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Learning Introductory Signal Processing Using Multimedia 1 Outline Overview of Information and Communications Some signal processing concepts Tools available for use in the laboratory A selection of exercises Learning Introductory Signal Processing Using Multimedia Roger Browne Institute of Information Sciences and Technology Massey University

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Learning Introductory Signal Processing Using Multimedia 2 Overview of Information and Communications The learning outcomes involve describing: the nature of information how information can be measured a basic communication systems model the nature of noise and its affect on communications A/D and D/A conversion, signals, modulation and Fourier theory some common coding schemes and how they can be used to combat the affect of noise application of discrete mathematics to simple linear systems A second year paper that underpins the BTech (Information Engineering) and BE (Information and Telecommunication Engineering) degrees

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Learning Introductory Signal Processing Using Multimedia 3 Overview of Information and Communications The learning outcomes involve describing: the nature of information how information can be measured a basic communication systems model the nature of noise and its affect on communications A/D and D/A conversion, signals, modulation and Fourier theory some common coding schemes and how they can be used to combat the affect of noise application of discrete mathematics to simple linear systems The relevant sections are:

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Learning Introductory Signal Processing Using Multimedia 4 Some signal processing concepts SourceEncoderTxRxDecoderOutput Channel + Noise Usually electronic noise Standard communications model:

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Learning Introductory Signal Processing Using Multimedia 5 Some signal processing concepts Time domain and frequency domain views of signals Time domain:

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Learning Introductory Signal Processing Using Multimedia 6 Some signal processing concepts Time domain and frequency domain views of signals Frequency domain:

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Learning Introductory Signal Processing Using Multimedia 7 Some signal processing concepts Conversion from analogue to digital involves: sampling aliasing

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Learning Introductory Signal Processing Using Multimedia 8 Tools PC and its multi-media facilities (sound card, microphone, speakers) Signal processing software (Matlab) Very sophisticated personal signal processing facilities: (our ears)

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Learning Introductory Signal Processing Using Multimedia 9 Exercise One: Spectrum Analysis Square wave: This has been studied using Fourier analysis in lectures

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Learning Introductory Signal Processing Using Multimedia 10 Sound of a square wave: Spectrum of a square wave

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Learning Introductory Signal Processing Using Multimedia 11 Spectrum of a square wave A filter selects a range of frequencies. For instance, selecting frequencies between 500Hz and 2000Hz results in a different sound: Filtered signal at original volume: Amplified version of filtered signal:

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Learning Introductory Signal Processing Using Multimedia 12 Viewing this in frequency space:

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Learning Introductory Signal Processing Using Multimedia 13 Exercise Two - Noise in Communications Noise usually refers to electronic noise, but could be acoustic noise. Original signal: SNR = 0dB SNR = -10dB

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Learning Introductory Signal Processing Using Multimedia 14 SNR = -20dB SNR = -30dB The signal frequency is 1300Hz The noise is wide-band or white noise, occupying the full frequency spectrum. Objective: design a filter to retrieve the signal in the case of SNR = -30dB Noise in Communications

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Learning Introductory Signal Processing Using Multimedia 15 Noise in Communications Technique: develop a narrow-band filter centred on 1300Hz. Filter = 1200Hz to 1400Hz: Filter = 1290Hz to 1310Hz: Filter = 1298Hz to 1302Hz:

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Learning Introductory Signal Processing Using Multimedia 16 What about noisy speech signals? Noise in Communications Original: SNR = 0dB: Band-pass filter, 200Hz to 1500Hz: No amount of filtering achieves a clear speech signal

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Learning Introductory Signal Processing Using Multimedia 17 Noise in Communications The way in which the frequencies are spread across the spectrum can be viewed pictorially: Hence a narrow- band filter is not effective.

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Learning Introductory Signal Processing Using Multimedia 18 Exercise Three - Aliasing This is a concept that students often find difficult. It is a characteristic of the process of converting from an analogue signal to a digital signal. A fundamental law in signal processing states that the sampling frequency must be at least twice the highest frequency present in the signal. For example, CDs are sampled at 44.1kHz so the highest frequency that can be present in the signal is 22kHz.

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Learning Introductory Signal Processing Using Multimedia 19 Aliasing If a higher frequency is present it folds over into the lower frequencies. This can be illustrated by taking a simple signal of 1000Hz and steadily reducing the sampling frequency. The minimum sampling frequency for correctly recording the sound is 2000Hz.

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Learning Introductory Signal Processing Using Multimedia 20 Aliasing Original: Sampled at 3000Hz: Sampled at 1800Hz: Sampled at 1500Hz: Sampled at 1200Hz: Students are asked to estimate the frequency in each case. (1000Hz)

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Learning Introductory Signal Processing Using Multimedia 21 Aliasing Their estimates are plotted against sampling frequency: 1000Hz2000Hz Sampling frequency Estimated frequency 1000Hz normal aliased

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Learning Introductory Signal Processing Using Multimedia 22 Aliasing What is the affect on speech? Original: Sampled at 2000Hz: Sampled at 1000Hz: Filtered 0-500Hz: Frequencies greater than 500Hz sound as lower frequencies, creating distortion

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Learning Introductory Signal Processing Using Multimedia 23 The modern multimedia computer offers excellent facilities for basic experiments in signal processing. By making use of: audible signals the sophisticated signal processing of the human ear, a number of basic concepts can be exemplified in a concrete and readily-assimilated form. Conclusions

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© University of Reading 2012 www.reading.ac.uk School of Systems Engineering Matlab Assessment for Final Year Units Victor M. Becerra School of Systems.

© University of Reading 2012 www.reading.ac.uk School of Systems Engineering Matlab Assessment for Final Year Units Victor M. Becerra School of Systems.

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