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Audio signal processing is a subfield of signal processing that is concerned with the electronic manipulation of audio signals. 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 or sound power level is typically measured in decibels. As audio signals may be represented in either digital or analog format, processing may occur in either domain. Analog processors operate directly on the electrical signal, while digital processors operate mathematically on its digital representation.
Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
Digital signal processing (DSP) is the use of digital processing, such as by computers or more specialized digital signal processors, to perform a wide variety of signal processing operations. The digital signals processed in this manner are a sequence of numbers that represent samples of a continuous variable in a domain such as time, space, or frequency. In digital electronics, a digital signal is represented as a pulse train, which is typically generated by the switching of a transistor.
Speech coding is an application of data compression to digital audio signals containing speech. Speech coding uses speech-specific parameter estimation using audio signal processing techniques to model the speech signal, combined with generic data compression algorithms to represent the resulting modeled parameters in a compact bitstream.
Signal processing is an electrical engineering subfield that focuses on analyzing, modifying and synthesizing signals, such as sound, images, potential fields, seismic signals, altimetry processing, and scientific measurements. Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, subjective video quality, and to also detect or pinpoint components of interest in a measured signal.
A vocoder is a category of speech coding that analyzes and synthesizes the human voice signal for audio data compression, multiplexing, voice encryption or voice transformation.
Linear predictive coding (LPC) is a method used mostly in audio signal processing and speech processing for representing the spectral envelope of a digital signal of speech in compressed form, using the information of a linear predictive model.
A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images, digital video, digital audio, digital television, digital radio, and speech coding. DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.
In sound processing, the mel-frequency cepstrum (MFC) is a representation of the short-term power spectrum of a sound, based on a linear cosine transform of a log power spectrum on a nonlinear mel scale of frequency.
Noise reduction is the process of removing noise from a signal. Noise reduction techniques exist for audio and images. Noise reduction algorithms may distort the signal to some degree. Noise rejection is the ability of a circuit to isolate an undesired signal component from the desired signal component, as with common-mode rejection ratio.
Lawrence R. Rabiner is an electrical engineer working in the fields of digital signal processing and speech processing; in particular in digital signal processing for automatic speech recognition. He has worked on systems for AT&T Corporation for speech recognition.
Voice activity detection (VAD), also known as speech activity detection or speech detection, is the detection of the presence or absence of human speech, used in speech processing. The main uses of VAD are in speaker diarization, speech coding and speech recognition. It can facilitate speech processing, and can also be used to deactivate some processes during non-speech section of an audio session: it can avoid unnecessary coding/transmission of silence packets in Voice over Internet Protocol (VoIP) applications, saving on computation and on network bandwidth.
Demosaicing, also known as color reconstruction, is a digital image processing algorithm used to reconstruct a full color image from the incomplete color samples output from an image sensor overlaid with a color filter array (CFA) such as a Bayer filter. It is also known as CFA interpolation or debayering.
Thomas Shi-Tao Huang was a Chinese-born American computer scientist, electrical engineer, and writer. He was a researcher and professor emeritus at the University of Illinois at Urbana-Champaign (UIUC). Huang was one of the leading figures in computer vision, pattern recognition and human computer interaction.
Adaptive feedback cancellation is a common method of cancelling audio feedback in a variety of electro-acoustic systems such as digital hearing aids. The time varying acoustic feedback leakage paths can only be eliminated with adaptive feedback cancellation. When an electro-acoustic system with an adaptive feedback canceller is presented with a correlated input signal, a recurrent distortion artifact, entrainment is generated. There is a difference between the system identification and feedback cancellation.
Bandwidth extension of signal is defined as the deliberate process of expanding the frequency range (bandwidth) of a signal in which it contains an appreciable and useful content, and/or the frequency range in which its effects are such. Its significant advancement in recent years has led to the technology being adopted commercially in several areas including psychacoustic bass enhancement of small loudspeakers and the high frequency enhancement of coded speech and audio.
The Hilbert–Huang transform (HHT) is a way to decompose a signal into so-called intrinsic mode functions (IMF) along with a trend, and obtain instantaneous frequency data. It is designed to work well for data that is nonstationary and nonlinear. In contrast to other common transforms like the Fourier transform, the HHT is an algorithm that can be applied to a data set, rather than a theoretical tool.
Adaptive differential pulse-code modulation (ADPCM) is a variant of differential pulse-code modulation (DPCM) that varies the size of the quantization step, to allow further reduction of the required data bandwidth for a given signal-to-noise ratio.
Diamond Cut Audio Restoration Tools is a set of digital audio editor tools from Diamond Cut Productions used for audio restoration, record restoration, sound restoration of gramophone records and other audio containing media.
Audio forensics is the field of forensic science relating to the acquisition, analysis, and evaluation of sound recordings that may ultimately be presented as admissible evidence in a court of law or some other official venue.
References
- ↑ J. Benesty, S. Makino, J. Chen (ed). Speech Enhancement. pp.1-8. Springer, 2005. ISBN 978-3-540-24039-6.
- ↑ Sahidullah, Md; Patino, Jose; Cornell, Samuele; Yin, Ruiking; Sivasankaran, Sunit; Bredin, Herve; Korshunov, Pavel; Brutti, Alessio; Serizel, Romain; Vincent, Emmanuel; Evans, Nicholas; Marcel, Sebastien; Squartini, Stefano; Barras, Claude (2019-11-06). "The Speed Submission to DIHARD II: Contributions & Lessons Learned". arXiv: 1911.02388 [eess.AS].
- ↑ J. Benesty, M. M. Sondhi, Y. Huang (ed). Springer Handbook of Speech Processing. pp.843-869. Springer, 2007. ISBN 978-3-540-49125-5.
- J. Benesty, M. M. Sondhi, Y. Huang (ed). Springer Handbook of Speech Processing. Springer, 2007. ISBN 978-3-540-49125-5.
- J. Benesty, S. Makino, J. Chen (ed). Speech Enhancement. Springer, 2005. ISBN 978-3-540-24039-6.
- P. C. Loizou. Speech Enhancement: Theory and Practice. CRC Press, 2013. ISBN 978-1-466-50421-9.
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