import jinja2
tuned = Corpus(
"/Users/james/Cloud/Projects/ElectroMagnetic/reaper/Convolutions/anchors/media/07-glued.wav"
)
db = (Corpus("~/Cloud/Projects/DataBending/DataAudioUnique").duration(
min_duration=2, max_duration=20))
unstatic = Corpus(
"~/Cloud/Projects/ElectroMagnetic/outputs/classification/4_Split/0")
static = Corpus(
"~/Cloud/Projects/ElectroMagnetic/outputs/classification/4_Split/1")
output = "../../reaper/Convolutions/tuned"
analysis = Chain(source=(db + tuned + unstatic + static), folder=output)
kdtree = KDTree()
dr = UMAP(components=10, cache=1) # we need access to the original data
analysis.add(
# FluidMFCC(discard=True, numcoeffs=20, fftsettings=[4096, -1, -1], cache=1),
LibroCQT(cache=0),
Stats(numderivs=1, flatten=True, cache=1),
Standardise(cache=1),
dr,
kdtree)
if __name__ == "__main__":
analysis.run()
pinpoint = tuned.items[0] # single item
# Description
"""
Split by activity into two clusters hopefully rendering sections that are static, versus those that are more gestural or dynamic
"""
from ftis.analyser import Flux, Stats, Normalise, AgglomerativeClustering
from ftis.process import FTISProcess as Chain
from pathlib import Path
from shutil import copyfile
src = "outputs/segments/2_ExplodeAudio"
folder = "outputs/classification"
process = Chain(
source=src,
folder=folder
)
cluster = AgglomerativeClustering(numclusters=2)
process.add(
Flux(cache=False),
Stats(numderivs=2),
Normalise(),
cluster
)
if __name__ == "__main__":
process.run()
# Now implement a quasi one-shot analyser to copy the sound files to individual directories
from ftis.analyser import (FluidOnsetslice, FluidMFCC, Stats, Standardise,
UMAP, ExplodeAudio)
from ftis.process import FTISProcess as Chain
from ftis.corpus import Corpus, PathLoader
analysis = Chain(source=Corpus("../../reaper/highgain_source/bounces"),
folder="../outputs/em_detailed_segmentation")
analysis.add(
# Segmentation
FluidOnsetslice(framedelta=20,
minslicelength=2,
filtersize=5,
threshold=0.3,
metric=0,
cache=1),
ExplodeAudio(),
# Analysis
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[4096, 512, 4096]),
Stats(numderivs=1),
Standardise(),
UMAP(components=2))
if __name__ == "__main__":
analysis.run()
from ftis.process import FTISProcess as Chain
from ftis.corpus import Corpus
from ftis.common.io import write_json, get_duration
from pathlib import Path
import jinja2
"""
Give me a crude 3 cluster output of all of the 'static' files. Later on we'll find other files similar to these.
We can use both bits of information to help compose.
"""
output = "../../reaper/Convolutions/base_materials"
em = Corpus("~/Cloud/Projects/ElectroMagnetic/outputs/classification/4_Split/1")
analysis = Chain(
source = (em),
folder = output
)
dr = UMAP(components=10, cache=1)
clustering = AgglomerativeClustering(numclusters=3)
analysis.add(
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[2048, -1, -1], cache=1),
Stats(numderivs=1, flatten=True, cache=1),
Standardise(cache=1),
dr,
clustering
)
if __name__ == "__main__":
analysis.run()
from ftis.corpus import Corpus
from ftis.common.io import write_json
from pathlib import Path
corpus = (
Corpus("~/Cloud/Projects/DataBending/DataAudioUnique")
.duration(min_duration=0.1, max_duration=10)
)
point = Corpus("~/Cloud/Projects/ElectroMagnetic/reaper/Interruptions/media/06-Kindle Off-200513_1547-glued-04.wav")
output = "../outputs/isolate_static"
analysis = Chain(
source = (point+corpus),
folder = output
)
kdtree = KDTree()
dr = UMAP(components=10, cache=1)
analysis.add(
CollapseAudio(),
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[2048, -1, -1], cache=1),
Stats(numderivs=1, flatten=True, cache=1),
Standardise(cache=1),
dr,
kdtree
)
if __name__ == "__main__":
analysis.run()
import jinja2
db_corpus = (
Corpus("~/Cloud/Projects/DataBending/DataAudioUnique")
.duration(min_duration=0.1, max_duration=10)
)
em_corpus = (
Corpus("../outputs/em_detailed_segmentation/1_ExplodeAudio")
.duration(min_duration=0.1, max_duration=20)
)
output = "../outputs/multicorpus_exploring"
analysis = Chain(
source = (db_corpus + em_corpus),
folder = output
)
clustering = HDBSCAN(minclustersize=10, cache=1)
analysis.add(
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[2048, -1, -1], cache=1),
Stats(numderivs=1, flatten=True, cache=1),
Standardise(cache=1),
UMAP(components=10, cache=1),
clustering
)
if __name__ == "__main__":
analysis.run()
tracks = {}
from ftis.analyser.descriptor import FluidMFCC
from ftis.analyser.scaling import Standardise
from ftis.analyser.dr import UMAP
from ftis.analyser.clustering import HDBSCAN
from ftis.analyser.stats import Stats
from ftis.process import FTISProcess as Chain
from ftis.corpus import Corpus
db_corpus = (Corpus("~/Cloud/Projects/DataBending/DataAudioUnique").duration(
min_duration=0.03))
em_corpus = (Corpus("../outputs/em_detailed_segmentation/1_ExplodeAudio"))
analysis = Chain(source=db_corpus + em_corpus,
folder="../outputs/multicorpus_exploring")
analysis.add(
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[2048, -1, -1], cache=1),
Stats(numderivs=1, flatten=True, cache=1), Standardise(),
UMAP(components=2), HDBSCAN(minclustersize=5))
if __name__ == "__main__":
analysis.run()
from ftis.analyser import (FluidMFCC, Stats, Standardise, HDBSCluster)
from ftis.process import FTISProcess as Chain
from ftis.corpus import Corpus, PathLoader
from ftis.common.io import get_duration
from pathlib import Path
import jinja2
folder = "../outputs/em_detailed_clustering"
corpus = Corpus("../outputs/em_detailed_segmentation/1_ExplodeAudio").loudness(
max_loudness=20)
analysis = Chain(source=corpus, folder=folder)
clustering = HDBSCluster(minclustersize=10, cache=1)
analysis.add(
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[1024, -1, -1], cache=1),
Stats(numderivs=1, flatten=True, cache=1), Standardise(cache=1),
clustering)
if __name__ == "__main__":
analysis.run()
tracks = {}
for cluster, items in clustering.output.items():
track_id = cluster
pos = 0
for audiofile in items:
dur = get_duration(audiofile)
item = {
"file": audiofile,
"length": get_duration(audiofile),
"start": 0.0,
# Description
"""
Analyse each item belonging to a cluster so that they can be ranked in order of a descriptor.
"""
from ftis.analyser import Flux, FluidLoudness, Stats
from ftis.process import FTISProcess as Chain
src = "outputs/micro_segmentation/2_ExplodeAudio"
flux_chain = Chain(
source=src,
folder="outputs/micro_segmentation_flux",
)
loud_chain = Chain(source=src, folder="outputs/micro_segmentation_loudness")
flux = Flux()
loud = FluidLoudness()
flux_chain.add(flux, Stats(numderivs=0))
loud_chain.add(loud, Stats(numderivs=0))
if __name__ == "__main__":
flux_chain.run()
loud_chain.run()