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 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
# We will use the directories as a significant progress point from which new analysis will be orchestrated
out = Path(folder) / "4_Split"
out.mkdir(exist_ok=True)
for c in cluster.output:
folder = out / c
folder.mkdir(exist_ok=True)
from ftis.analyser import FluidMFCC, Stats, Standardise, UMAP, Normalise, HDBSCluster
from ftis.corpus import CorpusLoader, CorpusFilter
from ftis.process import FTISProcess as Chain
src = "outputs/micro_segmentation/2_ExplodeAudio"
folder = "outputs/quitest"
process = Chain(source=src, folder=folder)
process.add(CorpusLoader(cache=1), CorpusFilter(max_loudness=10, cache=1),
FluidMFCC(discard=True, cache=1), Stats(numderivs=1, cache=1),
UMAP(components=10, cache=1), HDBSCluster(minclustersize=5))
if __name__ == "__main__":
process.run()
"""
Analyse each segment in the 'gestural' pool and cluster it
"""
from ftis.analyser import (FluidMFCC, HDBSCluster, Stats, UMAP, Standardise,
Normalise)
from ftis.process import FTISProcess as Chain
src = "outputs/micro_segmentation/2_ExplodeAudio"
folder = "outputs/micro_clustering"
process = Chain(source=src, folder=folder)
process.add(FluidMFCC(cache=True),
Stats(numderivs=1,
flatten=True, cache=False), Standardise(cache=False),
Normalise(cache=False), UMAP(components=6),
HDBSCluster(minclustersize=10))
if __name__ == "__main__":
process.run()
# Description
"""
Arrange segmentations along a single axis using dimension reduction
"""
from ftis.analyser import FluidMFCC, UMAP, Stats, Normalise
from ftis.process import FTISProcess as Chain
from ftis.common.conversion import samps2ms
src = "outputs/micro_segmentation/2_ExplodeAudio/"
folder = "outputs/oned"
process = Chain(source=src, folder=folder)
mfcc = FluidMFCC(cache=True)
stats = Stats(flatten=True, numderivs=1, cache=True)
umap = UMAP(components=1, cache=True)
normalise = Normalise()
process.add(mfcc, stats, umap, normalise)
if __name__ == "__main__":
process.run()
from ftis.analyser import FluidMFCC, Stats, Standardise, UMAP, Normalise, HDBSCluster
from ftis.common.io import get_duration
from ftis.corpus import CorpusLoader, CorpusFilter
from ftis.process import FTISProcess as Chain
from pathlib import Path
import jinja2
src = "outputs/micro_segmentation/2_ExplodeAudio"
folder = "outputs/loudest"
process = Chain(source=src, folder=folder)
clustering = HDBSCluster(minclustersize=10)
process.add(
CorpusLoader(cache=1), CorpusFilter(max_loudness=100, min_loudness=75),
FluidMFCC(discard=True, numcoeffs=20, fftsettings=[8192, 128, 8192]),
Stats(numderivs=1,
spec=["median", "max", "min", "stddev", "mean", "skewness"]),
UMAP(components=2), clustering)
if __name__ == "__main__":
process.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),
# 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()