10 DSP Interview Questions To Prepare (With Sample Answers)

Updated 4 March 2023

Digital signal processing (DSP) is a branch of electrical engineering focusing on the analysis and manipulation of digital signals. As DSP is useful in many areas, such as audio and video processing, radar, sonar and telecommunications, employers expect individuals working in DSP jobs to have good technical knowledge. If you wish to pursue a career in DSP, knowing how to answer commonly asked questions can help you appear confident and increase your chances of receiving a job offer. In this article, we list 10 DSP interview questions with their sample answers to help you prepare for an interview.

10 DSP Interview Questions With Sample Answers

Here are 10 DSP interview questions with example answers to help you prepare for your interview:

1. What is sectional convolution?

Sectional convolution is a common concept in the digital processing system. The interviewer might ask this question to evaluate your understanding of DSP and whether you have the necessary skills to complete your job duties. When answering, give a detailed explanation and definition of sectional convolution, including its major uses.

Example: Sectional convolution in DSP divides a data sequence into smaller sections. DSP professionals can independently process and control these smaller sections to obtain an output. This division of data sequences into more manageable parts provides greater control and ensures the computer processes the output as you expect. Typically, sectional convolution occurs with lengthy data sequences because these make the output sequence challenging to achieve due to limited computer memory.

2. What are DIT algorithms?

When working in any electrical engineering role, you are likely to use several DSP algorithms. By asking this question, the interviewer wants to understand your knowledge of algorithms. When answering, clearly explain this algorithm and outline its importance.

Example: DIT refers to decimation in time, which is one way of implementing the Fast Fourier Transform. It helps reduce the total number of computations used by the Discrete Fourier Transform or DFT. The DIT algorithm calculates the n value in a data sequence by dividing it into two parts. This halves the n values and makes it easier to combine values. Typically, this algorithm helps calculate the n values for very large data sequences.

3. Outline the difference between a digital signal microprocessor and a microprocessor

While many non-technical people might confuse these two devices, employers expect individuals working in a DSP role to know the difference between them. Your answer shows the interviewer whether you understand the devices you might use in your job.

Example: A microprocessor incorporates a computer central processing unit function on single or multiple integrated circuits. Its main purpose is accepting digital data as inputs, processing it according to instructions and providing the output. Typically, microprocessors are not application-specific and help in processing control-oriented tasks. Most general microprocessors are present in personal computers. The DSP microprocessor supports high-performance and intensive tasks and is ideal for performing calculations on converted digitised signals from the analogue domain. DSP microprocessors are less powerful than general microprocessors.

4. What is Aliasing?

Aliasing is a common issue in DSP and has several different meanings. This is one of the important DSP interview questions which an interviewers might ask to understand whether you are familiar with this concept. When answering, explain the different meanings of aliasing and how to prevent this effect from occurring.

Example: Aliasing is a major concern in DSP because it causes misinterpretation of digital signals. This occurs when it is impossible to distinguish one signal from another. When you sample a digital signal, it converts into discrete values. If the sampling rate is not high, some higher frequency components of the signal might get lost, which causes misinterpretation. In my previous job, I used specific filters, including optical fibres, to remove high frequencies and prevent aliasing.

5. How do IIR filters differ from FIR filters?

Based on the impulse response, there are two main types of filters. An interviewer might ask this question if your job requires you to use these filters frequently. In your answer, define the filters and outline some key differences between the two to show your knowledge.

Example: Infinite impulse response (IIR) filters are ideal for applications where the linear phase is less important and the computer has limited memory. IIRs are helpful in audio equalisation, smart sensors and high-speed telecommunication applications. Alternatively, finite impulse response (FIR) filters have a finite impulse response, which settles to zero in finite time. They are ideal for applications where the linear phase is important and the computer has a reasonable amount of memory. Unlike IIR, which becomes unstable, FIR never becomes unstable irrespective of the input signal.

6. How do sampling and quantisation relate to each other?

Sampling and quantisation are present in nearly all DSPs. When working in a DSP role, you might map input values from a larger set to output values in a smaller set. To perform such job duties, knowledge of sampling and quantisation is useful. When answering this question, define each term and explain how they relate.

Example: Sampling refers to converting a continuous signal into a discrete signal, whereas quantisation transforms analogue signals with a continuous set of values to a digital signal with a discrete set. These two relate to each other because quantisation helps reduce the number of bits representing a signal.

7. What are the different system classifications?

Knowing about different systems is necessary to progress your career in a DSP role. Interviewers may ask this question to test your knowledge of systems and decide whether you are suitable for the job. When answering, list the different system classifications and briefly explain them to show your expertise and knowledge.

Example: Digital processing professionals might classify systems in different ways. Despite this, some system classifications include casual and anti-casual, static and dynamic, time-variant and time-invariant, linear and non-linear and stable and unstable systems. Casual systems are those in which the output depends upon previous and current inputs instead of future ones, whereas anti-casual systems depend completely on future inputs. Static systems do not change, whereas dynamic systems continuously change.

Time-variant systems are those where time changes impact the output, while in time-invariant systems, time changes do not impact the output. In addition, linear systems follow the superposition and homogeneity principles, unlike non-linear systems. Finally, stable systems have stable state variables and unstable systems have unstable state variables.

8. How can you reduce noise while transmitting data over long distances?

Interviewers ask this question to learn if you know how to reduce potential noise during long-distance data transmission. Your job might involve transmitting data from one location to another. This makes your ability to reduce or eliminate noises important for your success in this role.

Example: One way to reduce noise during data transmission is with error-correcting codes. These codes detect and correct errors occurring during transmission. Another way to reduce noise is with signal conditioning techniques. This primarily involves using optical filters to remove unwanted noises during the transmission.

9. What is meant by modulation and demodulation?

Modulation and demodulation occur during data transmission, making them essential concepts in signal processing. Interviewers ask this question to evaluate your technical skills. When answering, define both terms and outline some differences between the two.

Example: In modulation, you encode information in a transmitted signal, while in demodulation, you extract information from the transmitted signal. Modulation occurs at the transmitter side, while demodulation occurs at the communication system's receiver side. During modulation, a low-frequency signal converts to a high-frequency signal, while in demodulation, a high-frequency signal converts to a low-frequency signal. In addition, demodulation is a complex process compared to modulation.

10. Outline the difference between DFT and DTFT

Both DFT and DTFT are common but important concepts in DSP. Interviewers ask this question to assess your understanding of concepts you are likely to use in your job role. When answering this question, convince the interviewer how competent you are by briefly describing the two terms and their common differences.

Example: DFT stands for the discrete fourier transform, while DTFT stands for discrete-time fourier transform and enables users to find the spectrum of a finite-duration signal. DFT performs general time domain signal processing and classic frequency domain processing. Typically, human speech and hearing use signals with this type of encoding. Alternatively, DTFT operates on periodic and discrete signals. The input in DFT is occasional, while it is not the same in DTFT. While DFT is physically realisable, DTFT is mathematically precise. In addition, the frequency is continuous in DTFT, while it is discrete in DFT.