Document Type

Conference Paper

Version Deposited

Published Version

Publication Date


Conference Name

2017 ASEE Annual Conference & Exposition


A blind approach for estimating the signal to noise ratio (SNR) of a speech signal corrupted by additive noise is proposed. The method is based on a pattern recognition paradigm using various linear predictive based features, a vector quantizer classifier and estimation combination. Blind SNR estimation is very useful in biometric speaker identification systems in which a confidence metric is determined along with the speaker identity. The confidence metric is partially based on the mismatch between the training and testing conditions of the speaker identification system and SNR estimation is very important in evaluating the degree of this mismatch. The educational impact of this project is two-fold: 1. Undergraduate students are initiated into research/development by working on a team to achieve a software implementation of the SNR estimation system. The students will also evaluate the performance of the system by experimenting with different features and classifiers. Producing a paper in a refereed technical conference is the objective. 2. The students will also write a laboratory manual for a portion of this project to be run in a junior level signals and systems class and a senior level class on speech processing. Producing a paper in a refereed education conference is the objective. The learning outcomes for the students engaged in research and for the students doing the project in a class include: • Enhanced application of math skills • Enhanced software implementation skills • Enhanced interest in signal processing • Enhanced ability to analyze experimental results • Enhanced communication skills. The assessment instruments include: • Student surveys (target versus control group comparison that includes a statistical analysis) • Faculty tracking of student learning outcomes based on student work • Faculty evaluation of student work based on significant rubrics • A concept inventory test


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