|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/audio-signal-processing
课程评论：没有评论
课程名称：音乐应用的音频信号处理 课程概述：在本课程中，您将学习针对音乐的音频信号处理方法，这些方法在实际应用中非常有用。课程重点关注与声音描述和转换相关的谱处理技术，旨在培养分析、合成、转换和描述音频信号的基本理论和实践知识，尤其是在音乐应用的背景下。课程基于开放软件和内容进行，演示和编程练习使用Python在Ubuntu系统下进行，参考材料来自开放的在线资源。我们还提供开放许可的软件和课程材料。 课程大纲： 1. **介绍**：课程介绍，音频信号处理领域概述，以及开始课程所需的基础数学知识。介绍将使用的一些软件应用程序和工具，以及Python和sms-tools包的介绍。 2. **离散傅里叶变换**：介绍离散傅里叶变换方程，复杂指数，DFT中的标量积，复正弦波和实正弦波的DFT及逆DFT。演示如何使用DFT分析声音，并介绍Freesound.org。生成正弦波并在Python中实现DFT。 3. **傅里叶定理**：线性、平移、对称性、卷积；能量守恒和分贝；相位展开；零填充；快速傅里叶变换和零相位窗口；分析/合成。演示简单周期信号和复杂声音的分析。 4. **短时傅里叶变换**：STFT方程；分析窗口；FFT大小和跳跃大小；时间-频率折中；逆STFT。演示计算声音的谱图工具，以及如何使用它们分析声音。 5. **正弦波模型**：正弦波模型方程；谱中的正弦波；谱峰的正弦波；谱图中的时间变化正弦波；正弦波合成。演示sms-tools包的正弦波模型接口及其在声音分析和合成中的应用。 6. **谐波模型**：谐波模型方程；正弦波、部分和谐波；多声部和单声部信号；谐波检测；时域和频域的基频检测。 7. **正弦波加残差模型**：随机信号；随机模型；声音的随机近似；正弦/谐波加残差模型；残差减法。 8. **声音变换**：使用短时傅里叶变换进行滤波与形态变换；使用正弦波模型进行频率与时间缩放；以及使用谐波加残余模型进行频率变换。 9. **声音和音乐描述**：使用谱分析方法提取音频特征，描述声音、声音集合、音乐录音及音乐集合。声音分类和聚类。 10. **总结主题**：超越本课程的音频信号处理。课程主题回顾，学习更多相关主题的资源介绍。 课程适合希望深入了解音频信号处理在音乐应用中理论与实践的学习者，旨在提供全面的技术理解及相关软件工具的实践经验。
Name:Introduction
Description: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.
Name:Discrete Fourier transform
Description: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.
Name:Fourier theorems
Description: 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.
Name:Short-time Fourier transform
Description: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.
Name:Sinusoidal model
Description:Sinusoidal model equation; sinewaves in a spectrum; sinewaves as spectral peaks; time-varying sinewaves in spectrogram; sinusoidal synthesis. Demonstration of the sinusoidal model interface of the sms-tools package and its use in the analysis and synthesis of sounds. Implementation of the detection of spectral peaks and of the sinusoidal synthesis using Python and presentation of the sineModel functions from the sms-tools package, explaining how to use them.
Name:Harmonic model
Description:Harmonic model equation; sinusoids-partials-harmonics; polyphonic-monophonic signals; harmonic detection; f0-detection in time and frequency domains. Demonstrations of pitch detection algorithm, of the harmonic model interface of the sms-tools package and of its use in the analysis and synthesis of sounds. Implementation of the detection of the fundamental frequency in the frequency domain using the TWM algorithm in Python and presentation of the harmonicModel functions from the sms-tools package, explaining how to use them.
Name:Sinusoidal plus residual model
Description:Stochastic signals; stochastic model; stochastic approximation of sounds; sinusoidal/harmonic plus residual model; residual subtraction; sinusoidal/harmonic plus stochastic model; stochastic model of residual. Demonstrations of the stochastic model, harmonic plus residual, and harmonic plus stochastic interfaces of the sms-tools package and of its use in the analysis and synthesis of sounds. Presentation of the stochasticModel, hprModel and hpsModel functions implemented in the sms-tools package, explaining how to use them.
Name:Sound transformations
Description:Filtering and morphing using the short-time Fourier transform; frequency and time scaling using the sinusoidal model; frequency transformations using the harmonic plus residual model; time scaling and morphing using the harmonic plus stochastic model. Demonstrations of the various transformation interfaces of the sms-tools package and of Audacity. Presentation of the stftTransformations, sineTransformations and hpsTransformations functions implemented in the sms-tools package, explaining how to use them.
Name:Sound and music description
Description:Extraction of audio features using spectral analysis methods; describing sounds, sound collections, music recordings and music collections. Clustering and classification of sounds. Demonstration of various plugins from SonicVisualiser to describe sound and music signals and demonstration of some advance features of freesound.org. Presentation of Essentia, a C++ library for sound and music description, explaining how to use it from Python. Programming with the Freesound API in Python to download sound collections and to study them.
Name:Concluding topics
Description:Audio signal processing beyond this course. Beyond audio signal processing. Review of the course topics. Where to learn more about the topics of this course. Presentation of MTG-UPF. Demonstration of Dunya, a web browser to explore several audio music collections, and of AcousticBrainz, a collaborative initiative to collect and share music data.
Name:Concluding topics: Lesson Choices
Description:
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. The course is based on open software and content. The demonstrations and programming exercises are done using Python under Ubuntu, and the references and materials for the course come from open online repositories. We are also distributing with open licenses the software and materials developed for the course.
