Help high school students prepare for the HSA exam in the spring by tutoring them in the Fall.

• Receive Two Year Membership to ASM
• Earn up to $4000 in student stipend
• Participate in the ASM Fellows Presentation Institute prior to presenting research at the ASM General Meeting
• Receive up to $1000 in travel support to the ASM General Meeting (if abstract is accepted)
• Recognition in ASM publications

Masters Thesis Defense

Approximation of Nonintegral Frequency Moments

Brian Pilz

10:00am 30 November 2011, ITE325b

Let a data stream have length m over an alphabet of n letters, with letter i occurring m_i times for i = 1,…,n. For any k, define the frequency moments F_k as F_k = \sum_{i=1}^n m_i^k. Alon, Matias, and Szegedy showed how to estimate F_k for integers k>0 with a one-pass algorithm using O(n^{1-1/k}log n) space for given length m, accuracy, and confidence. Here we extend those results to non-integral k obtaining bounds on the variance giving accuracy and confidence estimates, and giving quantitative results on the algorithm’s space requirements with particular interest to when k is near 1. We also give some performance statistics of the algorithm for these cases and consider an application to entropy estimation. This algorithm is known as a sketching algorithm. Sketching algorithms are probabilistic algorithms generally requiring sublinear space vs. a "classical" O(n) (linear) space requirement, and may have applications for anomaly detection of systems or networks.

Committee:

• Drs. Samuel Lomonaco
• Brooke Stephens
• Kostas Kalpakis (chair)
• Larry Wagoner

EE Graduate Seminar

Time-Varying Amplitude Compression Processing to
Preserve and Enhance Spectral Contrast in Speech Signals

Dr. Janet C. Rutledge
Dean, UMBC Graduate School
Vice-Provost for Graduate Education
Affiliate Associate Professor of Electrical Engineering

11:30-12:45 Friday, 18 November 2011, ITE 231

Multichannel amplitude compression processing is used to reduce the level variations of speech to fit the reduced dynamic ranges of listeners with sensorineural hearing loss. This processing, however, can result in smearing of temporal information, artifacts due to spectral discontinuities at fixed channel edges, and spectral flattening due to reduced peak-to-valley ratios. Presented here is an implementation of a time-varying compression processing algorithm based on a sinusoidal speech model. The algorithm operates on a time-varying, stimulus-dependent basis to adjust to the speech variations and the listeners hearing profile. The algorithm provides fast-acting compression with minimal artifact, has time-varying frequency channels, is computationally inexpensive and preserves the important spectral peaks in speech.

This method has been extended to provide real-time enhancement of spectral peaks and valleys. This work is also related to processing audio signals that will be transmitted over amplitude-limited noisy channels or for listeners in a noisy environment.

Dr. Janet Rutledge is Dean of the Graduate School and Affiliate Associate Professor in the CSEE Department at UMBC. She received the BS in electrical engineering from Rensselaer Polytechnic Institute and the MS and Ph.D. in electrical engineering from Georgia Tech. Prior to coming to UMBC in 2001, she was a faculty member at Northwestern University, and program director at the National Science Foundation.

Host: Prof. Joel M. Morris

EE Graduate Seminar

Time-Varying Amplitude Compression Processing to
Preserve and Enhance Spectral Contrast in Speech Signals

Dr. Janet C. Rutledge
Dean, UMBC Graduate School
Vice-Provost for Graduate Education
Affiliate Associate Professor of Electrical Engineering

11:30-12:45 Friday, 18 November 2011, ITE 231

Multichannel amplitude compression processing is used to reduce the level variations of speech to fit the reduced dynamic ranges of listeners with sensorineural hearing loss. This processing, however, can result in smearing of temporal information, artifacts due to spectral discontinuities at fixed channel edges, and spectral flattening due to reduced peak-to-valley ratios. Presented here is an implementation of a time-varying compression processing algorithm based on a sinusoidal speech model. The algorithm operates on a time-varying, stimulus-dependent basis to adjust to the speech variations and the listeners hearing profile. The algorithm provides fast-acting compression with minimal artifact, has time-varying frequency channels, is computationally inexpensive and preserves the important spectral peaks in speech.

This method has been extended to provide real-time enhancement of spectral peaks and valleys. This work is also related to processing audio signals that will be transmitted over amplitude-limited noisy channels or for listeners in a noisy environment.

Dr. Janet Rutledge is Dean of the Graduate School and Affiliate Associate Professor in the CSEE Department at UMBC. She received the BS in electrical engineering from Rensselaer Polytechnic Institute and the MS and Ph.D. in electrical engineering from Georgia Tech. Prior to coming to UMBC in 2001, she was a faculty member at Northwestern University, and program director at the National Science Foundation.

Host: Prof. Joel M. Morris