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Analyzed about 7 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
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Section 1223 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1226 updated
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
Section 1223 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1223 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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+--------------------------------------------------------------------------------------------------------------------------------------------- +1226. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Maximum Population Density and Area-Population Ratio as arguments - 12,13,14,15 +March 2022 - related to 135,675,753,1150,1174,1175,1177,1179,1194,1211,1212,1225 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH Urban Growth Models, Preferential attachment,Random Graphs,Dynamic Graphs,NeuronRain CoronaVirus 2019 analyzer (R and Python - GEM,R Logit,Chaotic ERSIR Cellular Automaton Graph),Chaos,Central Limit Theorem,Cybercrime analytics,Ramsey Theory,Integer and Real Factorization +---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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--------------------------------------------------------------------------------------------------------------------------------------------- 1226. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Maximum Population Density and Area-Population Ratio as arguments - 12,13,14,15 March 2022 - related to 135,675,753,1150,1174,1175,1177,1179,1194,1211,1212,1225 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata-SLEUTH Urban Growth Models, Preferential attachment,Random Graphs,Dynamic Graphs,NeuronRain CoronaVirus 2019 analyzer (R and Python - GEM,R Logit,Chaotic ERSIR Cellular Automaton Graph),Chaos,Central Limit Theorem,Cybercrime analytics,Ramsey Theory,Integer and Real Factorization --------------------------------------------------------------------------------------------------------------------------------------------- 1226.1 GISUrbanSprawlAnalytics.py has been refactored to simplify population density computation clauses - with legend and without legend (or if color coding legend range is huge, proportionate scaling of population density by pixel color is inevitable) - by removing hardcoded values for maximum population density (~100000) and urban sprawl to population ratio heuristic (~6.22) and replacing them by function arguments to urban_sprawl_from_segments() - maximum_population_density and sqkmareatopopulationratio 1226.2 NASA SEDAC 2030 Urban Sprawl Expansion GIS projection in previous section has been reanalyzed for a fictitious maximum population density of 100000 and urban sprawl to population ratio heuristic of 6.22. There are no changes to contour-segment-convex hull visuals - logs of recomputed population estimates are at python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.15March2022. 1226.3 NeuronRain UGM (Urban Growth Model) is based on One Way Dynamic Graph upward growth of an Urban Sprawl FaceGraph by attaching faces (or composing ears as countermagnets) along its circumference, a generalization of widely used Cellular Automaton square grid expansion models (SLEUTH) to arbitrary shape cells - Cellular automata UGMs expand by adding square faces to periphery of an urban sprawl CA grid while NeuronRain Dynamic FaceGraph Urban Growth Model expands by annexing faces of arbitrary shapes to periphery of urban extent. 1226.4 High Population Density faces along the boundaries and interiors of an urban sprawl FaceGraph are most likely to expand in future to adjoining and new peripheral faces. Parallel Algorithm for Cellular Automaton Fluid Dynamics in 135 is of uncanny similarity to CA Urban Growth Models - Fluid dynamics is replaced by Population dynamics. 1226.5 Cellular Automaton Urban Growth Models are exactly instances of CAGraph model of Pandemics/Memes/Opinions/Fads/Cybercrimes in Social and Electronic networks - each face of CAGraph is a square. 1226.6 Cellular Automata Grid of Square cells of an urban sprawl is a minimum bounding box rectangle of area (in contrast to minimum enclosing circle of an urban sprawl) equal to urban sprawl contour area extent sides of which are found by factorization (integer or real). While a real factorization of urban area into sides of a square is trivial by square root algorithm, integer factorization of an urban contour area into sides of an almost-square is non-trivial (a semiprime factorization of almost equal factor pairs). E.g factorization of an area 105 as 105=15*7 is less rectangular than 105=3*35 as ratio of the factors is closer to 1 in the former. 1226.7 Minimum enclosing circle of an urban area is trivially a variant of real square root algorithm - urban sprawl is enclosed in a circle of radius = sqrt(contour_area / 3.1428)
