Digital signal processing

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,^[1]^[2] which is typically generated by the switching of a transistor.^[3]

Digital signal processing and analog signal processing are subfields of signal processing. DSP applications include audio and speech processing, sonar, radar and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, data compression, video coding, audio coding, image compression, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others.

DSP can involve linear or nonlinear operations. Nonlinear signal processing is closely related to nonlinear system identification^[4] and can be implemented in the time, frequency, and spatio-temporal domains.

The application of digital computation to signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.^[5] Digital signal processing is also fundamental to digital technology, such as digital telecommunication and wireless communications.^[6] DSP is applicable to both streaming data and static (stored) data.

Signal sampling

To digitally analyze and manipulate an analog signal, it must be digitized with an analog-to-digital converter (ADC).^[7] Sampling is usually carried out in two stages, discretization and quantization. Discretization means that the signal is divided into equal intervals of time, and each interval is represented by a single measurement of amplitude. Quantization means each amplitude measurement is approximated by a value from a finite set. Rounding real numbers to integers is an example.

The Nyquist–Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency component in the signal. In practice, the sampling frequency is often significantly higher than this.^[8] It is common to use an anti-aliasing filter to limit the signal bandwidth to comply with the sampling theorem, however careful selection of this filter is required because the reconstructed signal will be the filtered signal plus residual aliasing from imperfect stop band rejection instead of the original (unfiltered) signal.

Theoretical DSP analyses and derivations are typically performed on discrete-time signal models with no amplitude inaccuracies (quantization error), created by the abstract process of sampling. Numerical methods require a quantized signal, such as those produced by an ADC. The processed result might be a frequency spectrum or a set of statistics. But often it is another quantized signal that is converted back to analog form by a digital-to-analog converter (DAC).

Domains

DSP engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, and wavelet domains. They choose the domain in which to process a signal by making an informed assumption (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal and the processing to be applied to it. A sequence of samples from a measuring device produces a temporal or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain representation.

Time and space domains

Time domain refers to the analysis of signals with respect to time. Similarly, space domain refers to the analysis of signals with respect to position, e.g., pixel location for the case of image processing.

The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. The surrounding samples may be identified with respect to time or space. The output of a linear digital filter to any given input may be calculated by convolving the input signal with an impulse response.

Frequency domain

Signals are converted from time or space domain to the frequency domain usually through use of the Fourier transform. The Fourier transform converts the time or space information to a magnitude and phase component of each frequency. With some applications, how the phase varies with frequency can be a significant consideration. Where phase is unimportant, often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared.

The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missing. Frequency domain analysis is also called spectrum- or spectral analysis.

Filtering, particularly in non-realtime work can also be achieved in the frequency domain, applying the filter and then converting back to the time domain. This can be an efficient implementation and can give essentially any filter response including excellent approximations to brickwall filters.

There are some commonly used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the harmonic structure of the original spectrum.

Z-plane analysis

Digital filters come in both infinite impulse response (IIR) and finite impulse response (FIR) types. Whereas FIR filters are always stable, IIR filters have feedback loops that may become unstable and oscillate. The Z-transform provides a tool for analyzing stability issues of digital IIR filters. It is analogous to the Laplace transform, which is used to design and analyze analog IIR filters.

Autoregression analysis

A signal is represented as linear combination of its previous samples. Coefficients of the combination are called autoregression coefficients. This method has higher frequency resolution and can process shorter signals compared to the Fourier transform.^[9] Prony's method can be used to estimate phases, amplitudes, initial phases and decays of the components of signal.^[10]^[9] Components are assumed to be complex decaying exponents.^[10]^[9]

Time-frequency analysis

A time-frequency representation of signal can capture both temporal evolution and frequency structure of analyzed signal. Temporal and frequency resolution are limited by the principle of uncertainty and the tradeoff is adjusted by the width of analysis window. Linear techniques such as Short-time Fourier transform, wavelet transform, filter bank,^[11] non-linear (e.g., Wigner–Ville transform^[10]) and autoregressive methods (e.g. segmented Prony method)^[10]^[12]^[13] are used for representation of signal on the time-frequency plane. Non-linear and segmented Prony methods can provide higher resolution, but may produce undesirable artifacts. Time-frequency analysis is usually used for analysis of non-stationary signals. For example, methods of fundamental frequency estimation, such as RAPT and PEFAC^[14] are based on windowed spectral analysis.

