Digital signal processing refers to the use of digital processing machines or processors to perform an array of operations. It is quite a technical field that requires brains, expertise, and a great deal of knowledge.
If you are interested in a digital processing job, you should find out how their interviews are conducted. Strive to know some of the areas likely to be assessed by the interviewer and then go ahead and brainstorm the best responses.
In this article, we will look at 25 common interview questions that you should expect. We hope that you will be better prepared and more confident at the end. Take a look at the following:
8 Tips to Prepare for a Digital Signal Processing Interview
Here are eight key areas to focus on when preparing for a Digital Signal Processing (DSP) interview:
Digital Signal Processing Interview Preparation
|Mathematical Foundations||Understanding of signal processing mathematics. Brush up on concepts such as Fourier transforms, Laplace transforms, convolution, and basic linear algebra.||Practice solving problems related to signal processing mathematics. Familiarize yourself with common transforms and their properties.|
|Digital Signal Processing Basics||Fundamental concepts and principles of DSP. Understand concepts like sampling, quantization, digital filters, and signal representation in the time and frequency domains.||Be prepared to explain the basics of how signals are processed in the digital domain. Know the advantages and limitations of digital signal processing compared to analog processing.|
|Filter Design||Designing digital filters. Know the types of filters (e.g., FIR, IIR), filter specifications, and the design methods (e.g., windowing, frequency sampling, Butterworth, Chebyshev).||Practice designing filters to meet specific frequency response requirements. Understand the trade-offs between different filter types.|
|DSP Hardware and Software||Knowledge of hardware and software used in DSP. Understand DSP processors, programming languages (e.g., C, assembly), and development tools commonly used in DSP applications.||Be familiar with the architecture and features of popular DSP processors. Practice coding DSP algorithms in a programming language of your choice.|
|Digital Signal Processors (DSP) Architectures||Understanding DSP hardware architectures. Know the architecture of common DSP processors, such as the structure of instruction sets, memory organization, and parallel processing capabilities.||Study the architecture of popular DSP chips like those from Texas Instruments or Analog Devices. Be prepared to discuss how specific features of these architectures can be leveraged in DSP applications.|
|Signal Processing Software Tools||Proficiency with software tools used in DSP. Be familiar with software tools like MATLAB, Simulink, or other DSP-specific development environments.||Practice implementing and simulating DSP algorithms using these tools. Demonstrate your ability to analyze and interpret results.|
|Digital Modulation and Demodulation||Understanding digital communication principles. Know the basics of digital modulation techniques (e.g., BPSK, QPSK, FSK) and demodulation methods.||Be ready to discuss how DSP is applied in digital communication systems. Understand the impact of noise and channel impairments on digital signals.|
|Real-Time Signal Processing||Knowledge of real-time signal processing. Understand the challenges and considerations when implementing signal processing algorithms in real-time systems.||Be able to discuss the importance of real-time processing in various applications. Practice optimizing algorithms for real-time performance.|
Remember to combine theoretical knowledge with practical application through hands-on exercises and projects. Additionally, stay updated on recent developments and trends in the field of DSP.
Top 25 Digital Signal Processing Interview Questions & Answers
Here are the top 25 Digital Signal Processing interview questions, along with a sample answer for each question.
1. Can You Define a Discrete-Time Signal and a Discrete-Time System?
There are a few terms and concepts akin to digital signal processing that you need to know. Make sure that you answer this question clearly and precisely. Also, your answer should capture both sides of the question.
A discrete-time signal is the function of an independent variable, normally an integer. It is normally expressed as x(n). On the other hand, a discrete-time system is an algorithm that performs a prescribed operation on a discrete-time signal.
2. Mention The Classifications of Discrete-Time Signals?
This is a follow-up question. Such questions are normally asked to shed light on a former response or as a buildup of a concept you mentioned in a previous response. All in all, make sure that you get it right lest you come off as incompetent.