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1225 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1223 Updated (2/2)
Krishna iResearch - NeuronRain - K.Srinivasan
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1223. (THEORY and FEATURE) Music Information Retrieval - AI Music Synthesis from Sum of Damped Sinusoids - 4 March 2022 - related to 3,298,642,643,644,659,681,827,829,1124,1144,1189,1218,1219,1220 and all sections on Merit of Music - MIR, Novelty Detection - Original Music Score, Music clustering, Dynamic Time Warping, Timeseries analysis, AI Music Synthesis, Weighted Automata Learning from Music waveform notes, Speech Synthesis, Fourier analysis, ProofNets, Girard Geometry of Interaction, Logic, Intrinsic merit of academic publications - Bibliometrics, Embedding music on vectorspace, Shell Turing Machines and Kernel Lifting, People Analytics-Unique identification
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1223 updated - Original Music Score,Novelty Detection and Copyright Infringement - No music is new - there are only 68 billion 12-note melodies
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1220 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 272 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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--------------------------------------------------------------------------------------------------------------------------------------------- 1224. (THEORY and FEATURE) Music Information Retrieval - AI Music Synthesis from Sum of Damped Sinusoids - 8 March 2022 - related to 3,298,642,643,644,659,681,1124,1218,1219,1223 and all sections on Merit of Music - MIR, Novelty Detection - Original Music Score, Music clustering, Dynamic Time Warping, Timeseries analysis, AI Music Synthesis, Weighted Automata Learning from Music waveform notes, Speech Synthesis, Fourier analysis --------------------------------------------------------------------------------------------------------------------------------------------- --------------------------------------------------------------------------------------------------------------------------------------------- 1225. (THEORY and FEATURE) GIS Urban Sprawl Analytics - SEDAC Urban Expansion Probabilities Projection for Year 2030 - a casestudy of urban sprawls - 8 March 2022 - related to 675,753,1174,1175,1177,1179,1194,1211,1212 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata, Preferential attachment,Random Graphs,Dynamic Graphs ---------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1225. (THEORY and FEATURE) GIS Urban Sprawl Analytics - SEDAC Urban Expansion Probabilities Projection for Year 2030 - a casestudy of urban sprawls - 8 March 2022 - related to 675,753,1174,1175,1177,1179,1194,1211,1212 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from electricity consumption, Jordan Curve Theorem, Weirstrass Theorem, Cellular Automata, Preferential attachment,Random Graphs,Dynamic Graphs --------------------------------------------------------------------------------------------------------------------------------------------- 1225.1 GISUrbanSprawlAnalytics.py has been updated to compute Voronoi tessellation facegraph,Delaunay triangulation,convex hull,contour segments and minimum enclosing circles of SEDAC Urban Expansion Probabilities Projection for Year 2030 (Southern India) - SEDAC2030UrbanExpansionProbabilities.jpg - which is a cellular automaton square grid raster of urban extent expansion probabilities in year 2030 proportionately coded by color legend. 1225.2 Color legend is of size 100 for SEDAC Urban Expansion 2030 GIS and ranges from light to dark corresponding to 0% to 100% probabilities of urban expansion. For this reason, default grading legend averageBGR/255 * 100000 (e.g Barcelona in section 1211) has been used to compute population density. 