Eric Battenberg

I have recently received my PhD in Electrical Engineering and Computer Sciences from UC Berkeley, where I did research on the application of signal processing and machine learning techniques to music and audio analysis and processing.

I was advised by David Wessel at the Center for New Music and Audio Technologies (CNMAT) and co-advised by Nelson Morgan at the International Computer Science Institute (ICSI). I worked with the Parallel Computing Laboratory (Par Lab) on parallel music applications. My research interests include music information retrieval, audio signal processing, applications of machine learning, and architecting parallel music software.

You can contact me at:

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My CV is available upon request.

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Publications

Techniques for Machine Understanding of Live Drum Performances: Techniques for drum detection, multi-hypothesis beat tracking, and drum pattern analysis are presented as a complete system for drum understanding.; Eric Battenberg; PhD Dissertation, EECS, UC Berkeley, Dec 2012
Analyzing Drum Patterns Using Conditional Deep Belief Networks: Applies multi-layer neural networks to the analysis of drum patterns.; Eric Battenberg and David Wessel; ISMIR, Porto, Portugal, 2012
Toward Live Drum Separation Using Probabilistic Spectral Clustering Based on the Itakura-Saito Divergence: This paper introduces techniques for decomposing drum audio onto spectral templates which are learned using a probabilistic Gamma Mixture Model.; Eric Battenberg, Victor Huang, and David Wessel; AES 45th Conference: Applications of Time-Frequency Processing in Audio, Helsinki, Finland, March 2012
Implementing Real-Time Partitioned Convolution Algorithms on Conventional Operating Systems: Compares the performance, plugin compatibility, and required programmer effort of preemptive and time-distributed implementations of non-uniform partitioned convolution.; Eric Battenberg and Rimas Avizienis; Digital Audio Effects Conference, Paris, France, Sept 2011
Real-Time Musical Applications on an Experimental Operating System for Multi-Core Processors: Describes the operating system needs of real-time music software and a current approach to meeting these needs.; Juan Colmenares, Ian Saxton, Eric Battenberg, Rimas Avizienis, Nils Peters, Krste Asanovic, John D. Kubiatowicz, and David Wessel; International Computer Music Conference, Huddersfield, UK, Aug 2011
Advances in the Parallelization of Music and Audio Applications: A survey of parallel computer music work being done at CNMAT and the Par Lab.; Eric Battenberg, Adrian Freed, and David Wessel, June 2010; International Computer Music Conference, New York City/Stony Brook, New York, Jun 2010
Accelerating Non-Negative Matrix Factorization for Audio Source Separation on Multi-Core and Many-Core Architectures: | poster |; OpenMP and CUDA implementations of NMF to speed up drum track extraction.; Eric Battenberg and David Wessel, May 2009; International Society for Music Information Retrieval Conference, Kobe, Japan, 2009
Improvements to Percussive Component Extraction Using Non-Negative Matrix Factorization and Support Vector Machines: Perceptual dimensionalty reduction and new features are used to improve the speed and performance of automatic drum track extraction.; Eric Battenberg; Masters Thesis, EECS, UC Berkeley, Dec 2008
Optimizing Hearing Aids for Music Listening: A subspace technique for optimal hearing aid fitting.; David Wessel, Kelly Fitz, Eric Battenberg, Andrew Schmeder, and Brent Edwards; 19th International Congress on Acoustics, Madrid, Spain, Sep 2007.

Software

If you use any of this code, please send me an email to let me know how you plan on using it; I'd love to hear. Also, your feedback will help determine where the code needs to be improved. Feel free to send me any questions you have about its use. If you use any of this code for any published work, please cite the appropriate paper.

NMF-CUDA: A CUDA implementation of non-negative matrix factorization for GPUs as described in the ISMIR 2009 pulbication above.
CRBM-Drum-Patterns: A Python implement of conditional restricted Boltzmann machine training and generation as applied to drum pattern generation.

Talks

The Breadth of Applications for Music: | slides |; The range of music apps being pursued at CNMAT and what they need from parallel computing. Also, a case study on parallelizing audio source separation on OpenMP and CUDA.; Eric Battenberg, May 2009.; UPCRC Applications Workshop, Microsoft Research, Redmond, WA, 29/05/09.

Projects

A Theoretical and Experimental Analysis of the Acoustic Guitar: | slides |; Automatically recognizing picking location and a look into natural harmonics and tuning systems.; Eric Battenberg, May 2009.
An Interior-Point Newton Algorithm for Non-negative Matrix Factorization: A Netwton step barrier method for non-negative matrix factorization is proposed and applied to audio source separation.; Eric Battenberg, Dec. 2008.
Parallelizing Audio Feature Extraction Using an Automatically-Partitioned Streaming Dataflow Language: | poster | | slides |; An attempt to use StreamIt for spectral feature extraction.; Eric Battenberg and Mark Murphy, May 2008.
Calculating Musical Rhythm Similarity: | poster |; A method for comparing rhythms using self-similarity.; Eric Battenberg, Dec. 2007.
Optimizing the Hearing Aid Musical Experience: Parameters of a multi-band compressor are tuned using a user-calibrated subjective parameter space.; Eric Battenberg, May 2007
A New Method for Calculating Music Similarity: Hidden Markov models and spectral fluctuation patterns are used to calculate a distance measure between songs.; Eric Battenberg and Vijay Ullal, Dec. 2006
A System for Automatic Cell Segmentation of Bacterial Microscopy Images: Various image processing and computer vision techniques are used to segment individual bacteria cells in a microscope image.; Eric Battenberg and IlkaBischofs-Pfeifer, Aug. 2006
Sparse Signal Representation: Image Compression Using Sparse Bayesian Learning.; Eric Battenberg, Vijay Ullal, and Galen Reeves, May 2006.

UCB Coursework

Year	Semester	Course	Title	Instructor
2005-06	Fall	EE 221A	Linear System Theory	S. Sastry
		EE 226A	Random Processes in Systems	J. Walrand
		EE 301	Teaching Electrical Engineering	B. Ayazifar
	Spring	EE 225A	Digital Signal Processing	M. Gastpar
		EE 225B	Digital Image Processing	A. Zakhor
		EE 290P	Brain-Machine Interfaces	J. Carmena
2006-07	Fall	CS 294-34	Practical Machine Learning	M. Jordan
	Fall	Music 208A	Advanced Music Perception and Cognition	D. Wessel
	Spring	EE 225D	Audio Signal Processing	N. Morgan
2007-08	Fall	CS 281A	Statistical Learning Theory	M. Jordan
2007-08	Spring	CS 267	Applications of Parallel Computers	H. Simon
2008-09	Fall	EE 227A	Introduction to Convex Optimization	L. El Ghaoui
	Spring	CS 294-45	Architecting Parallel Software	K. Keutzer
	Spring	ME 173	Fundamentals of Acoustics	G. Johnson
2009-10	Spring	CS 294-35	Cell Phones as a Computing Platform	E. Brewer

Teaching

Year	Semester	Course	Title	Position	Instructor
2005-06	Fall	EE 21	Signals and Systems	GSI	B. Ayazifar
2005-06	Spring	EE 123	Digital Signal Processing	Head GSI	K. Ramchandran
2006-07	Fall	EE 21	Signals and Systems	GSI	B. Ayazifar