Audio Signal Processing for Music Applications

Start Date: 09/15/2019

Course Type: Common Course

Course Link:

Explore 1600+ online courses from top universities. Join Coursera today to learn data science, programming, business strategy, and more.

Course Syllabus

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.

Deep Learning Specialization on Coursera

Course Introduction

In this course you will learn about audio signal processing methodologies that are specific for musi

Course Tag

Digital Signal Processing Signal Processing Python Programming Fft Algorithms

Related Wiki Topic

Article Example
Audio signal processing Audio signal processing or audio processing is the intentional alteration of audio signals often through an audio effect or effects unit. As audio signals may be electronically represented in either digital or analog format, signal processing may occur in either domain. Analog processors operate directly on the electrical signal, while digital processors operate mathematically on the digital representation of that signal.
Digital signal processing Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech signal processing, sonar, radar and other sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control of systems, biomedical engineering, seismic data processing, among others.
Digital signal processing The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, digital synthesizers, radar, sonar, financial signal processing, seismology and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room correction of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, medical imaging such as CAT scans and MRI, MP3 compression, computer graphics, image manipulation, hi-fi loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers.
Audio signal processing A digital representation expresses the pressure wave-form as a sequence of symbols, usually binary numbers. This permits signal processing using digital circuits such as microprocessors and computers. Although such a conversion can be prone to loss, most modern audio systems use this approach as the techniques of digital signal processing are much more powerful and efficient than analog domain signal processing.
Audio signal processing Traditionally the most important audio processing (in audio broadcasting) takes place just before the transmitter. Studio audio processing is limited in the modern era due to digital audio systems (mixers, routers) being pervasive in the studio.
Financial signal processing For a long time, financial signal processing technologies have been used by different hedge funds, such as Jim Simon's Renaissance Technologies. However, hedge funds usually do not reveal their trade secrets. Some early research results in this area are summarized by R.H. Tütüncü and M. Koenig and by T.M. Cover, J.A. Thomas. A.N. Akansu and M.U. Torun published the book in financial signal processing entitled "A Primer for Financial Engineering: Financial Signal Processing and Electronic Trading." An edited volume on the subject with the title "Financial Signal Processing and Machine Learning" was also published. There were two special issues of IEEE Journal of Selected Topics in Signal Processing published on Signal Processing Methods in Finance and Electronic Trading in 2012, and on Financial Signal Processing and Machine Learning for Electronic Trading in 2016 in addition to the special section on Signal Processing for Financial Applications in IEEE Signal Processing Magazine appeared in 2011.
Signal processing The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration.
Headroom (audio signal processing) In digital and analog audio, headroom refers to the amount by which the signal-handling capabilities of an audio system exceed a designated nominal level. Headroom can be thought of as a safety zone allowing transient audio peaks to exceed the nominal level without damaging the system or the audio signal, e.g., via clipping. Standards bodies differ in their recommendations for nominal level and headroom.
IEEE Signal Processing Society The Signal Processing society was formed in 1948 as the Professional Group on Audio of the Institute of Radio Engineers.
Signal processing Signal processing is an enabling technology that encompasses the fundamental theory, applications, algorithms, and implementations of processing or transferring information contained in many different physical, symbolic, or abstract formats broadly designated as "signals". It uses mathematical, statistical, computational, heuristic, and linguistic representations, formalisms, and techniques for representation, modelling, analysis, synthesis, discovery, recovery, sensing, acquisition, extraction, learning, security, or forensics.
Audio signal processing Audio signals are electronic representations of sound waves—longitudinal waves which travel through air, consisting of compressions and rarefactions. The energy contained in audio signals is typically measured in decibels. Audio processing was necessary for early radio broadcasting, as there were many problems with studio to transmitter links.
CLAM (audio software) CLAM (C++ Library for Audio and Music) is an open-source framework for research and application development in the audio and music domain. It is based on the concept of data-processing modules linked into a network. Modules can perform complex audio signal analysis, transformations and synthesis. CLAM also provides a uniform interface to common tasks within audio applications, such as accessing audio devices and audio files. CLAM serves as a library for C++ application development, but a graphical interface also allows full applications to be built without coding. It won the 2006 ACM Multimedia Open Source Competition.
Digital signal processing DSP algorithms have long been run on general-purpose computers and digital signal processors. DSP algorithms are also implemented on purpose-built hardware such as application-specific integrated circuit (ASICs). Additional technologies for digital signal processing include more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors.
Spectrum Signal Processing by Vecima Spectrum Signal Processing by Vecima is a technology company and commercial off-the-shelf (COTS) product provider, based in Vancouver, British Columbia. Spectrum designs and builds board and system-level hardware and software solutions for signal processing applications. Since May 2, 2007, Spectrum has operated as a division of Vecima Networks Inc., a Canadian technology company. In addition, Spectrum Signal Processing (USA), Inc. is a subsidiary of Vecima Networks Inc., and is primarily a sales organization for Spectrum’s products in the United States.
Audio signal As much of the older analog audio equipment has been emulated in digital form, usually through the development of audio plug-ins for digital audio workstation (DAW) software, the path of digital information through the DAW (i.e. from an audio track through a plug-in and out a hardware output) is also called an "audio signal" or "signal flow".
Audio signal processing Historically, before the advent of widespread digital technology, ASP was the only method by which to manipulate a signal. Since that time, as computers and software became more advanced, digital signal processing has become the method of choice.
Noise (signal processing) In signal processing, noise is a general term for unwanted (and, in general, unknown) modifications that a signal may suffer during capture, storage, transmission, processing, or conversion.
Array processing Array processing technique represents a breakthrough in signal processing. Many applications and problems which are solvable using array processing techniques are introduced. In addition to these applications within the next few years the number of applications that include a form of array signal processing will increase. It is highly expected that the importance of array processing will grow as the automation becomes more common in industrial environment and applications, further advances in digital signal processing and digital signal processing systems will also support the high computation requirements demanded by some of the estimation techniques.
Headroom (audio signal processing) In digital audio, headroom is defined as the amount by which digital full scale (FS) exceeds the nominal level in decibels (dB). The European Broadcasting Union (EBU) specifies several nominal levels and resulting headroom for different applications.
Statistical signal processing Statistical signal processing is an area of Applied Mathematics and Signal Processing that treats signals as stochastic processes, dealing with their statistical properties (e.g., mean, covariance, etc.). Because of its very broad range of application Statistical signal processing is taught at the graduate level in either Electrical Engineering, Applied Mathematics, Pure Mathematics/Statistics, or even Biomedical Engineering and Physics departments around the world, although important applications exist in almost all scientific fields.