1225.3 As mentioned in Section 1177, Urban Sprawl Expansion is the dynamic graph growing problem of adding new faces (or composing ears to create new faces) to Urban sprawl facegraph perimeter,a graph theoretic version of preferential attachment. Voronoi-Delaunay Facegraph of SEDAC Urban Expansion Probabilities Projection for Year 2030 computed earlier is a random graph wherein each ear or face is added to the periphery of an urban sprawl with probability assigned by color legend of the grid. Conventional Dynamic Graphs allow dynamic add-delete of vertices and edges while Urban Sprawl Expansion by face-ear composition along the circumference is an one-way Dynamic Graph and irreversible - only addition of edges and vertices are allowed and face of a suburb and its vertices-edges can't be deleted from an expanded urban sprawl. 1225.4 Most Urban area expansions in South India example previously have been colored in the probability range of 50% (plus or minus) and a projected emergence of megalopolises in 2030 is conspicuous in coastal urban areas of TN (tremendous westward and southward expansion of Chennai Metropolitan Sprawl upto Vellore-Puducherry and gradual coalescence of Puducherry urban sprawl-Cauvery river delta urban sprawl which could be another pearl river delta in the making) and entire west coast of Kerala. 1225.5 In terms of realestate, Light shaded colors might indicate a horizontal residential growth while dark shaded regions might witness a vertical growth of skyscrapers. 1225.6 Following are the images (contours,segments,convex hull,facegraph,Voronoi-Delaunay tessellation,minimum enclosing circles of UAs) and logs for this commit: testlogs/GISUrbanSprawlAnalytics.log.8March2022 testlogs/RemoteSensingGIS/SEDAC2030UrbanExpansionProbabilities_FaceGraph.jpg testlogs/RemoteSensingGIS/SEDAC2030UrbanExpansionProbabilities_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/SEDAC2030UrbanExpansionProbabilities_contour_segmented.jpg testlogs/RemoteSensingGIS/SEDAC2030UrbanExpansionProbabilities_convex_hull_segmented.jpg testlogs/RemoteSensingGIS/SEDAC2030UrbanExpansionProbabilities_segmented.jpg testlogs/SEDAC2030UrbanExpansionProbabilities-contourlabelled.jpg testlogs/SEDAC2030UrbanExpansionProbabilities.jpg
Krishna iResearch - NeuronRain - K.Srinivasan
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--------------------------------------------------------------------------------------------------------------------------------------------- 1224. (THEORY and FEATURE) Music Information Retrieval - AI Music Synthesis from Sum of Damped Sinusoids - 8 March 2022 - related to 3,298,642,643,644,659,681,1124,1218,1219,1223 and all sections on Merit of Music - MIR, Novelty Detection - Original Music Score, Music clustering, Dynamic Time Warping, Timeseries analysis, AI Music Synthesis, Weighted Automata Learning from Music waveform notes, Speech Synthesis, Fourier analysis --------------------------------------------------------------------------------------------------------------------------------------------- 1224.1 Some additional audacity music waveform files (.aup and .au) synthesised by AudioToBitMatrix.py from periodic mathematical functions have been added to repositories - simulate an instrumental music
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 420 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 572 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Updated Section 1195
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1175 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1137 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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1223. (THEORY and FEATURE) Music Information Retrieval - AI Music Synthesis from Sum of Damped Sinusoids - 4 March 2022 - related to 3,298,642,643,644,659,681,1124,1218,1219,1220 and all sections on Merit of Music - MIR, Novelty Detection - Original Music Score, Music clustering, Dynamic Time Warping, Timeseries analysis, AI Music Synthesis, Weighted Automata Learning from Music waveform notes, Speech Synthesis, Fourier analysis
Krishna iResearch - NeuronRain - K.Srinivasan
More... over 4 years ago
