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.
ofrecido por

Audio Signal Processing for Music Applications
Universitat Pompeu Fabra of BarcelonaAcerca de este Curso
Habilidades que obtendrás
- Digital Signal Processing
- Signal Processing
- Python Programming
- Fft Algorithms
ofrecido por

Universitat Pompeu Fabra of Barcelona
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.

Universidad de Stanford
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.
Programa - Qué aprenderás en este curso
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.
Reseñas
- 5 stars88,12 %
- 4 stars8,63 %
- 3 stars1,79 %
- 2 stars0,71 %
- 1 star0,71 %
Principales reseñas sobre AUDIO SIGNAL PROCESSING FOR MUSIC APPLICATIONS
good course . If there will any Speech recognition course it will useful > Hope Coursera will start speech recognition course in future .
A course which gives an overview of many transforms each with a theory, demonstrative and programming classes with quizzes but the assignments could have been a less difficulty level.
An absolutely awesome introduction to Audio Signal Processing. The additive introduction of new concepts is capable of teaching any beginner this topic which ordinarily is difficult to understand.
I learned a lot during this course. It took quite a lot of time and energy to complete it, but I'm glad I did. It is now much easier to follow the text of Richard Lyons' book. Highly recommended.
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