CENG722 Speech Enhancement

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG722
Name Speech Enhancement
Term 2024-2025 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Prof. Dr. ZEKERİYA TÜFEKCİ


Course Goal / Objective

The objective of this course is to provide basic speech enhancement techniques including spectral substractive algorithms, wiener filtering, statistical model based algorithms, subspace algorithms, and noise estimation algorithms

Course Content

This course covers basic speech enhancement techniques including spectral substractive algorithms, wiener filtering, statistical model based algorithms, subspace algorithms, and noise estimation algorithms

Course Precondition

no prerequisites

Resources

Speech Enhancement Theory and Practice Philipos C. Loizou

Notes

Speech Enhancement Jacob Benesty , Shoji Makino , Jingdong Chen


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Knows spectral substractive algorithms.
LO02 Knows wiener filtering
LO03 Knows statistical model based algorithms
LO04 Knows subspace algorithms
LO05 Knows noise estimation algorithms


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering.
PLO02 Bilgi - Kuramsal, Olgusal By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. 1
PLO03 Yetkinlikler - Öğrenme Yetkinliği Being aware of the new and developing practices of his / her profession and examining and learning when necessary. 2
PLO04 Yetkinlikler - Öğrenme Yetkinliği Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. 4
PLO05 Yetkinlikler - Öğrenme Yetkinliği Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. 4
PLO06 Yetkinlikler - Öğrenme Yetkinliği Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design.
PLO07 Beceriler - Bilişsel, Uygulamalı Has the skills of learning.
PLO08 Beceriler - Bilişsel, Uygulamalı Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. 4
PLO09 Beceriler - Bilişsel, Uygulamalı Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. 3
PLO10 Beceriler - Bilişsel, Uygulamalı Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering.
PLO11 Beceriler - Bilişsel, Uygulamalı Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. 3
PLO12 Bilgi - Kuramsal, Olgusal Observes social, scientific and ethical values in all professional activities.


Week Plan

Week Topic Preparation Methods
1 Spectral Substractive Algorithms Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
2 Nonlinear Spectral Substraction Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
3 Wiener Filtering Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
4 İterative Wiener Filtering Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
5 Statistical Model Based Algorithms Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
6 Maximum Likelihood Estimators Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
7 Bayesian Estimator Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
8 Mid-Term Exam Reading lecture notes and related chapters in the textbook Ölçme Yöntemleri:
Yazılı Sınav
9 MMSE Estimator Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
10 Subspace Algorithms Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
11 SVD Based Algorithms Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
12 EVD Based Algorithms Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
13 Noise Estimation Algorithms Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
14 Minimal Statistic Noise Estimation Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
15 Histogram Based Techniques Reading related chapter in the textbook Öğretim Yöntemleri:
Anlatım, Soru-Cevap, Tartışma
16 Term Exams Reading lecture notes and related chapters in the textbook Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Reading lecture notes and related chapters in the textbook Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 24.05.2024 05:00