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.
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音乐应用的音频信号处理
庞佩乌·法布拉·大学課程信息
您將獲得的技能
- Digital Signal Processing
- Signal Processing
- Python Programming
- Fft Algorithms
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庞佩乌·法布拉·大学
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.

斯坦福大学
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.
授課大綱 - 您將從這門課程中學到什麼
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.
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- 5 stars88.12%
- 4 stars8.63%
- 3 stars1.79%
- 2 stars0.71%
- 1 star0.71%
來自音乐应用的音频信号处理的熱門評論
Very nice course with nice materials! I am recommending for technical students interested in audio/music as me!!!
i will thanks to management, i really need to learn this course because my FYP is based on this course
Amazing, Challenging and honestly learnt a lot. This course helped me decide my masters specialisation and I am genuinely excited to pursue it!!
This course was really complete and well structured, the instructor knows the topic really well and makes you want to learn more.
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