Wavelet

In numerical analysis and functional analysis, a discrete wavelet transform is any wavelet transform for which the wavelets are discretely sampled. As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information. The accuracy of the joint time-frequency resolution is limited by the uncertainty principle of time-frequency.

Noise Reduction Techniques in Digital Signal Processing

Noise reduction techniques in Digital Signal Processing (DSP) are essential for improving the quality of signals in various applications, including audio processing, telecommunications, and biomedical engineering. Noise, which is unwanted random variation in signals, can degrade signal clarity and accuracy. DSP offers a range of algorithms to reduce noise while preserving the integrity of the original signal.

1.Spectral Subtraction:

Spectral subtraction is one of the simplest and most widely used noise reduction techniques, especially in speech processing. It works by estimating the power spectrum of the noise during silent periods and subtracting this noise spectrum from the noisy signal. This technique assumes that noise is additive and relatively stationary. While effective, spectral subtraction can introduce "musical noise," a type of artificial noise, if the noise spectrum estimate is inaccurate.

Applications: Primarily used in audio signal processing, including mobile telephony and hearing aids.

Advantages: Simple to implement and computationally efficient.

Limitations: Tends to perform poorly in the presence of non-stationary noise, and can introduce artifacts.

2. Adaptive Filtering:

Adaptive filters are highly effective in situations where noise is unpredictable or non-stationary. In adaptive filtering, the filter's parameters are continuously adjusted to minimize the difference between the desired signal and the actual output. The Least Mean Squares (LMS) and Recursive Least Squares (RLS) algorithms are commonly used for adaptive noise cancellation.

Applications: Used in active noise-canceling headphones, biomedical devices (e.g., EEG and ECG processing), and communications.

Advantages: Can adapt to changing noise environments in real-time.

Limitations: Higher computational requirements, which may be challenging for real-time applications on low-power devices.

3. Wiener Filtering:

Wiener filtering is a statistical approach to noise reduction that minimizes the mean square error between the desired signal and the actual output. This technique relies on knowledge of both the signal and noise power spectra, and it can provide optimal noise reduction if these spectra are accurately estimated.

Applications: Frequently applied in image processing, audio restoration, and radar.

Advantages: Provides optimal noise reduction for stationary noise.

Limitations: Requires accurate estimates of the signal and noise statistics, which may not always be feasible in real-world applications.

4. Kalman Filtering:

Kalman filtering is a recursive algorithm that estimates the state of a dynamic system from a series of noisy measurements. While typically used for tracking and prediction, it is also applicable to noise reduction, especially for signals that can be modeled as time-varying. Kalman filtering is particularly effective in applications where the signal is dynamic and the noise characteristics vary over time.

Applications: Used in speech enhancement, radar, and control systems.

Advantages: Provides excellent performance for time-varying signals with non-stationary noise.

Limitations: Requires a mathematical model of the system dynamics, which may be complex to design for certain applications.

5. Wavelet-Based Denoising:

Wavelet-based denoising (or wavelet thresholding) decomposes the signal into different frequency components using a wavelet transform and then removes the noise by thresholding the wavelet coefficients. This method is effective for signals with sharp transients, like biomedical signals, because wavelet transforms can provide both time and frequency information.

Applications: Commonly used in image processing, ECG and EEG signal denoising, and audio processing.

Advantages: Preserves sharp signal features and offers flexibility in handling non-stationary noise.

Limitations: The choice of wavelet basis and thresholding parameters significantly impacts performance, requiring careful tuning.

6. Non-Local Means (NLM) Denoising:

Non-Local Means is an advanced noise reduction technique that uses redundancy in the signal by averaging similar patches across the signal or image. While computationally more demanding, NLM is highly effective in removing noise from images and audio signals without blurring.

Applications: Applied primarily in image denoising, especially in medical imaging and photography.

Advantages: Preserves details and edges in images.

Limitations: Computationally intensive, often requiring hardware acceleration or approximations for real-time applications.