------------------------------------------------------------------------------------------------------------------------------------------- 1223. (THEORY and FEATURE) Music Information Retrieval - AI Music Synthesis from Sum of Damped Sinusoids - 4 March 2022 - related to 3,298,642,643,644,659,681,1124,1218,1219,1220 and all sections on Merit of Music - MIR, Novelty Detection - Original Music Score, Music clustering, Dynamic Time Warping, Timeseries analysis, AI Music Synthesis, Weighted Automata Learning from Music waveform notes, Speech Synthesis, Fourier analysis ------------------------------------------------------------------------------------------------------------------------------------------- 1223.1 notes_to_audio() function in AudioToBitMatrix.py has been changed to synthesize notes from python 3.8 lambda expression and map() instead of a list comprehension earlier. 1223.2 Lambda expression for any mathematical function is repetitively evaluated within a loop and notes are appended to get an onset-to-onset periodic music waveform timeseries. 1223.3 There has been sufficient publication prior art analyzing feasibility of speech synthesis from summation of damped sinusoid waveforms for simulating human utterances of syllables. Damped Sinusoid summation has been implemented for music synthesis equivalent in this commit. In a sense, summation of damped sinusoids is the Time domain converse of Fourier spectrum decomposition of music waveform into set of frequencies in Frequency domain. 1223.4 notes_to_audio() synthesizer is invoked for a sum of damped sinusoids and an instrumental music waveform simulating thunderbolt has been composed (function_synthesized_music.wav): notes_to_audio(function='((np.iinfo(np.int16).max/(1+x))*math.sin(2*3.1428/720*x) + (np.iinfo(np.int16).max/(1+x))*math.sin(2*3.1428/1240*x) + (np.iinfo(np.int16).max/(1+x))*math.sin(2*3.1428/2400*x))', fractal=False) corresponding to sum of three damped oscillator waveform frequencies of the form a/(b + x)*sin(2*pi*x/c) 1223.5 Earlier summation combines 3 damped sinusoids of frequencies 720,1240 and 2400 Hz into a natural rhythmic music. Music waveforms (especially orient) have a distinct periodic damping feature (notable difference could be visually felt by loading testlogs/054-SBC-Aanandhamridhakarshini.mp4 and testlogs/Bach_Flute_Sonata_EFlat.mp4 in Audacity - former has an intriguingly self-similar periodic damped sinusoidal structure) 1223.6 There have been raging debates on credibility of music synthesis and an orthodox view avers that music cannot be invented but could only be discovered (a notion strikingly similar to "Straight from the Book" of Erdos - Theorems are not invented but only discovered). As a trivia, Permutation of heptatonic scale allows only 5040 waveforms of enjoyable music (7!) discarding major,minor,flat,sharp variants.
Krishna iResearch - NeuronRain - K.Srinivasan
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1221. (THEORY and FEATURE) Fictitious Neuro Cryptocurrency NP-Hard Proof of Work - Moving Mount Fuji - Single Bin Sorted LIFO Histogram - Tow ers of Hanoi - implementation - 28 February 2022 - related to 112,113,114,488,751,784,785,787,791,841,848,859,1095(a,b,c,d),1209,1215,1216,121 7 and all sections on Economic merit, Set Partition analytics, Pseudorandom generators, Computational Geometric Factorization, Money Changing Problem - Frobenius Coin Diophantines, Computational Theory of Value, Poverty Alleviation, Neuro Cryptocurrency implementations in AstroInfer and KingCobra, Optimal Denominations,Computational Economics - Computational Hardness (Labour) Theory of Value, Histogram and Set Partition an alytics, Equidepth-Equiheight Histograms, Separate Chaining Hashtables, Timeout gadgets (Refcounted Expirable C++ objects, KingCobra atomic re fcounting and Survival Index Timeout) in NeuronRain, Digital Watermarking, Drone Electronic Voting Machines, Numeric Compression by Prime powe r encoding - Unique Factorization Domain, Multiplicative Partitions, Compressed Sensing (Succinct representation), Large Scale Visuals, Cyberc rime analytics, Russian Peasant's Linear Complexity Multiplication Algorithm, K Dimensional Trees, Computer Graphics, Collatz Conjecture, Mand elbrot sets, Earth Mover Distance, Graphical Event Models and Stock Timeseries, AI Music Synthesis
Krishna iResearch - NeuronRain - K.Srinivasan
