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Information
| Code | CENG722 |
| Name | Speech Enhancement |
| 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
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
Resources
Speech Enhancement Theory and Practice Philipos C. Loizou
Notes
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. | 4 |
| 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. | 4 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 4 |
| 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. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 3 |
| 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. | 3 |
| 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. | 4 |
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 | ||