Empirical mode decomposition

Empirical mode decomposition is based on decomposition signal into intrinsic mode functions (IMFs). IMFs are quasiharmonical oscillations that are extracted from the signal.^[15]

Implementation

DSP algorithms may be run on general-purpose computers^[16] and digital signal processors.^[17] DSP algorithms are also implemented on purpose-built hardware such as application-specific integrated circuit (ASICs).^[18] Additional technologies for digital signal processing include more powerful general purpose microprocessors, graphics processing units, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors.^[19]

For systems that do not have a real-time computing requirement and the signal data (either input or output) exists in data files, processing may be done economically with a general-purpose computer. This is essentially no different from any other data processing, except DSP mathematical techniques (such as the DCT and FFT) are used, and the sampled data is usually assumed to be uniformly sampled in time or space. An example of such an application is processing digital photographs with software such as Photoshop.

When the application requirement is real-time, DSP is often implemented using specialized or dedicated processors or microprocessors, sometimes using multiple processors or multiple processing cores. These may process data using fixed-point arithmetic or floating point. For more demanding applications FPGAs may be used.^[20] For the most demanding applications or high-volume products, ASICs might be designed specifically for the application.

Parallel implementations of DSP algorithms, utilising multi-core CPU and many-core GPU architectures, are developed to improve the performances in terms of latency of these algorithms.^[21]

Native processing is done by the computer's CPU rather than by DSP or outboard processing, which is done by additional third-party DSP chips located on extension cards or external hardware boxes or racks. Many digital audio workstations such as Logic Pro, Cubase, Digital Performer and Pro Tools LE use native processing. Others, such as Pro Tools HD, Universal Audio's UAD-1 and TC Electronic's Powercore use DSP processing.

Applications

General application areas for DSP include

For high-performance DSP applications, C and C++ are often used, especially when low-level control over hardware is required. Libraries such as Intel’s IPP (Integrated Performance Primitives) and ARM’s CMSIS-DSP provide optimized functions for signal processing.

Key Features

Performance: Offers high performance for real-time applications due to low-level programming.
Flexibility: Allows for custom implementation of algorithms tailored to specific applications.
Access to Hardware: Direct access to hardware resources, which is crucial for embedded systems.

Applications

C/C++ is used in applications requiring real-time processing, such as telecommunications, embedded systems, and video processing.

Conclusion

Digital signal processing is a versatile field supported by a wide array of software tools. From high-level environments like MATLAB and Python to low-level programming with C/C++, these tools cater to various needs, whether for research, education, or industry applications. As DSP continues to evolve, these software tools play a critical role in advancing the capabilities and efficiencies of signal processing technologies.

