats per cycle Laya, Tala Temporal discipline Etymology, Meaning Avartana Matra, Akshara, Gati
103. Kriya, Anga, Jati - Anga: A subdivision of the tala - Kriya: The gesture used to refer to an anga - Jati: The number of matras in a laghu Laya, Tala Temporal discipline Etymology, Meaning Avartanam Kriya, Anga, Jati Matra, Akshara, Gati
104. Matra, Akshara, Gati - Matra: beat (within an avartana) - Akshara: division of beat (within a matra) - Gati: gait; factor dividing matra into akshara Laya, Tala Temporal discipline Etymology, Meaning Avartana Matra, Akshara, Gati
105. Transcription based on laya and gati
106. Original Record Gati: 1 Gati: 2 Gati: 4 Gati: 8 Transcription Based on laya and gati A clinical process Detail vs structure trade-os Varna and Alankara Removing outliers
107. Gati: 2 Gati: 8 Melodic structure is visible at 1/2 a matra / beat Embellishments / Gamakas are visible at 1/8th Transcription Based on laya and gati A clinical process Detail vs structure trade-os Varna and Alankara Removing outliers
108. Transcription Based on laya and gati A clinical process Detail vs structure trade-os Varna and Alankara Removing outliers Varna and Alankara - Tonal paerns at matra (beat) level is called varna - Embellishments at akshara (sub-beat) level is called alankara
109. Removing outliers - Short-lived frequencies (< akshara/2) - Non prominent swaras in raga - Low and high cutos for unnatural frequencies - Octave shis, and continuous tonics - Detecting rests Transcription Based on laya and gati A clinical process Detail vs structure trade-os Varna and Alankara Removing outliers
110. Generation Last time, promise
111. Revisiting Markov Chains - Transcribing with input BPM improves quality - Markov chains at multiple levels of granularity - So many knobs/toggles to play with Revisiting Markov chains At matra, akshara levels Embellishment chains Aavrii Generation
112. (markov/generate-and-play mohanam-swarams {:order 12 :duration-seconds 100 :playback-gati 2 :generation-gati 4 :mode :discrete :bpm 80}) (markov/generate-and-play mohanam-swarams {:order 22 :duration-seconds 100 :playback-gati 10 :generation-gati 8 :mode :continuous :bpm 80}) Revisiting Markov chains At matra, akshara levels Embellishment chains Aavrii Generation
113. Embellishment chains - Filling matra chains with alankara chains fails - Gamakas are contextual, and have their own laya Revisiting Markov chains At matra, akshara levels Embellishment chains Aavrii Generation
114. Revisiting Markov chains At matra, akshara levels Embellishment chains Aavrii Generation Repetition / Avartana Organic repetition seems hard to model, I’m on it.
115. Melody Insights #4 Time is fundamental to melody, and inseparable from frequency Humans perceive more than a beat when hearing a beat Machines understand tonal structures at 1/2 a beat Markov chains don’t work for embellishments Simplest form of repetition can be powerful
116. By-products
117. Using a simple goodness of fit test By-product #1 Raga Identification (defn raga-diff [base-osp sample-osp] (let [ordered-vals (fn [osp] (vals (sort-by first < osp))) probs (ordered-vals base-osp) table (ordered-vals sample-osp)] (chisq-test :table table :probs (map #(* 0.01 %) probs))))
118. (defn raga-diff [base-osp sample-osp] (let [ordered-vals (fn [osp] (vals (sort-by first < osp))) probs (ordered-vals base-osp) table (ordered-vals sample-osp)] (chisq-test :table table :probs (map #(* 0.01 %) probs)))) Using a simple goodness of fit test By-product #1 Raga Identification
119. By-product #2 Useful in education, and preserving music Automatic notation, playback (let [props {:bpm 87 :gati 2 :offset 60} swaras (bpm-transcribe (first samples/research-files))] (prescriptive-notation (get swaras (:gati props)) 16)) (“.s .r .g .p .d , s , .d .p .g , " ".r .s , .r , .g .p , .d , s , .d" " .p , .g , .r , .s .r .g .p , .d" " s , .d .p , .g .r .s .r .g .p ," " .d s , , .d .p , .r .s .r .d s " ".d .p .g .r .g s .p .g .p s .p .”)
120. By-product #3 An electronic rhythm accompanist Electronic tala assistant (def talas {:eka [:I] :rupaka [:O :I] :jhampa [:I :U :O] :triputa [:I :O :O] :matya [:I :O :I] :ata [:I :I :O :O] :dhruva [:I :O :I :I]}) (play-avartanams 60 4 (->> (tala-accents :chatusra :rupaka) #(mapcat combo) (repeatedly 10)))
121. Is this music though? Is it art?
122. Behag, Dasarapada Abhishek Raghuram
123. References Sangita Ratnakara of Sarngadeva – RK Shringy, Prem Lata Sharma Sangita Sampradaya Pradarsini – SRJ, NR, RSJ Brhaddesi of Sri Matanga Muni – Prem Lata Sharma Appreciating Carnatic Music – Lakshmi Sreeram South Indian Music – P. Sambamurthy Compmusic – MTG, Barcelona, Xavier Serra Gayaka - Subramanian Srikumar’s PASR thesis
124. Making machines that make music ीहर ीरामन् नलेो Srihari Sriraman nilenso
125. Shruti ुत Etymology Intervals in indian music ूयते इत ुत: - that which should be heard - the vedas, tanpura - that which can be heard - the word shruti probably means an interval here