Audio pattern recognition
A.A. 2018/2019
Obiettivi formativi
- Understand the operation of standard data mining tools
- Ability to design and implement the entire pipeline of an audio pattern recognition system
- Ability to design and implement the entire pipeline of an audio pattern recognition system
Risultati apprendimento attesi
Non definiti
Periodo: Primo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Linea Milano
Responsabile
Periodo
Primo semestre
STUDENTI FREQUENTANTI
Programma
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
Informazioni sul programma
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
Propedeuticità
digital signal processing, statistics
Prerequisiti
digital signal processing, statistics
exam is project development and oral examination
exam is project development and oral examination
Metodi didattici
Oral presentations and practice lessons
Materiale di riferimento
STUDENTI NON FREQUENTANTI
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
Programma
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
Prerequisiti
digital signal processing, statistics
exam is project development and oral examination
exam is project development and oral examination
Materiale di riferimento
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
Docente/i