References

^ B. SOMANATHAN NAIR (2002). Digital electronics and logic design. PHI Learning Pvt. Ltd. p. 289. ISBN 9788120319561. Digital signals are fixed-width pulses, which occupy only one of two levels of amplitude.
^ Joseph Migga Kizza (2005). Computer Network Security. Springer Science & Business Media. ISBN 9780387204734.
^ 2000 Solved Problems in Digital Electronics. Tata McGraw-Hill Education. 2005. p. 151. ISBN 978-0-07-058831-8.
^ Billings, Stephen A. (Sep 2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains. UK: Wiley. ISBN 978-1-119-94359-4.
^ Broesch, James D.; Stranneby, Dag; Walker, William (2008-10-20). Digital Signal Processing: Instant access (1 ed.). Butterworth-Heinemann-Newnes. p. 3. ISBN 9780750689762.
^ Srivastava, Viranjay M.; Singh, Ghanshyam (2013). MOSFET Technologies for Double-Pole Four-Throw Radio-Frequency Switch. Springer Science & Business Media. p. 1. ISBN 9783319011653.
^ Walden, R. H. (1999). "Analog-to-digital converter survey and analysis". IEEE Journal on Selected Areas in Communications. 17 (4): 539–550. doi:10.1109/49.761034.
^ Candes, E. J.; Wakin, M. B. (2008). "An Introduction To Compressive Sampling". IEEE Signal Processing Magazine. 25 (2): 21–30. Bibcode:2008ISPM...25...21C. doi:10.1109/MSP.2007.914731. S2CID 1704522.
^ ^a ^b ^c Marple, S. Lawrence (1987-01-01). Digital Spectral Analysis: With Applications. Englewood Cliffs, N.J: Prentice Hall. ISBN 978-0-13-214149-9.
^ ^a ^b ^c ^d Ribeiro, M.P.; Ewins, D.J.; Robb, D.A. (2003-05-01). "Non-stationary analysis and noise filtering using a technique extended from the original Prony method". Mechanical Systems and Signal Processing. 17 (3): 533–549. Bibcode:2003MSSP...17..533R. doi:10.1006/mssp.2001.1399. ISSN 0888-3270. Retrieved 2019-02-17.
^ So, Stephen; Paliwal, Kuldip K. (2005). "Improved noise-robustness in distributed speech recognition via perceptually-weighted vector quantisation of filterbank energies". Ninth European Conference on Speech Communication and Technology.
^ Mitrofanov, Georgy; Priimenko, Viatcheslav (2015-06-01). "Prony Filtering of Seismic Data". Acta Geophysica. 63 (3): 652–678. Bibcode:2015AcGeo..63..652M. doi:10.1515/acgeo-2015-0012. ISSN 1895-6572. S2CID 130300729.
^ Mitrofanov, Georgy; Smolin, S. N.; Orlov, Yu. A.; Bespechnyy, V. N. (2020). "Prony decomposition and filtering". Geology and Mineral Resources of Siberia (2): 55–67. doi:10.20403/2078-0575-2020-2-55-67. ISSN 2078-0575. S2CID 226638723. Retrieved 2020-09-08.
^ Gonzalez, Sira; Brookes, Mike (February 2014). "PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise". IEEE/ACM Transactions on Audio, Speech, and Language Processing. 22 (2): 518–530. doi:10.1109/TASLP.2013.2295918. ISSN 2329-9290. S2CID 13161793. Retrieved 2017-12-03.
^ Huang, N. E.; Shen, Z.; Long, S. R.; Wu, M. C.; Shih, H. H.; Zheng, Q.; Yen, N.-C.; Tung, C. C.; Liu, H. H. (1998-03-08). "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 454 (1971): 903–995. Bibcode:1998RSPSA.454..903H. doi:10.1098/rspa.1998.0193. ISSN 1364-5021. S2CID 1262186. Retrieved 2018-06-05.
^ Weipeng, Jiang; Zhiqiang, He; Ran, Duan; Xinglin, Wang (August 2012). "Major optimization methods for TD-LTE signal processing based on general purpose processor". 7th International Conference on Communications and Networking in China. pp. 797–801. doi:10.1109/ChinaCom.2012.6417593. ISBN 978-1-4673-2699-5. S2CID 17594911.
^ Zaynidinov, Hakimjon; Ibragimov, Sanjarbek; Tojiboyev, Gayrat; Nurmurodov, Javohir (2021-06-22). "Efficiency of Parallelization of Haar Fast Transform Algorithm in Dual-Core Digital Signal Processors". 2021 8th International Conference on Computer and Communication Engineering (ICCCE). IEEE. pp. 7–12. doi:10.1109/ICCCE50029.2021.9467190. ISBN 978-1-7281-1065-3. S2CID 236187914.
^ Lyakhov, P.A. (June 2023). "Area-Efficient digital filtering based on truncated multiply-accumulate units in residue number system 2 n - 1 , 2 n , 2 n + 1". Journal of King Saud University - Computer and Information Sciences. 35 (6): 101574. doi:10.1016/j.jksuci.2023.101574.
^ Stranneby, Dag; Walker, William (2004). Digital Signal Processing and Applications (2nd ed.). Elsevier. ISBN 0-7506-6344-8.
^ JPFix (2006). "FPGA-Based Image Processing Accelerator". Retrieved 2008-05-10.
^ Kapinchev, Konstantin; Bradu, Adrian; Podoleanu, Adrian (December 2019). "Parallel Approaches to Digital Signal Processing Algorithms with Applications in Medical Imaging". 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS) (PDF). pp. 1–7. doi:10.1109/ICSPCS47537.2019.9008720. ISBN 978-1-7281-2194-9. S2CID 211686462.
^ Rabiner, Lawrence R.; Gold, Bernard (1975). Theory and application of digital signal processing. Englewood Cliffs, NJ: Prentice-Hall, Inc. ISBN 978-0139141010.
^ Kerckhoff, Jessica; Listenberger, Jennifer; Valente, Michael (October 1, 2008). "Advances in hearing aid technology". Contemporary Issues in Communication Science and Disorders. 35: 102–112. doi:10.1044/cicsd_35_F_102.

[1] B. SOMANATHAN NAIR (2002). Digital electronics and logic design. PHI Learning Pvt. Ltd. p. 289. ISBN 9788120319561. Digital signals are fixed-width pulses, which occupy only one of two levels of amplitude.