There are three main types of discrete-time signals. These are energy and power signals, periodic and aperiodic signals, and symmetric and antisymmetric signals. The symmetric signals are also known as even signals and the asymmetric, odd signals. (You can offer more information on these signals. However, make sure that you do not veer off the main points)
3. Do You Know the Classifications of Systems? Kindly Enlighten Us
Given that this is a highly technical field, most of the questions will take a similar approach. The interviewer is testing your knowledge on a given area in digital signal processing. List all the signals confidently and convince the interviewer that you are highly knowledgeable.
There are different ways of classifying systems that give rise to different system classifications. The most common are static and dynamic systems, time-invariant and variant systems, causal and anti causal systems, linear and nonlinear systems, and stable and unstable systems. ( Another good approach would be shedding light on these classification means before mentioning the different types of systems that exist)
4. What Do You Understand by Sectional Convolution?
This is a common concept in digital signal processing that you should expect in your upcoming interview. You must have probably done it at one point and therefore understand what it entails. Ensure that you illustrate your answer to the interviewer to enhance understanding.
Sectional convolution is dividing a data sequence into smaller sections that are then independently processed and later controlled to get an output. This normally happens when the data sequence is of a longer duration making the output sequence quite difficult to obtain due to limited computer memory.
5. What Do You Understand BY FFT and Why Is It Necessary?
This is yet another specific concept in digital signal processing that you may be asked about in your interview. The best way to go about it is to first define FFT before explaining why it is necessary. Your answer should be as comprehensive as possible.
FFT stands for fast Fourier transform, which is an algorithm that is used in DFT computation. It uses symmetry and periodicity properties of the twiddle factor, which greatly minimizes the time taken to compute DFT. Therefore, using the FFT algorithm helps reduce the number of complex computations, which explains why it is a popular alternative.
6. You Definitely Know What DIT Algorithm Is. Could You Please Tell Us About It?
There are several algorithms involved in digital signal processing that you should be aware of. The best approach to such a question is to show the interviewer that you are highly knowledgeable in the test area. Also, answer such questions confidently.
Known as the decimation-in-time algorithm, it calculates the discrete Fourier transform of an N point sequence. It breaks this sequence into two and then combines them to give the DFT of the original sequence. In DIT, the sequence x(n) is usually split into two smaller subsequences.
7. Is It Possible to Design Digital Filters from Analog Filters? If yes, how?
The interviewer wants to know how good you are at designing filters. Such questions test your competence and should therefore be answered with utmost care. Be comprehensive in your answer.
Yes. There are three main ways of doing this. One, you can map the desired digital filter specifications into those of an equal analog filter. Two, one can derive the analog transfer function for the analog prototype and, lastly, transform the analog prototype’s transfer function into an equally superior digital filter transfer function.
8. What Is Bilinear Transformation? Could You Please Explain Some of Its Properties?
You can easily establish that the vast majority of our questions are technical. The interviewer wants to know if you understand certain specifics about digital signal processing and would therefore make a good addition to the firm. To answer this question correctly, please offer a concise explanation of bilinear transformation before mentioning the properties.
Bilinear transformation is a mapping that prevents aliasing of frequency components by transforming the left half of the S plane into the unit circle of the Z-plane once. It is a one-to-one mapping that occurs for very point Z with only one corresponding S. The half of the S-plane also maps onto the exterior of the unit circle.
9. Mention The Advantages and Disadvantages of Bilinear Transformation
This is a follow-up question. The interviewer used the previous one as a foundation. Follow-up questions generally shed more light on the former question, especially if the reply was short. This should not be hard to answer, especially if your former response was great.
Bilinear mapping offers a range of advantages. It provides a one-to-one mapping and ensures that there are zero aliasings. It also allows stable continuous systems to be mapped into realizable and stable digital systems.
Some of the disadvantages are that it causes compression at high frequencies and does not preserve the impulse and phase response of the analog filter in the digital filter obtained from the transformation.
10. What Do You Understand by The Transition Theorem?
Your knowledge of digital signal processing should expose you to several theories in this field, such as the transposition theorem. Your answer should be comprehensive but clear. Also, mention some of the operations that define the transpose of a structure.