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-------------------------------------------------------------------------------------------------------------------------------------------- 1221. (THEORY and FEATURE) Fictitious Neuro Cryptocurrency NP-Hard Proof of Work - Moving Mount Fuji - Single Bin Sorted LIFO Histogram - Towers of Hanoi - implementation - 28 February 2022 - related to 112,113,114,488,751,784,785,787,791,841,848,859,1095(a,b,c,d),1209,1215,1216,1217 and all sections on Economic merit, Set Partition analytics, Pseudorandom generators, Computational Geometric Factorization, Money Changing Problem - Frobenius Coin Diophantines, Computational Theory of Value, Poverty Alleviation, Neuro Cryptocurrency implementations in AstroInfer and KingCobra, Optimal Denominations,Computational Economics - Computational Hardness (Labour) Theory of Value, Histogram and Set Partition analytics, Equidepth-Equiheight Histograms, Separate Chaining Hashtables, Timeout gadgets (Refcounted Expirable C++ objects, KingCobra atomic refcounting and Survival Index Timeout) in NeuronRain, Digital Watermarking, Drone Electronic Voting Machines, Numeric Compression by Prime power encoding - Unique Factorization Domain, Multiplicative Partitions, Compressed Sensing (Succinct representation), Large Scale Visuals, Cybercrime analytics, Russian Peasant's Linear Complexity Multiplication Algorithm, K Dimensional Trees, Computer Graphics, Collatz Conjecture, Mandelbrot sets, Earth Mover Distance, Graphical Event Models and Stock Timeseries, AI Music Synthesis -------------------------------------------------------------------------------------------------------------------------------------------- 1221.1 Streaming_SetPartitionAnalytics.py has been changed to define a new function single_bin_sorted_LIFO_histogram_ToH() for computing all order preserving moves of a single bin sorted LIFO histogram (a k-d tree or mountain sliced to horizontal disks in 3 dimensions) as Neuro Cryptocurrency Rig Proof of Work - Towers of Hanoi (or Towers of Brahma) - which is NP-Hard (not NP-Complete as no NP algorithm for Towers of Hanoi problem has been found yet). 1221.2 Set Partitions to Lagrange Sum of Squares Tile Cover of a Rectangle reduction + Computational Geometric Factorization + Money Changing Problem-Integer Linear Programming Proof of Work implementation earlier is NP-Complete and its hardness is subject to P != NP while NP-Hard class is independent of P != NP and universally present as even undecidable problems are NP-Hard (Halting problem). 1221.3 Function single_bin_sorted_LIFO_histogram_ToH() internally invokes print_Towers_of_Hanoi() routine recursively for n disks and 3 bins (source,destination and intermediary) and computes Earth mover distance between those triples after every move. Earth mover distance triples and 3 Towers are printed to logs testlogs/Streaming_SetPartitionAnalytics.TowersOfHanoi.NeuroProofOfWork.28February2022 after computing pairwise wasserstein_distance() triples for all O(2^n) moves. 1221.4 Towers of Hanoi algorithm is based on following pseudocode 2-part recursion for moving top n-1 disks from source x to destination z and source z to destination y having y and x as intermediaries respectively: single_bin_sorted_LIFO_histogram_ToH(n-1,x,z,y) single_bin_sorted_LIFO_histogram_ToH(n-1,z,y,x) 1221.5 Earth Mover Distance Triples printed sinusoidally fluctuate and always end in 0 exhibiting Collatz sequence behavior and are thus simulators for many naturally arising timeseries Graphical Event Model sequences - bidirectional object refcounting clockticks, Stock quote tickers et al. Stock quote timeseries influenced by high frequency algorithmic trading and manual trading platforms have been simulated by multifractals, Mandelbrot sets, Irrational exuberance and Bounded rationality. Undecidability of Mandelbrot sets implies prediction of market volatility is intractable. Towers of Hanoi and Earth Mover Distance are personifications for any human labour involving mass movement and formalize hardness in terms of simulated labour. Rhythmic ebb and tide of Towers of Hanoi EMD sequence makes it a candidate music waveform for AI Music Synthesis and the undulating histograms printed in intermediate steps are corresponding spectrograms of it. Following are the set of EMD triples for last few Towers of Hanoi POW computation steps grepped from testlogs/Streaming_SetPartitionAnalytics.TowersOfHanoi.NeuroProofOfWork.28February2022 ending in (0,0,0) for 3 towers: Pairwise ToH Earth Mover Distance Triple: (4.166666666666667, 2.0, 3.166666666666667) Pairwise ToH Earth Mover Distance Triple: (5.5, 2.5, 3.0) Pairwise ToH Earth Mover Distance