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Sound Applications
Advanced MultimediaTamara Berg
Reminder
• HW2 due March 13, 11:59pm • Questions?
Howard Leung
Audio Indexing and Retrieval
• Features for representing audio:– Metadata– low level features – high level audio features
• Example usage cases:Audio classificationMusic retrieval
Content Based Music Retrieval
Extract music descriptions from a database of music documents.
Extract music description from a query music document.
Compute similarity between query and database descriptions.
Retrieve similar music documents to query.
Casey et al IEEE 2008
MIR tasks
H: high level specificity – match specific instances of audio content.
M: mid-level specificity – match high level audio features like melody, but do not match audio content.
L: low specificity – match global (statistical) properties of the query
Different usage cases require different descriptions and matching schema.
Casey et al IEEE 2008
Metadata
• Most common method of accessing music• Can be rich and expressive• When catalogues become very large, difficult
to maintain consistent metadata
Useful for low specificity queries
Casey et al IEEE 2008
Metadata• Pandora.com – Uses metadata to estimate artist
similarity and track similarity and creates personalized radio stations. Experts entered metadata of musical-cultural properties (20-30 minutes per track of an expert’s time – 50 person-years for 1 million tracks).
• Crowd sourced metadata repositories (gracenote, musicbrainz). Factual metadata (artist, album, year, title, duration). Cultural metadata (mood, emotion, genre, style).
• Automatic metadata methods – generate descriptions from community metadata automatically. Language analysis to associate noun and verb phrases with musical features (Whitman & Rifkin).
Casey et al IEEE 2008
Content features• Low level or high level• Want features to be robust to certain changes in
the audio signal– Noise– Volume– Sampling
• High level features will be more robust to changes, low level features will be less robust.
• Low level features will be easy to compute, high level difficult
Content features• Low level or high level• Want features to be robust to certain changes in
the audio signal– Noise– Volume– Sampling
• High level features will be more robust to changes, low level features will be less robust.
• Low level features will be easy to compute, high level difficult
Content features• Low level or high level• Want features to be robust to certain changes in
the audio signal– Noise– Volume– Sampling
• High level features will be more robust to changes, low level features will be less robust.
• Low level features will be easy to compute, high level difficult
Low level audio features
• Low level measurements of audio signal that contain information about a musical work.
• Can be computed periodically (10-1000 ms intervals) or beat synchronous.
Casey et al IEEE 2008
In text analysis we had words, here we have to come up with our own set of features to compute from audio signal!
Example Low-Level Audio Features
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Average number of times signal crosses zero amplitude value.
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Average number of times signal crosses zero amplitude value.
Howard Leung
Average number of times signal crosses zero amplitude value.
1 if trueO o.w.
Howard Leung
Example Low-Level Audio Features
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Frequency Domain Reminder
How much of each describes the frequency spectrum of a signal.Li & Drew
Signals can be decomposed into a weighted sum of sinusoids
Frequency domain features
• How do we get to frequency domain?
Time Frequency
DFTDiscrete Fourier Transform (DFT) of the audio
Converts to a frequency representation
DFT analysis occurs in terms of number of equallyspaced ‘bins’
Each bin represents a particular frequency rangeDFT analysis gives the amount of energy in the audio signalthat is present within the frequency range for each bin
Inverse Discrete Fourier Transform (IDFT)Converts from frequency representation back to audio signal.
DFTDiscrete Fourier Transform (DFT) of the audio
Converts to a frequency representation
DFT analysis occurs in terms of number of equallyspaced ‘bins’
Each bin represents a particular frequency rangeDFT analysis gives the amount of energy in the audio signalthat is present within the frequency range for each bin
Inverse Discrete Fourier Transform (IDFT)Converts from frequency representation back to audio signal.
DFTDiscrete Fourier Transform (DFT) of the audio
Converts to a frequency representation
DFT analysis occurs in terms of number of equallyspaced ‘bins’
Each bin represents a particular frequency rangeDFT analysis gives the amount of energy in the audio signalthat is present within the frequency range for each bin
Inverse Discrete Fourier Transform (IDFT)Converts from frequency representation back to audio signal.
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FilteringRemoves frequency components from some
part of the spectrum Low pass filter – removes high frequency
components from input and leaves only low in the output signal.
High pass filter – removes low frequency components from input and leaves only high in the output signal.
Band pass filter – removes some part of the frequency spectrum.
How could you do this using the FT and IFT?
Compute FT spectrum of input.
Zero out the part of the frequency spectrum that you want to filter out.
Compute the IFT of this modified spectrum -> output will be input with some frequency components removed.
