Assigned Project Lab Papers

After the 7 structured labs that cover basic digital signal processing (DSP) operations, ECE 420 students will explore in depth a chosen fundamental DSP algorithm using high-level languages (such as MATLAB or Python) for 2 weeks on assigned project labs.

Later, the final projects should be built upon the assigned project lab. Students have to demonstrate their understanding of the algorithm and its implementation through oral quiz during the assigned project lab.

Students have to develop a testing and validation plan to demonstrate that the high-level implementation works. Methodology and results should be included in a short report.

Assigned Project Lab Proposal

Prior to the start of the Assigned Project Lab, a proposal must be submitted outlining the work to be performed. There is not a formal required structure for the Assigned Project Lab proposal, but it should address the following items.
  • Overview of the algorithm to be implemented, including citation of sources.
  • Plan for testing and validation of the algorithm's implementation.
  • Rough idea(s) for Final Project applications of the algorithm.

Recommended Papers

Following is a list of highly common and popular DSP algorithms that are used in many real-time DSP systems. Students should consult with the instructor and TAs in picking a paper that is fundamental to their intended final project.

[Audio processing]

Wang, Avery. "An industrial strength audio search algorithm." Ismir. Vol. 2003. 2003.

Cooper, Matthew, and Jonathan Foote. "Automatic Music Summarization via Similarity Analysis." ISMIR. 2002.

Pradhan, Swadhin, et al. "Smartphone-based acoustic indoor space mapping." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2.2 (2018): 1-26.

Graham, Daniel, et al. "A software-based sonar ranging sensor for smart phones." IEEE Internet of Things Journal 2.6 (2015): 479-489.

Rafii, Zafar, and Bryan Pardo. "Repeating pattern extraction technique (REPET): A simple method for music/voice separation." IEEE transactions on audio, speech, and language processing 21.1 (2012): 73-84.

Ellis, Daniel PW, and Bertin-Mahieux Thierry. "Large-scale cover song recognition using the 2d fourier transform magnitude." (2012): 241-246.

Hansen, John HL, and Taufiq Hasan. "Speaker recognition by machines and humans: A tutorial review." IEEE Signal processing magazine 32.6 (2015): 74-99.

[Image/Video processing]

Ballard, Dana H. "Generalizing the Hough transform to detect arbitrary shapes." Pattern recognition 13.2 (1981): 111-122.

Matthew Turk and Alex Pentland, "Eigenfaces for Recognition." Journal of Cognitive Neuroscience 1991 3:1, 71-86

Belhumeur, Peter N., João P. Hespanha, and David J. Kriegman. "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection." IEEE Transactions on pattern analysis and machine intelligence 19.7 (1997): 711-720.

Wagner, Andrew, et al. "Toward a practical face recognition system: Robust alignment and illumination by sparse representation." IEEE transactions on pattern analysis and machine intelligence 34.2 (2011): 372-386.

Butler, Darren E., V. Michael Bove, and Sridha Sridharan. "Real-time adaptive foreground/background segmentation." EURASIP Journal on Advances in Signal Processing 2005.14 (2005): 1-13.

Lowe, David G. "Object recognition from local scale-invariant features." Computer vision, 1999. The proceedings of the seventh IEEE international conference on. Vol. 2. Ieee, 1999.

S. Baker and I. Matthews, "Lukas-Kanade 20 years on: A unifying framework." International Journal of Computer Vision, vol. 56, no. 3, pp. 221-255, Mar. 2004.

Matthews, Iain, et al. "Extraction of visual features for lipreading." IEEE Transactions on Pattern Analysis and Machine Intelligence 24.2 (2002): 198-213.

Achanta, Radhakrishna, et al. "SLIC superpixels compared to state-of-the-art superpixel methods." IEEE transactions on pattern analysis and machine intelligence 34.11 (2012): 2274-2282.

Zitnick, C. Lawrence, and Piotr Dollár. "Edge boxes: Locating object proposals from edges." European conference on computer vision. Springer, Cham, 2014.

Yan, Qiong, et al. "Hierarchical saliency detection." Proceedings of the IEEE conference on computer vision and pattern recognition. 2013.

Cho, Sunghyun, Jue Wang, and Seungyong Lee. "Video deblurring for hand-held cameras using patch-based synthesis." ACM Transactions on Graphics (TOG) 31.4 (2012): 1-9.