Triple: (5.000000000000001, 0, 0) Pairwise ToH Earth Mover Distance Triple: (4.5, 2.0, 6.5) Pairwise ToH Earth Mover Distance Triple: (3.5, 2.5, 5.714285714285714) Pairwise ToH Earth Mover Distance Triple: (3.25, 0, 0) Pairwise ToH Earth Mover Distance Triple: (3.25, 1.0, 3.75) Pairwise ToH Earth Mover Distance Triple: (4.214285714285714, 1.5, 4.000000000000001) Pairwise ToH Earth Mover Distance Triple: (5.0, 1.0, 5.0) Pairwise ToH Earth Mover Distance Triple: (3.5, 2.0, 5.5) Pairwise ToH Earth Mover Distance Triple: (0, 0, 5.000000000000001) Pairwise ToH Earth Mover Distance Triple: (0, 0, 4.0) Pairwise ToH Earth Mover Distance Triple: (4.5, 1.0, 3.75) Pairwise ToH Earth Mover Distance Triple: (5.5, 1.5, 4.000000000000001) Pairwise ToH Earth Mover Distance Triple: (5.0, 0, 0) Pairwise ToH Earth Mover Distance Triple: (4.5, 1.0, 5.5) Pairwise ToH Earth Mover Distance Triple: (0, 0, 5.0) Pairwise ToH Earth Mover Distance Triple: (0, 0, 0) 1221.6 Function single_bin_sorted_LIFO_histogram_ToH() prints a digital-watermarked Visual Neuro currency after completion of Towers of Hanoi proof of work. Following are the Neuro currency visuals for an example denomination: testlogs/NeuroCryptoCurrency.jpg testlogs/NeuroCryptoCurrency.jpg_Watermarked.jpg
Krishna iResearch - NeuronRain - K.Srinivasan
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Section 1218 updated
Krishna iResearch - NeuronRain - K.Srinivasan
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1220. (THEORY and FEATURE) Music Information Retrieval (MIR) - Learning a polynomial from music waveform - 25 February 2022 - related to 3,298,642,643,644,659,681,1124,1218,1219 and all sections on Merit of Music - MIR, Novelty Detection - Original Music Score, Music clustering, Dynamic Time Warping, Timeseries analysis, AI Music Synthesis, Weighted Automata Learning from Music waveform notes
Krishna iResearch - NeuronRain - K.Srinivasan
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-------------------------------------------------------------------------------------------------------------------------------------------- 1220. (THEORY and FEATURE) Music Information Retrieval (MIR) - Learning a polynomial from music waveform - 25 February 2022 - related to 3,298,642,643,644,659,681,1124,1218,1219 and all sections on Merit of Music - MIR, Novelty Detection - Original Music Score, Music clustering, Dynamic Time Warping, Timeseries analysis, AI Music Synthesis, Weighted Automata Learning from Music waveform notes -------------------------------------------------------------------------------------------------------------------------------------------- 1220.1 Function audio_to_notes() in AudioToBitMatrix.py has been updated to learn a degree 5 polynomial from raw music waveform timeseries returned by librosa. NumPy polyfit() is invoked on the music waveform notes array to fit a degree 5 polynomial. 1220.2 Theoretically distance between 2 music waveform timeseries found by Dynamic Time Warping and inner product distance between polynomials of 2 music waveforms f and g - <f,g>=DefiniteIntegral(f(x)g(x)dx) must coincide or be proportional. Novelty or Originality or Similarity of a music score is therefore quantified algebraically though somewhat superficial ignoring the graph structure found by Weighted Automata. 1220.3 Exception in hertz to note function - librosa.hz_to_note() - of librosa has been handled. 1220.4 Following 3 instrumental MP4 music clips of different genres (MP3 files have been upgraded to MP4 as librosa fails to load MP3) - Orient and Western classical - JS Bach and S Balachander in Veena (Amritavarshini) - have been fit to music waveform polynomials and printed to testlogs/AudioToBitMatrix.log.PolyFit.25February2022: notes1 = audio_to_notes("testlogs/JSBach_Musicological_Offering.mp4",dur=20) notes2 = audio_to_notes("testlogs/054-SBC-Aanandhamridhakarshini.mp4", dur=20) notes3 = audio_to_notes("testlogs/Bach_Flute_Sonata_EFlat.mp4", dur=20) 1220.5 Following are the coefficients of the degree 5 polynomials learnt for clips earlier in order: polynomial learnt from raw audio-music waveform: [ 1.86408187e-29 -2.44250264e-23 1.12197649e-17 -2.12257382e-12 1.52604679e-07 2.37511803e-03] polynomial learnt from raw audio-music waveform: [ 3.26195558e-31 -3.39615870e-25 1.33295412e-19 -2.28165048e-14 1.22378846e-09 -6.05635122e-05] polynomial learnt from raw audio-music waveform: [-9.67130004e-32 2.01499959e-26 3.51124576e-20 -1.43713102e-14 1.59537748e-09 -4.41037145e-05]
