Difference between revisions of "356-winter-2023/hw2"
From CCRMA Wiki
(→Phase One: Extract, Classify, Validate) |
(→Phase Two: Design Audio Mosaic Tool) |
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* briefly report on your experiments | * briefly report on your experiments | ||
| − | === Phase Two: | + | === Phase Two: Designing Audio Mosaic Tool === |
| + | * here is the [https://ccrma.stanford.edu/courses/356/code/featured-artist/ sample code] | ||
* using what you've learned, build a database mapping sound frames (100::ms to 1::second) <=> feature vectors | * using what you've learned, build a database mapping sound frames (100::ms to 1::second) <=> feature vectors | ||
** curate your own set of audio files can be mixture of | ** curate your own set of audio files can be mixture of | ||
| Line 63: | Line 64: | ||
** modify the <code>feature-extract.ck</code> code from Phase One to build your database of sound frames to feature vectors | ** modify the <code>feature-extract.ck</code> code from Phase One to build your database of sound frames to feature vectors | ||
** note this does not require any labels, and like word2vec, we want to situate each sound window in a N-dimension feature space | ** note this does not require any labels, and like word2vec, we want to situate each sound window in a N-dimension feature space | ||
| − | * | + | * play with <code>mosaic-similar.ck</code>: a feature-based sound explorer to query your database and perform similarity retrieval (using KNN2) |
| − | * using your database and retrieval tool and [https://en.wikipedia.org/wiki/Concatenative_synthesis concatenative synthesis] | + | * using your database and retrieval tool and [https://en.wikipedia.org/wiki/Concatenative_synthesis concatenative synthesis] and the <code>mosaic-synth-mic.ck</code> and <code>mosaic-synth-doh.ck</a>, design an interactive audio mosaic generator |
** feature-based | ** feature-based | ||
** real-time | ** real-time | ||
** takes any audio input (mic or any unit generator) | ** takes any audio input (mic or any unit generator) | ||
| − | ** can be used for | + | ** can be used for expressive audio mosaic creation |
* (optional) do this in the audiovisual domain; build a GUI for exploring sounds by similarity; will need to reduce dimensions (using PCA or another technique) to 3 or 2 in order to visualize | * (optional) do this in the audiovisual domain; build a GUI for exploring sounds by similarity; will need to reduce dimensions (using PCA or another technique) to 3 or 2 in order to visualize | ||
Revision as of 02:45, 31 January 2023
Programming Project #2: "Featured Artist"
Music and AI (Music356/CS470) | Winter 2023 | by Ge Wang
In this programming project, we will learn to work with audio features for both supervised and unsupervised tasks. These include a real-time genre-classifier and a feature-based audio mosaic tool. Using the latter, create a feature-based musical statement or performance!
Due Dates
- Coding tutorial: Thursday evening (time TBD) | attendance is high recommended (will save you time later)
- Milestone (Phase One complete + Phase Two prototype): webpage due Wednesday (2/1, 11:59pm) | in-class critique Thursday (2/2)
- Final Deliverable: webpage due Wednesday (2/8, 11:59pm)
- In-class Presentation: Thursday (2/9)
Discord Is Our Friend
- direct any questions, rumination, outputs/interesting mistakes to our class Discord
Things to Think With
- read/skim the classic article "Musical Genre Classification of Audio Signals" (Tzanetakis and Cook, 2002)
- don't worry about the details yet; first get a general sense what audio features and how they can be used
Tools to Play With
- get the latest bleeding edge secret
chuckbuild (2023.01.23 or later!)- macOS this will install both command line
chuckand the graphical IDE miniAudicle, and replace any previous ChucK installation. - Windows you will need to download and use the bleeding-edge command line
chuck(for now, there is no bleeding-edge miniAudicle for Windows); can either use the defaultcmdcommand prompt, or might consider downloading a terminal emulator. - Linux you will need to build from source, provided in the
linuxdirectory - all platforms for this project, you will be using the command line version of chuck.
