Audio pattern recognition

A.Y. 2021/2022
6
Max ECTS
48
Overall hours
SSD
INF/01
Language
English
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.
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.
Course syllabus and organization

Single session

Responsible
Lesson period
First semester
The lessons will be held in presence and on the Teams platform following the hybrid modality.
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
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
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
Educational website(s)