Krishna iResearch - NeuronRain - K.Srinivasan
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-------------------------------------------------------------------------------------------------------------------------------------------- +1219. (THEORY and FEATURE) Music similarity - Dynamic Time Warping Waveform Distance algorithm implementation - 23 February 2022 - related to 815,1218 and all sections on Intrinsic merit of music, Novelty Detection - Original Music Score, Music clustering, Merit of audio-visuals, Physique recognition, Timeseries analysis - Stock quote tickers-Granger causality, Computer Graphics - Trimesh-Quadmesh Deformations, Face Recognition, Multiple Sequence Alignment, Graphical Event Models +--------------------------------------------------------------------------------------------------------------------------------------------
Krishna iResearch - NeuronRain - K.Srinivasan
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-------------------------------------------------------------------------------------------------------------------------------------------- 1219. (THEORY and FEATURE) Music similarity - Dynamic Time Warping Waveform Distance algorithm implementation - 23 February 2022 - related to 815,1218 and all sections on Intrinsic merit of music, Novelty Detection - Original Music Score, Music clustering, Merit of audio-visuals, Physique recognition, Timeseries analysis - Stock quote tickers-Granger causality, Computer Graphics - Trimesh-Quadmesh Deformations, Face Recognition, Multiple Sequence Alignment, Graphical Event Models -------------------------------------------------------------------------------------------------------------------------------------------- 1219.1 AudioToBitMatrix.py has been updated to define a new function dynamic_time_warping() implementing Dynamic Time Warping (DTW) Timeseries sequence similarity algorithm that could find use as broad-spectrum routine across whole gamut of sequence similarity analytics - stock quote timeseries, trimesh-quadmesh sequences of human physiques from motion captures of CCTV footages, DNA multiple sequence alignment, string matching - longest common subsequence among others. Granger causality (timeseries of stockA causing timeseries of another stockB separated by time duration) between two stock quote time series could be found by DTW similarity across timelines and Graphical Event Model of stocks could be established. 1219.2 Reference pseudocode from https://en.wikipedia.org/wiki/Dynamic_time_warping has been translated to NumPy Python 3.8 function dynamic_time_warping() - in a nutshell this function grows a dynamic programming memoization tableau and populates it by cost minimization function: dtw[i, j] = cost + min([dtw[i-1, j], dtw[i, j-1], dtw[i-1, j-1]]) 1219.3 Two example audio clips - ./testlogs/JSBach_Musicological_Offering.mp4, ./testlogs/AudioGraphExample_SpeechRecognition_2019-07-09-103018.wav have been compared by Dynamic Time Warping and logs are available at python-src/music_pattern_mining/testlogs/AudioToBitMatrix.log.DynamicTimeWarping.23February2022 1219.4 Function audio_distance() has been changed to include a clause and boolean flag argument for enabling dynamic time warping. 1219.5 Earlier audio clips have been curtailed to 0.01 duration each as np.array() from librosa returned notes are memory hogging (requires few GBs). DTW is a music clustering distance metric quite apt to define creativity and original music score - pattern or style in one's own work and dissimilarity to the rest - a foremost prerequisite for Novelty Detection.
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-------------------------------------------------------------------------------------------------------- +629. (THEORY) Complement Diophantine Equations over Complex Numbers and Algebraic/Arithmetic Circuits for them, Blum-Shub-Smale Turing Machines, Thue and Siegel Equations, Elliptic Curves, Mordell Conjecture and Theorem, ABC Conjecture, Riemann Zeta Function,Chaos,Integer Factorization,Multiplicative Partitions - 9 January 2019,12 April 2020,14 November 2021,23 February 2022 - related to 24,490,617 +--------------------------------------------------------------------------------------------------------
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