The audio processing lab has several active projects with collaborators both internal and external to San Diego State University. Current and prior supporters of the lab include: The Office of Naval Research, Chief of Naval Operations N45 Environmental Readiness Division, and The Scripps Institution of Oceanography.
Bioacoustics
We work with the
Marine Physical Lab's Whale Acoustics group led by Dr. John Hildebrand at The Scripps
Institution of Oceanography. We use passive acoustic monitoring to learn about populations of marine mammals. Much of our recent work has focused on using echolocation clicks produced by toothed whales and dolphins (odontocetes) to determine species identity. Some of our efforts include development of real-time systems for platforms such as the waveglider. Other projects include the analysis of tonal calls in blue whales and isolating individual individual whistles in groups of vocalizing odontocetes.
Providing automated analysis of sounds produced by animals helps biologists to better understand the ecosystem by providing information about population dynamics (abundance, seasonality, etc.) and may eventually yield information about how they communicate with one another.
Current students: Chris Marsh, Johanna Stinner-Sloan, and Jonathan Walton.
A note to students interested in joining the lab
It is extremely rare to accept a student without the student having successfully completed CS 682, Speech Processing, which provides a graduate level introduction to speech and speaker recognition. Typically, we accept one or two new students per year, so successful completion of the course is not a guarantee that there will be an open position. Students who are interested in taking CS 682 (offered once per year) should endeavor to make sure they meet the minimum prerequisites for the course: CS 310 or COMPE 260 (Data Structures, higher level CS/COMPE typically expected). Although not required, a background in statistics is highly desireable, and knowledge of basic linear algebra is helpful.
Lab Alumni
- Tyler Olmstead, "Autonomous vehicle based real-time acoustic detection and classification of odontocetes", M.S. computer science, spring 2011.
- Simon Qiu, "Background models for reducing false positives in the detection of odontocetes through passive acoustic monitoring", M.S. computer science, fall 2010.
- Jim Du, “Real time enhanced frequency compression for hearing aids,” M.S. computer science, fall 2009.
- Bhavesh Patel, "A graph search algorithm for following odontocete whistle contours,", spring 2009.
- Shyam Kumar Madhusudhana, “Detection of Blue Fin Whale D Calls using hidden Markov models,” fall 2008.
- Rhonda Hoenigman, “Support vector machine classification for applications of auditory scene analysis,” M.S. computer science, summer 2007.
- Deborah Curless, “Automated sensor acquisition and classification,” M.S. computer science, Spring 2007.
- Sonia Arteaga, B.S computer engineering, Spring 2006.
- Jinyi Wang, “A maximum entropy approach for high level speaker recognition,” M.S. computer science,Summer 2006
- Tong Huang, “Audio Scene Analysis for Hearing Aids,” M.S. computer science, Summer 2005.
- Jing Liu, “Instantaneous labeling of gender,” M.S. computer science, Spring 2004.
- Yanliang Chang, “Application of the Bayesian information criterion to speaker change point detection,” M.S. computer science, Fall 2003.
- Min-Wei Chan, “A Study of integration approximation methods for the integral decode,” M.S. computer science,Spring 2003.
Previous areas of research
In general, the lab is no longer accepting students in these areas:
Speaker recognition
Speaker recognition is a form of biometric where the goal is to construct algorithms to determine a person's identity based upon their voice. Projects we have worked on include recognition in the presence of noise, speaker segmentation, extensions to Gaussian selection to set thresholds independently of the number of dimensions, and high level speaker recognition.
Control systems for hearing aids
In collaboration with the Assistive Devices Lab led by Dr. Richard Hurtig in
The Dept. of Speech Pathology and Audiology at The University of Iowa, we design dynamic control systems for
hearing aids. Our current system parameterizes a frequency compression algorithm which reduces the frequency
bandwidth of a signal while maintaining formant ratios. Formants are the harmonic frequencies which are
reinforced by our vocal tract and the ratio between formants is known to be important for perception.
By remapping the frequency domain, we move the formants to an audible range for a severely hearing impaired listener.