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

This Saturday, November 19, students enrolled in Introduction to Engineering Science (ENES 101) will put robotic vessels to the test during UMBC's first AROW competition. AROW–Academy Robotics on the Water–was developed by Captain Jonathan Russell, Lt. Cmdr. Brian Maggi and Stephen Grenier of the US Coast Guard Academy as an introductory engineering design experience. The competition requires students to design robotic vessels capable of performing simulated tasks akin to those of the US Coast Guard. Forty teams will compete to see how many tasks can be performed by their robotic vessels within a four minute time limit.

What: UMBC AROW Competition

When: Saturday, November 19th 9:30 a.m. to Noon, 1:30 p.m. to 4:00 p.m.

Where: Engineering/ Computer Science Building (ECS), 2nd Floor Atrium

Click here for more information.

This Saturday, November 19, students enrolled in Introduction to Engineering Science (ENES 101) will put robotic vessels to the test during UMBC's first AROW competition. AROW–Academy Robotics on the Water–was developed by Captain Jonathan Russell, Lt. Cmdr. Brian Maggi and Stephen Grenier of the US Coast Guard Academy as an introductory engineering design experience. The competition requires students to design robotic vessels capable of performing simulated tasks akin to those of the US Coast Guard. Forty teams will compete to see how many tasks can be performed by their robotic vessels within a four minute time limit.

What: UMBC AROW Competition

When: Saturday, November 19th 9:30 a.m. to Noon, 1:30 p.m. to 4:00 p.m.

Where: Engineering/ Computer Science Building (ECS), 2nd Floor Atrium

Click here for more information.

Ph.D. Dissertation Defense

Identifying and Isolating Text Classification Signals
from Domain and Genre Noise for Sentiment Analysis

Justin Martineau

1:30-4:00 Friday, 18 November 2011, ITE 325b, UMBC

Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. This thesis provides methods to identify, characterize, and isolate the sentiment bearing terms to improve textual sentiment classification when there is little or no labeled data for the domain.

We introduce a new theoretical framework that explains the different sources of noise that affect term level sentiment bias. This noise comes from the genre the author communicates in and the domain or general topic that the author is writing about. To understand the affects of domain noise we defined sentimental domain independence and statistically described it in the multi-domain product review data set. This allowed us to design a Domain Independence Verification Algorithm (DIVA) to eliminate this noise and produce a domain-independent sentiment model using data drawn from a variety of different domains. This model is the most accurate method to classify documents in the 25 category product review data set.

Committee:

• Dr. Tim Finin (chair)
• Dr. Marie desJardins
• Dr. Akshay Java
• Dr. James Mayfield
• Dr. Tim Oates

Ph.D. Dissertation Defense

Identifying and Isolating Text Classification Signals
from Domain and Genre Noise for Sentiment Analysis

Justin Martineau

1:30-4:00 Friday, 18 November 2011, ITE 325b, UMBC

Sentiment analysis is the automatic detection and measurement of sentiment in text segments by machines. This thesis provides methods to identify, characterize, and isolate the sentiment bearing terms to improve textual sentiment classification when there is little or no labeled data for the domain.

We introduce a new theoretical framework that explains the different sources of noise that affect term level sentiment bias. This noise comes from the genre the author communicates in and the domain or general topic that the author is writing about. To understand the affects of domain noise we defined sentimental domain independence and statistically described it in the multi-domain product review data set. This allowed us to design a Domain Independence Verification Algorithm (DIVA) to eliminate this noise and produce a domain-independent sentiment model using data drawn from a variety of different domains. This model is the most accurate method to classify documents in the 25 category product review data set.

Committee:

• Dr. Tim Finin (chair)
• Dr. Marie desJardins
• Dr. Akshay Java
• Dr. James Mayfield
• Dr. Tim Oates

Congratulations to Dr. Yelena Yesha, professor of Computer Science and Electrical Engineering, for receiving this year's IBM CAS Faculty Fellow of the Year award. 

The award is a recognition of Dr. Yesha's positive impact on the goals and reputation of IBM, as well as her influence on IBM's Center for Advanced Studies (CAS) as an ambassador in promoting IBM in academia.

Congratulations to Dr. Yelena Yesha, professor of Computer Science and Electrical Engineering, for receiving this year's IBM CAS Faculty Fellow of the Year award. 

The award is a recognition of Dr. Yesha's positive impact on the goals and reputation of IBM, as well as her influence on IBM's Center for Advanced Studies (CAS) as an ambassador in promoting IBM in academia.

EE Graduate Seminar

Stochastic Graph Grammars

Prof. Tim Oates
Associate Professor of Computer Science
Computer Science and Electrical Engineering, UMBC

11:30am Friday November 11, ITE 231, UMBC

Many important domains are naturally described relationally, often using graphs in which nodes correspond to entities and edges to relations. Stochastic graph grammars compactly represent probability distributions over graphs and can be learned from data, such as a set of graphs corresponding to proteins that have the same function.

In this talk we consider the problem of learning the parameters (i.e., the production probabilities) of stochastic graph grammars and the structure of the grammar (i.e., the productions) given a representative sample of graphs taken from the underlying distribution. We also present efficient algorithms for computing properties of the distribution over graphs defined by a graph grammar such as expectations of graph size, node degree, and number of edges.

Dr. Tim Oates is an Associate Professor in the CSEE Department at UMBC. He received B.S. degrees in Computer Science and Electrical Engineering from North Carolina State University in 1989, and M.S. and PhD degrees from the Univ of Massachusetts Amherst in 1997 and 2000, respectively. Prior to coming to UMBC in Fall 2001, Prof. Oates spent a year as a postdoc in the Artificial Intelligence Lab at MIT.

Host: Prof. Joel M. Morris

EE Graduate Seminar

Stochastic Graph Grammars

Prof. Tim Oates
Associate Professor of Computer Science
Computer Science and Electrical Engineering, UMBC

11:30am Friday November 11, ITE 231, UMBC

Many important domains are naturally described relationally, often using graphs in which nodes correspond to entities and edges to relations. Stochastic graph grammars compactly represent probability distributions over graphs and can be learned from data, such as a set of graphs corresponding to proteins that have the same function.

In this talk we consider the problem of learning the parameters (i.e., the production probabilities) of stochastic graph grammars and the structure of the grammar (i.e., the productions) given a representative sample of graphs taken from the underlying distribution. We also present efficient algorithms for computing properties of the distribution over graphs defined by a graph grammar such as expectations of graph size, node degree, and number of edges.

Dr. Tim Oates is an Associate Professor in the CSEE Department at UMBC. He received B.S. degrees in Computer Science and Electrical Engineering from North Carolina State University in 1989, and M.S. and PhD degrees from the Univ of Massachusetts Amherst in 1997 and 2000, respectively. Prior to coming to UMBC in Fall 2001, Prof. Oates spent a year as a postdoc in the Artificial Intelligence Lab at MIT.

Host: Prof. Joel M. Morris