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Audio signal processing is a subfield ofsignal processing that is concerned with the electronic manipulation ofaudio signals. Audio signals are electronic representations ofsound waves—longitudinal waves which travel through air, consisting of compressions and rarefactions. The energy contained in audio signals orsound power level is typically measured indecibels. As audio signals may be represented in eitherdigital oranalog format, processing may occur in either domain. Analog processors operate directly on the electrical signal, while digital processors operate mathematically on its digital representation.
The motivation for audio signal processing began at the beginning of the 20th century with inventions like thetelephone,phonograph, andradio that allowed for the transmission and storage of audio signals. Audio processing was necessary for earlyradio broadcasting, as there were many problems withstudio-to-transmitter links.[1] The theory of signal processing and its application to audio was largely developed atBell Labs in the mid 20th century.Claude Shannon andHarry Nyquist's early work oncommunication theory,sampling theory andpulse-code modulation (PCM) laid the foundations for the field. In 1957,Max Mathews became the first person tosynthesize audio from acomputer, giving birth tocomputer music.
Major developments indigitalaudio coding andaudio data compression includedifferential pulse-code modulation (DPCM) byC. Chapin Cutler at Bell Labs in 1950,[2]linear predictive coding (LPC) byFumitada Itakura (Nagoya University) and Shuzo Saito (Nippon Telegraph and Telephone) in 1966,[3]adaptive DPCM (ADPCM) by P. Cummiskey,Nikil S. Jayant andJames L. Flanagan at Bell Labs in 1973,[4][5]discrete cosine transform (DCT) coding byNasir Ahmed, T. Natarajan andK. R. Rao in 1974,[6] andmodified discrete cosine transform (MDCT) coding by J. P. Princen, A. W. Johnson and A. B. Bradley at theUniversity of Surrey in 1987.[7] LPC is the basis forperceptual audio coding and is widely used inspeech coding,[8] while MDCT coding is widely used in modernaudio coding formats such asMP3[9] andAdvanced Audio Coding (AAC).[10]
An analog audio signal is a continuous signal represented by an electrical voltage or current that isanalogous to the sound waves in the air. Analog signal processing then involves physically altering the continuous signal by changing the voltage or current or charge viaelectrical circuits.
Historically, before the advent of widespreaddigital technology, analog was the only method by which to manipulate a signal. Since that time, as computers and software have become more capable and affordable, digital signal processing has become the method of choice. However, in music applications, analog technology is often still desirable as it often producesnonlinear responses that are difficult to replicate with digital filters.
A digital representation expresses the audio waveform as a sequence of symbols, usuallybinary numbers. This permits signal processing usingdigital circuits such asdigital signal processors,microprocessors and general-purpose computers. Most modern audio systems use a digital approach as the techniques of digital signal processing are much more powerful and efficient than analog domain signal processing.[11]
Processing methods and application areas includestorage,data compression,music information retrieval,speech processing,localization,acoustic detection,transmission,noise cancellation,acoustic fingerprinting,sound recognition,synthesis, and enhancement (e.g.equalization,filtering,level compression,echo andreverb removal or addition, etc.).
Audio signal processing is used when broadcasting audio signals in order to enhance their fidelity or optimize for bandwidth or latency. In this domain, the most important audio processing takes place just before the transmitter. The audio processor here must prevent or minimizeovermodulation, compensate for non-linear transmitters (a potential issue withmedium wave andshortwave broadcasting), and adjust overallloudness to the desired level.
Active noise control is a technique designed to reduce unwanted sound. By creating a signal that is identical to the unwanted noise but with the opposite polarity, the two signals cancel out due todestructive interference.
Audio synthesis is the electronic generation of audio signals. A musical instrument that accomplishes this is called a synthesizer. Synthesizers can eitherimitate sounds or generate new ones. Audio synthesis is also used to generate humanspeech usingspeech synthesis.
Audio effects alter the sound of amusical instrument or other audio source. Common effects includedistortion, often used with electric guitar inelectric blues androck music;dynamic effects such asvolume pedals andcompressors, which affect loudness;filters such aswah-wah pedals andgraphic equalizers, which modify frequency ranges;modulation effects, such aschorus,flangers andphasers;pitch effects such aspitch shifters; and time effects, such asreverb anddelay, which create echoing sounds and emulate the sound of different spaces.
Musicians,audio engineers and record producers use effects units during live performances or in therecording studio, typically with electric guitar, bass guitar,electronic keyboard orelectric piano. While effects are most frequently used withelectric orelectronic instruments, they can be used with any audio source, such asacoustic instruments, drums, and vocals.[12][13]
Computer audition (CA) or machine listening is the general field of study ofalgorithms and systems for audio interpretation by machines.[14][15] Since the notion of what it means for a machine to "hear" is very broad and somewhat vague, computer audition attempts to bring together several disciplines that originally dealt with specific problems or had a concrete application in mind. The engineerParis Smaragdis, interviewed inTechnology Review, talks about these systems — "software that uses sound to locate people moving through rooms, monitor machinery for impending breakdowns, or activate traffic cameras to record accidents."[16]
Inspired by models ofhuman audition, CA deals with questions of representation,transduction, grouping, use of musical knowledge and general soundsemantics for the purpose of performing intelligent operations on audio and music signals by the computer. Technically, this requires a combination of methods from the fields ofsignal processing,auditory modelling, music perception andcognition,pattern recognition, andmachine learning, as well as more traditional methods ofartificial intelligence for musical knowledge representation.[17][18]
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