friday lunchtime lecture: making music with open data

8/11/2019 Friday lunchtime lecture: Making music with open data

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TROUBLE AT

T'LEEDSDATAMILL:

OOMPAH.PY

BIG DATA MEETS BIG BRASS/

/Nicholas H.Tollervey @ntoll

Simon Davy @bloodearnest
http://twitter.com/bloodearnesthttp://github.com/bloodearnesthttp://twitter.com/ntollhttp://ntoll.org/


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AGENDAOompah.huh..? (Nicholas & Simon)

Data analysis. (Simon)Musical mischief. (Nicholas)Questions. (You)Performance! (Enderby Brass Band)


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OOMPAH.HUH..?


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"WE TELL STORIES..."


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LDM HACK DAYCan we tell an engaging story about Leeds with data?


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http://leedsdatamill.org/dataset/leeds-city-centre-footfall-

data
http://leedsdatamill.org/dataset/leeds-city-centre-footfall-data


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8/31

CRAZY IDEA #1Turn the footfall data into music.


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CRAZY IDEA #2


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CRAZY IDEA #2What musical genre epitomizes Leeds?


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Our aim was to write Python scripts that turned raw footfalldata into music for brass band called "Rhythm of the City".

And so, oompah.pywas ironically checked into Github.


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DATA ANALYSIS

MUNGING


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EXCEL + MESSY DATA = AARGH!

Please use CSV


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Tuesdayname | median | min histogram max|-------------------------------------------------------------------------00:00 : 33.0 ms [ 1.0 ___ _ __ _ _ _ 382.0]

01:00 : 30.0 ms [ 4.0

________ __ _____ ___ 397.0]02:00 : 28.0 ms [ 5.0 ____ __ _ ____ _ 321.0]03:00 : 23.0 ms [ 5.0 _______ _ ____ __ 264.0]04:00 : 12.0 ms [ 2.0

______ __ _ ___ __ 180.

0]

05:00 : 17.0 ms [ 1.0

___ ____ __ __ __ 186.0]06:00 : 64.0 ms [ 6.0

_

_ ___ 163.0]07:00 : 205.0 ms [ 5.0 ___ ___ _ _ _ ____ 405.0]08:00 : 470.0 ms [ 12.0 _ __ ___ _ ____ 745.0]

09:00 : 554.0 ms [ 14.0 _____

_

______ 1096.0]10:00 : 931.0 ms [ 24.0 __ _ _ ____ ____ 1970.0]11:00 : 1357.0 ms [ 0.0 _ _ _____

______ 2

478.0]12:00 : 1823.0 ms [ 56.0 ___ _ __ _ ________ _ 5652.0]


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TIME TRAVEL FTW

WEEK = {}

WEND = {}

forf inglob.glob('*.csv'):

voice = f.split('.')[0].lower().replace(' ', '_')

WEEK[voice] = defaultdict(list)

WEND[voice] = defaultdict(list)

withopen(f) asfp:

forrow incsv.DictReader(fp): t = row['Hour']

d = row['WeekDay']

ifd.lower() in('saturday', 'sunday'):

data = WEND

else:

data = WEEK

data[voice][t].append(int(row['Count']))


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PITCH

defpitch(data, scale):

"""Generate notes from the scale driven by the data."""

index = 0 ford indata:

index = (index + d) % len(scale)

yieldscale[index]


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DURATION

defrhythm(data, mn, mx):

"""Produce note durations from provided patterns,

based on mean intensity

"""

i = threshold(data)

pattern = patterns[i]

# use the data to chose which pattern, # so the process is deterministic

index = 0

ford indata:

index = (index + d) % len(pattern)

forduration inpattern[index]:

yieldduration

yieldNone # indicates a bar has been produced


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VOICE OF THE STREET

defvoice(data, scale, mn, mx, length=8):

"""Generate a voice from the data, combining pitch and rhythm""" bars = 0 note_iter = pitch(data, scale) forduration inrhythm(data, mn, mx): ifduration isNone: bars += 1 yield'|' ifbars == length:

raiseStopIteration() elifduration[0] == 'r': yieldduration else: note = next(note_iter) if'%s'induration: # more complex templated duration (e.g. tuplets) yieldduration % note

else: yield"%s%s"% (note, duration)


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MUSICAL

MISCHIEF


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BLOOPS!

0:00


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Charles E.Ives


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Music affects because of the effects of sound.

Music engages us to listen.

Music, in some sense, tells a story in sound (there is adiscernable narrative).


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CAN WE REVEAL THE STORY

OF THE DATA THROUGH

MUSIC?


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ELEMENTS OF MUSIC


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ELEMENTS OF MUSIC

Pitch: how high or low a note sounds.Rhythm: a sequence of different durations.Melody = Pitch + Rhythm.Timbre: the quality of a sound (e.g. the difference

between a flute and violin).Dynamics: loudness.Key: the set of available pitches.Harmony: how pitches sound together as polyphony.

Texture: how the harmony is "voiced".Etc, etc...


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A MUSICAL MAPPINGThe ingredients for generating "Rhythm of the City".

Each instrument group in the band represents a specificarea of the city. Ergo, cameras/locations aredifferentiated by timbre.The amount of footfall in a location changes the intensity

of dynamics and rhythm (more footfall = louder / busiermelodies).Use "nice" sounding pentatonic keys.Time of day is indicated by key.

Each hour of the day is represented by an eight barfragment.Tubular bells sound out the hours every eight bars.

THE RESULT!


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THE RESULT!


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DOES IT WORK..?

IT'S TUBA TIME!


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IT'S TUBA TIME!

1am, 7am, 12 noon

friday lunchtime lecture: making music with open data

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