Demonstrations of the various transformation interfaces of the sms-tools package and of Audacity. 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. In this course you will learn about audio signal processing methodologies that are specific for music and of use in real applications. About this course: In this course you will learn about audio signal processing methodologies that are specific for music and of use in real applications. Audio signal processing is a subfield of signal processing that is concerned with the electronic manipulation of audio signals. The demonstrations and programming exercises are done using Python under Ubuntu, and the references and materials for the course come from open online repositories. Presentation of Essentia, a C++ library for sound and music description, explaining how to use it from Python. Week 1: Introduction; basic mathematics Week 2: Discrete Fourier transform Week 3: Fourier transform properties Week 4: Short-time Fourier transform Week 5: Sinusoidal model Week 6: Harmonic model Week 7: Sinusoidal plus residual modeling Week 8: Sound transformations Week 9: Sound/music description Week 10:Concludin… Accountable Talk®: Conversation that Works. In this course you will learn about audio signal processing methodologies that are specific for music and of use in real applications. Review of the course topics. All the labs of the course are done using Python and all the materials and code used in the class are available under open licenses (Creative Commons and GPL). Audacity is an easy-to-use, multi-track audio editor and recorder for Windows, Mac OS X, GNU/Linux and other operating systems. Presentation of MTG-UPF. Spectral Audio Signal Processing is the fourth book in the music signal processing series by Julius O. Smith. Fourier transform properties: Linearity; Shift; Evenness; Convolution; Phase unwrapping; Zero padding; Power & amplitude in dB; Fast Fourier Transform (FFT); FFT and zero-phase. Topics include: • Phasors and tuning forks • The wave equation • Sampling and quantizing • Feedforward and feedback filters • Comb and string filters • Periodic sounds • Transform methods Demonstration of tools to compute the spectrogram of a sound and on how to analyze a sound using them. Thanks guys, The ASP course is great and everything is well explained. In order to use these tools you have to install python (recommended 3.7.x) and the following modules: ipython, numpy, matplotlib, scipy, and cython. 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. Introduction to Python and to the sms-tools package, the main programming tool for the course. The core of essentia.js is powered by Essentia C++ library back-end using WebAssembly along with a high-level Typescript API and add-on utility modules. Harmonic model: number of harmonic components: instantaneous amplitude: instantaneous frequency (Hz) It is quite similar to sinusoidal model. Audio signal processing beyond this course. Sound transformations: Filtering; Morphing; Frequency scaling and pitch transposition; Time scaling. An informal and easy-to-understand introduction to digital signal processing, this treatment emphasizes digital audio and applications to computer music. Short-Time Fourier Transform: STFT equation; Window type; Window size; FFT size; Hop size; Time-frequency compromise; Inverse STFT; STFT implementation. Amazing support community. STFT equation; analysis window; FFT size and hop size; time-frequency compromise; inverse STFT. The author clearly explains the analysis of audio signals and the extraction of metadata describing the content of the signal, covering both abstract … Sound/music description: Extraction of audio features; Describing sounds, sound collections, music recordings and music collections; Clustering and classification of sounds. A JavaScript library for music/audio signal analysis and processing for both real-time and offline use-cases. Sinusoidal plus residual modeling: Sinusoidal plus residual model; Sinusoidal subtraction; Stochastic model; Sinusoidal plus stochastic model. Sinusoidal model equation; sinewaves in a spectrum; sinewaves as spectral peaks; time-varying sinewaves in spectrogram; sinusoidal synthesis. This creates additional challenges in sound-source localization, signal enhancement and recognition. Programming with the Freesound API in Python to download sound collections and to study them. 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audio signal processing for music applications

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