[2] Joseph Migga Kizza (2005). Computer Network Security. Springer Science & Business Media. ISBN 9780387204734.

[3] 2000 Solved Problems in Digital Electronics. Tata McGraw-Hill Education. 2005. p. 151. ISBN 978-0-07-058831-8.

[4] Billings, Stephen A. (Sep 2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains. UK: Wiley. ISBN 978-1-119-94359-4.

[5] Broesch, James D.; Stranneby, Dag; Walker, William (2008-10-20). Digital Signal Processing: Instant access (1 ed.). Butterworth-Heinemann-Newnes. p. 3. ISBN 9780750689762.

[Srivastava-6] Srivastava, Viranjay M.; Singh, Ghanshyam (2013). MOSFET Technologies for Double-Pole Four-Throw Radio-Frequency Switch. Springer Science & Business Media. p. 1. ISBN 9783319011653.

[7] Walden, R. H. (1999). "Analog-to-digital converter survey and analysis". IEEE Journal on Selected Areas in Communications. 17 (4): 539–550. doi:10.1109/49.761034.

[8] Candes, E. J.; Wakin, M. B. (2008). "An Introduction To Compressive Sampling". IEEE Signal Processing Magazine. 25 (2): 21–30. Bibcode:2008ISPM...25...21C. doi:10.1109/MSP.2007.914731. S2CID 1704522.

[Marple-9] Marple, S. Lawrence (1987-01-01). Digital Spectral Analysis: With Applications. Englewood Cliffs, N.J: Prentice Hall. ISBN 978-0-13-214149-9.

[Ribeiro-10] Ribeiro, M.P.; Ewins, D.J.; Robb, D.A. (2003-05-01). "Non-stationary analysis and noise filtering using a technique extended from the original Prony method". Mechanical Systems and Signal Processing. 17 (3): 533–549. Bibcode:2003MSSP...17..533R. doi:10.1006/mssp.2001.1399. ISSN 0888-3270. Retrieved 2019-02-17.

[11] So, Stephen; Paliwal, Kuldip K. (2005). "Improved noise-robustness in distributed speech recognition via perceptually-weighted vector quantisation of filterbank energies". Ninth European Conference on Speech Communication and Technology.

[12] Mitrofanov, Georgy; Priimenko, Viatcheslav (2015-06-01). "Prony Filtering of Seismic Data". Acta Geophysica. 63 (3): 652–678. Bibcode:2015AcGeo..63..652M. doi:10.1515/acgeo-2015-0012. ISSN 1895-6572. S2CID 130300729.

[13] Mitrofanov, Georgy; Smolin, S. N.; Orlov, Yu. A.; Bespechnyy, V. N. (2020). "Prony decomposition and filtering". Geology and Mineral Resources of Siberia (2): 55–67. doi:10.20403/2078-0575-2020-2-55-67. ISSN 2078-0575. S2CID 226638723. Retrieved 2020-09-08.

[14] Gonzalez, Sira; Brookes, Mike (February 2014). "PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise". IEEE/ACM Transactions on Audio, Speech, and Language Processing. 22 (2): 518–530. doi:10.1109/TASLP.2013.2295918. ISSN 2329-9290. S2CID 13161793. Retrieved 2017-12-03.

[15] Huang, N. E.; Shen, Z.; Long, S. R.; Wu, M. C.; Shih, H. H.; Zheng, Q.; Yen, N.-C.; Tung, C. C.; Liu, H. H. (1998-03-08). "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis". Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences. 454 (1971): 903–995. Bibcode:1998RSPSA.454..903H. doi:10.1098/rspa.1998.0193. ISSN 1364-5021. S2CID 1262186. Retrieved 2018-06-05.

[16] Weipeng, Jiang; Zhiqiang, He; Ran, Duan; Xinglin, Wang (August 2012). "Major optimization methods for TD-LTE signal processing based on general purpose processor". 7th International Conference on Communications and Networking in China. pp. 797–801. doi:10.1109/ChinaCom.2012.6417593. ISBN 978-1-4673-2699-5. S2CID 17594911.

[17] Zaynidinov, Hakimjon; Ibragimov, Sanjarbek; Tojiboyev, Gayrat; Nurmurodov, Javohir (2021-06-22). "Efficiency of Parallelization of Haar Fast Transform Algorithm in Dual-Core Digital Signal Processors". 2021 8th International Conference on Computer and Communication Engineering (ICCCE). IEEE. pp. 7–12. doi:10.1109/ICCCE50029.2021.9467190. ISBN 978-1-7281-1065-3. S2CID 236187914.