Transposition theorem dictates that reversing the directions of all branch transmittance and interchanging both the input and output in the flow graph does not affect the functioning of a system. Some of the operations that define the transpose of s structure include reversing the directions of the branches in the signal flow graph and the roles of the flow graph nodes, interchanging the outputs and inputs, summing and points becoming branching points, and vice versa.
11. What Do You Understand by Aliasing In DSP? How Can One Avoid It?
Aliasing is a common term that we may have mentioned in our article. The interviewer is testing your understanding of different concepts, which will dictate whether you will be a good fit in the organization. First, start by explaining what aliasing means before you mention how one can avoid it.
Aliasing is an effect that arises when we cannot distinguish one signal from the other. It can also mean the distortion in the reconstructed signal after reconstruction from the original continuous signal. The best way to avoid this effect is to use an anti-aliasing filter such as optical anti-aliasing fiber which filters out the high-frequency components.
12. What Is the Main Difference Between a Microprocessor and a Digital Signal Microprocessor?
Most people normally confuse these two devices under the belief that they are similar or slightly similar. However, as an expert in this field, you should clearly know how to differentiate the two. The interviewer wants to see if you understand the devices that you will be interacting with. Therefore, make sure that your answer is as clear as possible.
The difference between these two lines is in the tasks they are both made to support. Digital signal processors normally support high performance and intensive tasks, which can also be repetitive, whereas microprocessors are meant for control-oriented tasks. They are therefore less powerful than their counterparts.
13. Kindly Differentiate Between DFT and DTFT
DFT stands for the discrete Fourier Transform, whereas DFTF is the acronym for discrete-time Fourier transform. These are common concepts in digital signal processing that we must have mentioned at one point in our article. This is a technical question, and therefore, convince the interviewer how good you are by pitting these two against each other.
DFT has a limited number of samples of periodic signals, whereas DTFT has an unlimited number of samples. The input in DFT is occasional, which may not be the case for DTFT. The former is physically realizable, whereas the latter is mathematically precise. Lastly, the frequency becomes discrete in DFT, whereas it is continuous in DTFT.
14. Tell Us the Difference and Similarities Between DIF and DIT Algorithms
There are several algorithms in digital signal processing that you should always have at the back of your mind. Your answer will help the interviewer ascertain what you know about this field and whether you are qualified enough for the role at hand. Ensure that your answer captures both the difference and similarities.
For DIT, the input is slightly revered, whereas the output is in a natural order. For DIF, the output is somewhat reversed and the input in its natural order. These two also have slightly different butterflies. In DIF, complex multiplication only occurs after the add-subtract operation.
As for the similarities, both of these need equal operations to compute DFT. They can both be done in place and can also perform bit reversal at a given place during the computation.
15. You Definitely Know IIR and FIR Filters Owing to Your Years of Experience in This Field. Could You Please Differentiate Between the Two?
There are two main types of filters based on impulse response- the IIR and FIR filters. Show the interviewer that you have a vast knowledge of them and can, therefore, perfectly execute what is required in the new role.
FIR filters can be designed to have a linear phase which lacks in IIR filters. The former can also be realized both ways- recursively and non-recursively, whereas the latter is only realized recursively. FIR filters are more flexible, whereas the flexibility of IRR filters is limited to just a few kinds of filters. Lastly, you will experience lesser round-off noise with IIR filters as opposed to FIR filters. This is because feedback is not used in the former.
16. Define Both Input and Product Quantization Error
Digital signal processing is a highly technical field that explains the myriad of technical questions that you are likely to be asked by the interviewer. There are lots of concepts, functions, and even now errors that you need to know. Treat this question like you would treat any other technical question. Be as comprehensive as possible while going straight to the point.
Input quantization is a result of the truncation or rounding of the filter coefficients to b bits. This is normal in digital computation, where the filter coefficients are usually represented in binary and stored in registers. In theory, the filter coefficients are generally computed to infinite precision.
