Audio Pattern Recognition
A.Y. 2024/2025
Learning objectives
The course aims at introducing the students to fundamental concepts of data mining algorithms and how these are adapted for needs of audio signal processing and recognition. The principal
statistical modeling techniques are presented inluding neural networks and hidden Markov models.
statistical modeling techniques are presented inluding neural networks and hidden Markov models.
Expected learning outcomes
The student is expected to understand the operation of the princical data mining algorithms. The student will gain the ability to design and implement the entire pipeline of an audio pattern
recognition system.
recognition system.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Data mining
-statistics
-clustering
-classification
-anomaly detection
Audio analysis
-signal transformations
-filtering
-feature extraction
-pattern recognition
-alignment and temporal modeling
-music information retrieval
-audio enhancement
-statistics
-clustering
-classification
-anomaly detection
Audio analysis
-signal transformations
-filtering
-feature extraction
-pattern recognition
-alignment and temporal modeling
-music information retrieval
-audio enhancement
Prerequisites for admission
It is suggested that the student is familiar with the content of digital signal processing and statistics.
Teaching methods
Oral presentations and practice lessons
Teaching Resources
Books
1. Introduction to Data Mining (Second Edition)
2. Data Mining Practical Machine Learning Tools and Techniques (weka book)
3. Introduction to Audio Analysis
4. Speech Enhancement: Theory and Practice
1. Introduction to Data Mining (Second Edition)
2. Data Mining Practical Machine Learning Tools and Techniques (weka book)
3. Introduction to Audio Analysis
4. Speech Enhancement: Theory and Practice
Assessment methods and Criteria
Project development, paper writing, and oral examination. The evaluation is expressed in thirtieths.
INF/01 - INFORMATICS - University credits: 6
Lessons: 48 hours
Professor:
Ntalampiras Stavros
Shifts:
Turno
Professor:
Ntalampiras StavrosProfessor(s)