[18] Lyakhov, P.A. (June 2023). "Area-Efficient digital filtering based on truncated multiply-accumulate units in residue number system 2 n - 1 , 2 n , 2 n + 1". Journal of King Saud University - Computer and Information Sciences. 35 (6): 101574. doi:10.1016/j.jksuci.2023.101574.

[19] Stranneby, Dag; Walker, William (2004). Digital Signal Processing and Applications (2nd ed.). Elsevier. ISBN 0-7506-6344-8.

[20] JPFix (2006). "FPGA-Based Image Processing Accelerator". Retrieved 2008-05-10.

[:0-21] Kapinchev, Konstantin; Bradu, Adrian; Podoleanu, Adrian (December 2019). "Parallel Approaches to Digital Signal Processing Algorithms with Applications in Medical Imaging". 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS) (PDF). pp. 1–7. doi:10.1109/ICSPCS47537.2019.9008720. ISBN 978-1-7281-2194-9. S2CID 211686462.

[22] Rabiner, Lawrence R.; Gold, Bernard (1975). Theory and application of digital signal processing. Englewood Cliffs, NJ: Prentice-Hall, Inc. ISBN 978-0139141010.

[23] Kerckhoff, Jessica; Listenberger, Jennifer; Valente, Michael (October 1, 2008). "Advances in hearing aid technology". Contemporary Issues in Communication Science and Disorders. 35: 102–112. doi:10.1044/cicsd_35_F_102.

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v t e Digital electronics
Components	Transistor Resistor Inductor Capacitor Printed electronics Printed circuit board Electronic circuit Flip-flop Memory cell Combinational logic Sequential logic Logic gate Boolean circuit Integrated circuit (IC) Hybrid integrated circuit (HIC) Mixed-signal integrated circuit Three-dimensional integrated circuit (3D IC) Emitter-coupled logic (ECL) Erasable programmable logic device (EPLD) Macrocell array Programmable logic array (PLA) Programmable logic device (PLD) Programmable Array Logic (PAL) Generic Array Logic (GAL) Complex programmable logic device (CPLD) Field-programmable gate array (FPGA) Field-programmable object array (FPOA) Application-specific integrated circuit (ASIC) Tensor Processing Unit (TPU)
Theory	Digital signal Boolean algebra Logic synthesis Logic in computer science Computer architecture Digital signal Digital signal processing Circuit minimization Switching circuit theory Gate equivalent
Design	Logic synthesis Place and route Placement Routing Transaction-level modeling Register-transfer level Hardware description language High-level synthesis Formal equivalence checking Synchronous logic Asynchronous logic Finite-state machine Hierarchical state machine
Applications	Computer hardware Hardware acceleration Digital audio radio Digital photography Digital telephone Digital video cinematography television Electronic literature
Design issues	Metastability Runt pulse

v t e Digital signal processing
Theory	Detection theory Discrete signal Estimation theory Nyquist–Shannon sampling theorem
Sub-fields	Audio signal processing Digital image processing Speech processing Statistical signal processing
Techniques	Z-transform Advanced z-transform Matched Z-transform method Bilinear transform Constant-Q transform Discrete cosine transform (DCT) Discrete Fourier transform (DFT) Discrete-time Fourier transform (DTFT) Impulse invariance Integral transform Laplace transform Post's inversion formula Starred transform Zak transform
Sampling	Aliasing Anti-aliasing filter Downsampling Nyquist rate / frequency Oversampling Quantization Sampling rate Undersampling Upsampling

Signal sampling

Domains

Time and space domains

Frequency domain

Z-plane analysis

Autoregression analysis

Time-frequency analysis

Wavelet

Empirical mode decomposition

Implementation

Applications

Techniques

Related fields

Software Tools for Digital Signal Processing

1. MATLAB

Overview

Key Features

Applications

2. Python (with NumPy and SciPy)

Overview

Key Features

Applications

3. LabVIEW

Overview

Key Features

Applications

4. GNU Radio

Overview

Key Features

Applications

5. Octave

Overview

Key Features

Applications

6. C/C++ with DSP Libraries

Overview

Key Features

Applications

Conclusion

Further reading

References