On the other hand, the product quantization error occurs at the multiplier output. Whenever a b bit data is multiplied with a b bit coefficient, the result is 2b bits. The multiplier output will either be rounded or truncated to produce an error.
17. Could You Please Mention Some of The Characteristics That One Should Pay Close Attention to When Designing the Window Function?
Like we mentioned, there are different functions that you need to know when it comes to digital signal processing. The interviewer will therefore ask such questions to find out just how knowledgeable you are on these functions. The best approach is to offer the right information without being too wordy.
There are three main desirable characteristics of the window. These are normally based on the lobe. First, it should have a small highest sidelobe level of the frequency response. Second, the central lob should be narrow and have the highest energy. Lastly, the side lobes should rapidly decrease in energy.
18. Mention The Different Types of Filters Based on Impulse Response
Filters are normally based on either impulse or frequency response. Do not, therefore, confuse these two. Kindly stick to those based on impulse response and answer the question confidently. You can also expound a little on them.
There are two filters based on impulse response, i.e., IIR and FIR filters. IIR filters are normally recursive. Their present output sample relies on the current input, output, and past inputs samples. On the other hand, FIR filters are non-recursive, meaning that their present output samples are dependent on the present input and previous input samples.
19. Differentiate Between the TTL and CMOS Chips
Whenever you are faced with such a question, the best approach is to pit the subjects against one another. However, first, start by explaining what both are before delving into the comparisons.
The TTL chip is fully read as transistor-transistor logic. Each logic gate is usually designed using two bipolar junction transistors. On the other hand, the CMOS chip refers to the Complementary Metal Oxide Semiconductor Chip, an integrated chip whose transistors are designed by field-effect transistors.
TTL chips are normally used in computers, whereas CMOS are mostly used in mobile phones. The former also has several parts, such as resistors, whereas the latter is mostly logic gates. TTL chips also use lots of power as compared to their counterparts. Lastly, TTL chips use BJTs, whereas the CMOS chips utilize FETs.
20. In Your Understanding and Experience, What Are Some of The Factors That Affect Threshold Voltage
Do not be confused by the framing of the question. This is a technical question and should be treated as one.
The threshold voltage is largely affected by the voltage connected to the body terminal. Temperature can also influence it. A rise in temperature causes a decrease in the threshold voltage.
21. What Is The Difference Between A Digital Signal Processor And A Microprocessor?
A digital signal processor (DSP) is a specialized microprocessor that is designed to perform mathematical operations on digital signals. In contrast, a microprocessor is a general-purpose processor that is designed to perform a wide range of tasks.
22. What Are The Different Types Of Digital Filters?
There are two main types of digital filters: Finite Impulse Response (FIR) filters and Infinite Impulse Response (IIR) filters.
23. What Is The Difference Between A Finite Impulse Response (FIR) Filter And An Infinite Impulse Response (IIR) Filter?
The main difference between FIR and IIR filters is that FIR filters provide an impulse response of a finite period, while IIR filters generate impulse responses of infinite duration for a dynamic system.
24. What Is The Nyquist Sampling Theorem?
The Nyquist sampling theorem is a fundamental principle in digital signal processing that links the frequency range of a signal and the sample rate required to avoid aliasing distortion. The theorem states that the sample rate must be at least twice the bandwidth of the signal to avoid aliasing distortion.
25. What Is The Difference Between Linear Convolution And Circular Convolution Of Two Sequences?
Linear convolution is a mathematical operation done to calculate the output of any Linear-Time Invariant (LTI) system given its input and impulse response. Circular convolution is essentially the same process as linear convolution. Just like linear convolution, it involves the operation of folding a sequence, shifting it, multiplying it with another sequence, and summing the resulting products. However, in circular convolution, the signals are all periodic. Thus the shifting can be thought of as actually being a rotation. Since the values keep repeating because of the periodicity, it is known as circular convolution.
These are some of the questions that you should expect in your upcoming interview. Remember to also polish your listening and speaking skills before stepping into the interview room.