- macOS this will install both command line
- NOTE: to return your chuck back to a pre-bleeding-edge state, you can always install the latest official ChucK release
GTZAN Dataset
- next, you'll need to download the GTZAN dataset
- 1000 30-second music clips, labeled by humans into ten genre categories
Phase One: Extract, Classify, Validate
- understanding audio, audio features, FFT, feature extraction
- extract different sets of audio features from GTZAN dataset
- run real-time classifier using different feature sets
- run cross-validation to evaluate the quality of classifier based different features
- you can find relevant code here
- start playing with these, and reading through these to get a sense of what the code is doing
- example-centroid.ck -- a basic example of using ChucK's unit analyzer framework (things connected using the upchuck operator
=^) to extract an audio feature:- generate an input (a 440hz sine wave) -- this can be any audio source, e.g., adc for the microphone
- take a Fast Fourier Transform (FFT) on a frame of audio (size is determined by the FFT size)
- using the output of FFT's analysis to compute the Spectral Centroid for that frame of audio
- note how the ChucK timing is used to precisely control how often to do a frame of analysis
- the
.upchuck()is used to trigger an analysis, automatically cascading up the=^
- example-mfcc.ck -- this is like the previous example, but now we compute a multi-dimensional feature, Mel Frequency Cepstral Coefficients (MFCCs)
- feature-extract.ck -- in a "real-world" scenario, we would extract multiple features. a
FeatureCollectoris used to aggregate multiple features into a single vector (see comments in the file for more details) - genre-classify.ck -- using output of
feature-extract.ck, do real-time classification by performing the same feature extraction and using k-NN to predict likelihood of each genre category (see comments in the file for more details) - x-validate.ck -- using output of
feature-extract.ck, do cross-validation to get a sense of the classifier quality
- experiment by choosing different features and different number of features, extracting them on GTZAN, try the real-time classifier, and perform cross-validation
- available features: Centroid, Flux, RMS, RollOff, ZeroX, MFCCs, Chroma, Kurtosis
- try at least five different feature configurations and evaluate the resulting classifier using cross-validation
- keep in mind that the baseline score is .1 (a random classifier for 10 genre), and 1 is max
- how do different--and different numbers of--features affect the classification results?
- in your experiment, what configuration yielded the highest score in cross-validation?
- briefly report on your experiments
Phase Two: Designing Audio Mosaic Tool
- here is the sample code
- using what you've learned, build a database mapping sound frames (100::ms to 1::second) <=> feature vectors
- curate your own set of audio files can be mixture of
- short sound effects (~1 second)
- music (we will perform feature extraction on all short-time windows from beginning to end; in essence each short-time window becomes a short sound fragment with its own feature vector)
- modify the
feature-extract.ckcode from Phase One to build your database of sound frames to feature vectors - note this does not require any labels, and like word2vec, we want to situate each sound window in a N-dimension feature space
- curate your own set of audio files can be mixture of
- play with
mosaic-similar.ck: a feature-based sound explorer to query your database and perform similarity retrieval (using KNN2) - using your database and retrieval tool and concatenative synthesis and the
mosaic-synth-mic.ckandmosaic-synth-doh.ck</a>, design an interactive audio mosaic generator- feature-based
- real-time
- takes any audio input (mic or any unit generator)
- can be used for expressive audio mosaic creation
- (optional) do this in the audiovisual domain; build a GUI for exploring sounds by similarity; will need to reduce dimensions (using PCA or another technique) to 3 or 2 in order to visualize
Phase Three: Make a Musical Mosaic!
- use your prototype from Phase Two to create a feature-based musical mosaic in the form of a musical statement or performance
- (optional) do this in the audiovisual domain
Reflections
- write ~300 words of reflection on your project. It can be about your process, or the product. What were the limitations (and how did you try to get around them?)
Deliverables
- create a CCRMA webpage for this etude
- the URL should live at https://ccrma.stanford.edu/~YOURUSERID/356/hw2 or https://ccrma.stanford.edu/~YOURUSERID/470/hw2
- alternately, you may use Medium or another publishing platform (but please still link to that page from your CCRMA webpage)
- your webpage is to include
- a title and description of your project (free free to link to this wiki page)
- all relevant chuck code from all three phases
- phase 1: all code used (extraction, classification, validation)
- phase 2: your mosaic generator, and database query/retrieval tool
- phase 3: code used for your musical statement
- video recording of your musical statement (please start early!)
- your 300-word reflection
- any acknowledgements (people, code, or other things that helped you through this)
- submit to Canvas only your webpage URL
