I was born in Dec. 10, 1962 in Haifa, in Israel.
I am interested to know how people acquire their tacit knowledge
on music, and how do they learn to perform in real time so well, complex
musical activities such as listening to music.
For this aim, I take ideas from three disciplines: Artificial Intelligence(AI),
Music and Cognition.
I develop a modular approach in building recurrent
neural network (RNN) architectures for real time performance of music. The
publications below describe how we apply these
architectures for on-line learning of: chord sequences or harmonizing
melodies in real time performance situation; modeling listener's
expectations: ambiguity, surprises, and meter recognition.
We also built a hybrid system that integrates recurrent net with DAI
agent-based modules, applied to composing polyphonic music in real time and
for representing musical knowledge.
Currently, I am Fulbright Post-Doctoral Fellow at Stanford University at the
Center for Computer Research in Music and Acoustics CCRMA . My Ph.D research was completed
at the Hebrew University in
Jerusalem at the Institute of Computer Scienc in the AI lab.
If this subject seems to be interesting, I would like to refer you to the
course: Music,
Cognition and Artificial Intelligence (Hebrew University, 1997);
and to the course: Modeling Creativity
(Stanford University-CCRMA, 1999) which were tought by me.
To read my publications, please follow the links by clicking on
the name of the papers below:
C. Goldman, D. Gang, J. Rosenschein and D. Lehmann.
NetNeg: a Connectionist-Agent Integrated System for Representing Musical
Knowledge, Annals of Mathematics and Artificial Intelligence ,
pp. 69-90, 1999.
Figures of the paper:
fig1,
learningEx ,
musicalEx ,
examples ,
results
D. Gang, D. Lehmann, C. Goldman and J. Rosenschein. Knowledge Representation for Interactive Computer Music: the Hybrid Approach, Hybrid Systems and AI: Modeling, Analysis and Control of Discrete + Continuous Systems, AAAI Technical Report SS-99-05, pp. 59-64, March, 1999.
D. Gang and J. Berger. A Unified Neurosymbolic Model of the Mutual Influence of Memory, Context and Prediction of Time Ordered Sequential Events During the Audition of Tonal Music, Hybrid Systems and AI: Modeling, Analysis and Control of Discrete + Continuous Systems, AAAI Technical Report SS-99-05, pp.53-58, March, 1999.
J. Berger and D. Gang
A Real Time Model for Formulation and Realization of Musical Expectation,
- In review process.
Submitted to the Journal: Music Perception in July, 1999.
Figures of the paper:
fig1,
fig2,
fig3,
fig4,
fig5,
fig6,
fig7,
fig8,
fig9a,
fig9b,
fig9c,
fig10,
D. Gang, D. Lehmann and N. Wagner. Tuning Neural Network for Harmonizing Melodies in Real-Time, International Computer Music Conference (ICMC98), Ann-Arbor, Michigan.
J. Berger and D. Gang. A Computational Model of Meter Cognition During the Audition of Functional Tonal Music: Modeling A-priori Bias in Meter Cognition, International Computer Music Conference (ICMC98), Ann-Arbor, Michigan.
D. Gang, D. Lehmann and N. Wagner. Harmonizing Melodies in Real-Time: the Connectionist Approach , in Proceedings of the International Computer Music Conference, Thessaloniki, September 1997.
J. Berger and D. Gang. A Neural Network Model of Metric Perception and Cognition in the Audition of Functional Tonal Music, in Proceedings of the International Computer Music Conference, Thessaloniki, September 1997.
D. Gang, G. Chockler, T. Anker, A. Kremer and T. Winkler. TransMIDI: A System for MIDI Sessions Over the Network Using Transis , in Proceedings of the International Computer Music Conference, Thessaloniki, September 1997.
C. Goldman, D. Gang, J. Rosenschein and D. Lehmann. NetNeg: A Hybrid Interactive Architecture for Composing Polyphonic Music in Real Time, in Proceedings of the International Computer Music Conference , Hong Kong, August 1996.
D. Gang and J. Berger Modeling the Degree of Realized Expectation in Functional Tonal Music: A Study of Perceptual and Cognitive Modeling Using Neural Networks, in Proceedings of the International Computer Music Conference, Hong Kong, August 1996.
D. Gang Studio Report: The Institute of Computer Science, Hebrew University, in Proceedings of the International Computer Music Conference, Hong Kong, August 1996.
D. Gang and D. Lehamnn Melody Harmonization with Neural Nets, with Daniel Lehmann. in Proceedings of the ICMC95 , Banff, September 1995.
C. Goldman, D. Gang and J. Rosenschein. NetNeg: A Hybrid System Architecture for Composing Polyphonic Music, Workshop on Artificial Intelligence and Music at the Fourteenth International Joint Conference on Artificial Intelligence , Montreal, August 1995.
Tuning a Neural Network for Harmonizing Melodies in Real-Time , with: Daniel Lehmann and Naptali Wagner. in Proceedings of the International Computer Music Conference, Michigan, Ann Arbor, October 1998.
Following is a typical representative example which results from our neural network model. The model produces hundreds of results and the following result is not nessecarily the best, from the aesthetic point of view. We use this example to demonstrait some common features that are often found in the many of the resulted harmonization.
The net learns relations between important notes of the melody and their harmonies and is able to produce harmonies for new melodies in real-time. The net ability to generalize is examined by providing it unfamiliar melodies' notes to harmonize in real-time (Swanee River is an example from the generalization set). Real-time harmonization here means, harmonizing without advanced knowledge of the continuation of the melody. I would like to refer you to the section Musical Results, of the paper above, for more detailed and analysis of the results.
Musical Results
Score
In the score of the song Swanee River the upper harmonization is the output of the neural network, the lower found in the book.
Audio Example of the source
To hear an audio example of the harmonized melody of the source book follow the link- Swanee River in wav format (source) .
The neural network harmonization
The net real-time harmonization results in comparison with the source harmonization found in the book:
GANG band in mp3 format
List of Songs (partial)
Mechabek Ota (Give a huge to her)
Notsa Baruach (Feather in the wind)
At Yacholt laset(You could stand it)
© 1998, Dan Gang ;dang@ccrma.stanford.edu>, All rights reserved.Phones:1-650-723-4971 ex. 350(w) 1-650-322-2151(h)
Fax: 1-650-723-8468
Email: dang@ccrma.stanford.edu
http://www-ccrma.stanford.edu/~dang
Address at Stanford:
CCRMA, Department of Music
Stanford University
Stanford, CA 94305