Open Curriculum

Our devices are listening to us. Previous generations of audio-technology transmitted, recorded or manipulated sound. Today our digital voice assistants, smart speakers, and a growing range of related technologies are increasingly able to analyse and respond to it as well. Scientists and engineers increasingly refer to this as “machine listening”, though the first widespread use of the term was in computer music. Machine listening is much more than just a new scientific discipline or vein of technical innovation however. It is also an emergent field of knowledge-power, of data extraction and colonialism, of capital accumulation, automation and control. It demands critical and artistic attention.

This is an open curriculum for machine listening’s study.

Section 0 (’metacurriculum’) is about what the curriculum is, how it is structured, what is meant by openness, and what you might need to know to use or contribute to the curriculum.

Everything from Section 1 on is the curriculum itself, which grows additively and serially. In other words, it gets longer and longer. Rather than edit, rewrite, or delete entries, new additions build on, reframe, intervene in, or comment on existing ones.

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Metacurriculum
0
structuremethods
What is this curriculum?
0a
This curriculum’s history
0a1
Zettelkasten as a structural idea
0a2
methodsexperiments
What is ‘open’ about this open curriculum?
0a2a
How can this curriculum be decolonized, pluralised, or remapped?
0a3
methods
Conscious thinking and continuous education
0a3a
Plurality as transformative, not additive
0a3b
Positionality and site specificity
0a3c
Situating theory
0a3d
Who is this curriculum being written by?
0b
Who is this curriculum for? And how can it be used?
0c
What kind of contributions can you make? And where do they go?
0d
What do the tags mean?
0f
How should I format my contribution?
0g
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Machine listening
1
Forensic listening
1b
truthmethods
Hungry listening
1c
methods
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Techniques of machine listening
2
Audio Fingerprinting
2a
readingtechniques
Shazam as a verb
2a1
methods
Google Content ID
2a2
techniques
Adversarial Audio
2b
techniques
Keyword spotting
2c
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Speech recognition
3
big topic
Histories of speech recognition
3a
Language and speech recognition
3b
What do wake words do?
3c
experiments
Metaphors of machine listening
3d
Metaphors of Alexa Skills Kit
3e
objectsreading
Critical readings of "voice industry" consumer adoption reports
3f
objectsreading
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Voice analysis
5
Accent bias
5a
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Music Information Retrieval
6
Automatic music transcription
6a
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Computational Auditory Scene Analysis (CASA)
7
reading
Calling in Our Corals (Google)
7b
objects
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Datasets
8
big topic
AudioSet
8a
objectsreading
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Instruments
9
Voyager
9a
objects
Word Processor/Decomposer
9b
objects
LANDR
9c
objects
Holly+
9d
objects
The assistive pretext
10
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Surveillance
11
big topic
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Voice Assistants
12
objects
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Audio Deepfakes
13
truthobjects
Voice clone survey
13a
objectsexperiments
Deepfakes beyond deceit
13b
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Labour in/and Machine Listening
14
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Speech Synthesis
15
big topic
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Music
16
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Platforms
17
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Environments
18