In this course you will learn about audio signal processing methodologies that are specific for music and of use in real applications. We focus on the spectral processing techniques of relevance for the description and transformation of sounds, developing the basic theoretical and practical knowledge with which to analyze, synthesize, transform and describe audio signals in the context of music applications.
제공자:

Audio Signal Processing for Music Applications
바르셀로나 폼페우 파브라 대학교이 강좌에 대하여
귀하가 습득할 기술
- Digital Signal Processing
- Signal Processing
- Python Programming
- Fft Algorithms
제공자:

바르셀로나 폼페우 파브라 대학교
Pompeu Fabra University (UPF) is a modern public university, conveniently located in the centre of Barcelona (Spain) with the aim of providing top quality education and standing out as a research-based university. UPF is both a specialised university with a unique teaching model and a cutting-edge research institution. UPF places a strong emphasis on quality teaching, based on comprehensive education and student-centred learning, and innovation in the learning processes. UPF’s MOOCs are produced within this general goal.

스탠퍼드 대학교
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
강의 계획표 - 이 강좌에서 배울 내용
Introduction
Introduction to the course, to the field of Audio Signal Processing, and to the basic mathematics needed to start the course. Introductory demonstrations to some of the software applications and tools to be used. Introduction to Python and to the sms-tools package, the main programming tool for the course.
Discrete Fourier transform
The Discrete Fourier Transform equation; complex exponentials; scalar product in the DFT; DFT of complex sinusoids; DFT of real sinusoids; and inverse-DFT. Demonstrations on how to analyze a sound using the DFT; introduction to Freesound.org. Generating sinusoids and implementing the DFT in Python.
Fourier theorems
Linearity, shift, symmetry, convolution; energy conservation and decibels; phase unwrapping; zero padding; Fast Fourier Transform and zero-phase windowing; and analysis/synthesis. Demonstration of the analysis of simple periodic signals and of complex sounds; demonstration of spectrum analysis tools. Implementing the computation of the spectrum of a sound fragment using Python and presentation of the dftModel functions implemented in the sms-tools package.
Short-time Fourier transform
STFT equation; analysis window; FFT size and hop size; time-frequency compromise; inverse STFT. Demonstration of tools to compute the spectrogram of a sound and on how to analyze a sound using them. Implementation of the windowing of sounds using Python and presentation of the STFT functions from the sms-tools package, explaining how to use them.
검토
- 5 stars88.12%
- 4 stars8.63%
- 3 stars1.79%
- 2 stars0.71%
- 1 star0.71%
AUDIO SIGNAL PROCESSING FOR MUSIC APPLICATIONS의 최상위 리뷰
Top class! Very well explained, good examples, excellent learning material, practical exercises, and lots and lots of room for further personal study! Well done guys, and especially Xavier! Cheers!
Great Course, lots of concepts to learn and implement! I had amazing time doing the assignments. Should be an ASPMA part 2 course.
i will thanks to management, i really need to learn this course because my FYP is based on this course
Excellent material for an interesting subject. The video lectures are very good. The course does need some updates for Python3 and associated libraries to stay current.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
Can I take this course for free?
Can I pay to get a Course Certificate?
What resources will I need for this class?
Do I need to buy a textbook for the course?
How much programming background is needed for the course?
What is the coolest thing I'll learn if I take this class?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.