How could you do this using the FT and IFT?f = input
How could you do this using the FT and IFT?f = input
FT(f)
How could you do this using the FT and IFT?
1
0
.*
f = input FT(f)
How could you do this using the FT and IFT?
1
0
.*
f = input FT(f)
Zero out some freq components
How could you do this using the FT and IFT?
1
0
.*
=
f = input FT(f)
Zero out some freq components
x xxxxxxxxxxxxx
How could you do this using the FT and IFT?
1
0
.*
=
f = input FT(f)
Zero out some freq components IFT
o = Frequency limited output
x xxxxxxxxxxxxx
How could you do this using the FT and IFT?
1
0
.*
=
f = input FT(f)
Zero out some freq components IFT
o = Frequency limited output
x xxxxxxxxxxxxx
What kind of filter is this?
How could you do this using the FT and IFT?f = input
How could you do this using the FT and IFT?f = input
FT(f)
How could you do this using the FT and IFT?
1
0
.*
f = input FT(f)
How could you do this using the FT and IFT?
1
0
.*
f = input FT(f)
Zero out some freq components
How could you do this using the FT and IFT?
1
0
.*
=
f = input FT(f)
Zero out some freq components
xxxxxxxxxxxxx
How could you do this using the FT and IFT?
1
0
.*
=
f = input FT(f)
Zero out some freq components IFT
o = Frequency limited output
xxxxxxxxxxxxx
How could you do this using the FT and IFT?
1
0
.*
=
f = input FT(f)
Zero out some freq components IFT
o = Frequency limited output
xxxxxxxxxxxxx
What kind of filter is this?
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Howard Leung
Frequency Domain Reminder
How much of each describes the frequency spectrum of a signal.Li & Drew
Signals can be decomposed into a weighted sum of sinusoids
Pitch-Class Profile (PCP)
• Represent the energy due to each pitch class • Integrates the energy in all octaves into a single band• There are 12 equally spaced pitch classes in western tonal
music. So, typically 12 bands in the PCP.
Pitch-Class Profile (PCP)
• Represent the energy due to each pitch class • Integrates the energy in all octaves into a single band• There are 12 equally spaced pitch classes in western tonal
music. So, typically 12 bands in the PCP.
How might we calculate this using the DFT?
High level music featuresHigh level intuitive information about a piece of music (melody, harmony etc).
“It is melody that enables us to distinguish one work from another. It is melody that human beings are innately able to reproduce by singing, humming, andwhistling. It is melody that makes music memorable: we are likely to recall a tune long after we have forgotten its text.”
-Selfridge-Field
Intuitive features, but hard to extract and ongoing areas of research.
Casey et al IEEE 2008
Melody & Bass Estimation
• Melody and bass lines represented as continuous temporal trajectory of fundamental frequency, F0, (a series of musical notes).
• PreFEst (Goto 1999) – Estimate the F0 trajectory in mid-high freq range of
input -> melody. – Estimate the F0 trajectory in low freq range-> bass.
Casey et al IEEE 2008
Chord Recognition
Recognize chord progressions based on:- Estimated PCPs- Statistics of transitions between PCPs
Casey et al IEEE 2008
Chord Recognition
Chord Recognition
Music as vector of features
• Once again we represent (music) documents as a vector of numbers – Each entry (or set of entries) in this vector is a different
feature
Music as vector of features
• Once again we represent (music) documents as a vector of numbers – Each entry (or set of entries) in this vector is a different
feature
• To retrieve music documents given a query we can:– Find exact matches– Find nearest match– Find nearby matches– Train a classifier to recognize a given category (genre, style
etc).
Audio Similarity
We have a description of a music document based on some set of features, now how do we compare two descriptions?
Casey et al IEEE 2008
Usage examples
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Query by humming• Requires robustness to variation because
matches will not be exact• Extract melody from dataset of songs• Extract melody from hum• Match by comparing similarities of melodies
(nearby matches)
Copyright monitoring
• Compute fingerprints from database examples• Compute fingerprint from query example• Find exact matches
Best performing systems on MIREX 2007
Casey et al IEEE 2008
Music BrowsingMusicream – UI for discovering and managing musical pieces.
User can select a disc and listen to it. By dragging a disc in the flow, the user can easily pick out other similar pieces (attach similardiscs). This interaction allows a user to unexpectedly comeacross various pieces similar to other pieces the user likes.
Link to demo
Casey et al IEEE 2008
Music Browsing
Musicrainbow – UI for discovering unknown artists.
Artists are mapped on a circular rainbow where colors represent different styles of music. Similar artists are mapped near each other.
User rotates rainbow by turning a knob.
Link to demo
Casey et al IEEE 2008
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