Jazzomat & Dig That LickSome Assorted Results

Martin Pfleiderer

Simon Dixon

Klaus Frieler

MIRAGE Symposium #1:

Computational Musicology

Oslo, 9th June 2021

Studies

Corpus Studies

• Performance

• Microtiming & swing (WJD)

• Sound & intonation analysis (WJD)

• Style & History

• Case studies (e.g., Trane ./. Miles) (WJD)

• Feature history of jazz improvisation (WJD)

• Style classification post-bop vs. be/hard bop (WJD)

• Solo dramaturgy (WJD)

• Walking bass (WJD)

• Patterns

• Anatomy of a Lick (WJD, DTL1000)

• Pattern Use (WJD, DTL1000)

• Pattern Transmission (WJD, DTL1000)

• Psychology of Improvisation

• Jazz line grammar (WJD)

• Mid-level analysis (WJD)

• Easy First (WJD)

• Analysis-by-synthesis solo generation

Experimental Studies

• Perception of virtuosity, musicality, and emotions in jazz

• Development of jazz improvisation skills

Technical Papers

• Database description

• Transcription theory, practice, and algorithms

• Flexible Quantiziation algorithm

• Software manual, tutorials etc.

1. PERFORMANCE

Swing Ratio

• Swing here means playing “uneven eighths”.

• ~15,000 Swing Triples form the WJD.

• Performers show one or two clusters of

eighths: Even and swung.

• Average swing ratio for swung eighths:

1.42:1.

• Large variations with styles and performers.

• Single „soft“ swung cluster in earlier styles.

• „Hard“ swing as stylistic device in later

styles.

Corcoran & Frieler, Music Perception, 2021

Intonation of jazz performers

“On” if pitch in 25 cents window

Average Non/N = .72

Overall tendency to play sharp

Abeßer et al, IEEE/ACM Transactions on Audio, Speech and Language Processing, 2017

2. STYLE & HISTORY

Miles vs. Trane

John Coltrane Miles Davis

Rhythm Mostly fast lines

(„Sheets of sound“)

Rhythmically diverse

Longer tones & pauses

Intervals More descending More ascending

Fewer tone repetitions More tone repetitions

Larger intervals Smaller intervals

More thirds & arpeggios Fewer thirds

Pitch Larger pitch range Smaller pitch range

Avoids thirds, more blue notes

Midlevel Units More lines, expressive &

fragments

More licks, melody & void

Patterns More patterns Fewer patterns

No common pattern vocabulary

How did solos change over time?

Example: Pitch ranges (ρ = 0.596, p <.001)

Jazz solos over time

• Time

– Solos became longer

– Rhythms became more uniform

– More fast notes

– More lines

• Pitch

– Fifths of chords decrease

– Tonally more complex

– More chromatics

• Intervals

– Wider intervals

– More diverse interval combinations

• Sound

– Tones more stable

– Articulation more diverse

– Intonation more “precise”

• Expression

– Expanding ambitus

– Increasing exhaustion of pitch space

– Heightened expressivity

– More diverse overall design of solos

→ Overall trend to higher complexity, diversity

and expressivity.

Frieler, Jazz @100. Darmstädter Beiträge zur Jazzforschung (2018)

3. PATTERNS, PATTERNS

Patterns in Jazz

• Licks and formulas (patterns) important for jazz improvisation.

• Patterns are short snippets (N-grams) in a set of sequences (intervals,

pitches etc).

• Usually subject to certain properties (e.g., length, document &

relative frequencies).

• Licks are repeated patterns of a recognizable musical gestalt.

Pattern Example

18-tone interval N-gram by Bob Berg on „Angles“

[-2, 1, 1, -2, 1, -1, -1, -1, -1, 2, 2, -4, 2, -1, -1, 4, -2]

Measure 30

Measure 108

Pattern coverage100 % coverage by

N-grams with N ≥ 4

occurring at least in

two different solos

50-75 % coverage by

N-grams with N ≥ 4

occurring at least 32

times in at least

4 different solos

Pattern coverage: Number of notes in a solo contained in at least one interval N-gram

Solo frequency

Corp

us fre

quency

Pattern Transmission (Saxophonists, DTL1000 + WJD + Omnibook)

4. PSYCHOLOGY OF IMPROVISATION

Beaty, Frieler et al., Journal Exp. Psych. Gen. (2021)

Easy First(Easier stuff occurs earlier in a phrase)

Values ≤ 0

for lines

First increase, then

saturation

Values ≥ 0

for licks

Value = 0 for

simulations

Conclusion

• Large, well-curated, and richly annotated databases have immense

potentials for musicological research.

• Scientific use of a database strongly dependent on its quality.

• Algorithms change, data stays.

• Bottle-neck in digital jazz research: transcriptions.

• MIR techniques allow generation of very large databases, while

integrating data from various sources.

• But: Further need to improve quality of transcriptions and

annotations in order to dig the full potential of jazz databases.

• Desideratum: Move beyond the monophonic case.

Thank you!

The Jazzomat Team: Martin Pfleiderer, Jakob Abeßer, Wolf-Georg Zaddach,

Friederike Bartel, Benjamin Burkhard, Martin Breternitz, Peter Heppner, Yvette

Kneisel, Benedikt Koch, Simon Meininger, Benjamin Napravnik, Albrecht Probst,

Franziska Risch, Lydia Schulz, Amelie Zimmermann, Alaa Zouiten, Klaus Frieler

The DTL Team: Frank Höger, Simon Dixon, Polina Proutskova, Tillman Weyde, Daniel

Wolff, Helene-Camille Crayencour, Dogac Basaran, Geoffroy Peeters, Krin

Gabbard, Andrew Vogel, Gabriel Solis, Lucas Henry, Olga Velichkina, Klaus Frieler

Acknowledgements

DTL was funded under the Trans-Atlantic Program Digging into Data Challenge with

the support of the UK Economic and Social Research Council (ES/R004005/1), the

French National Research Agency (ANR-16-DATA-0005), the German Research

Foundation (PF 669/9-1), and the US National Endowment for the Humanities (NEH-

HJ-253587-17).

The Jazzomat Project was funded by the German Research Foundation (DFG-PF

669/7-1)