OBJECTIVES

1. To test students’ ability to analyze and design system (PO3).

2. To instill appreciation of lifelong learning among students.

3. To test students’ ability to select and use appropriate technique and resources

(PO6).

4. To test students’ skills and techniques in engineering practice (PO14).

SIGNAL ANALYSIS

1. Radin.wav (152980 Radin Yusof B Radin Sadi)

2. Hafis.wav (154594 Mohamad Hafis Fauzan B Mohamad Hanif Loo)

1) Generating and Analyzing the Signal

The sound file Radin.wav and Hafis.wav were played using music player in order to check

whether the files are corrupted or not. Then, the file is imported into MATLAB in order to

analyse the sound for detection of higher frequency, which is more than 500Hz. Here is

command for analysing the sound.

Figure 1: The MATLAB command for analyzing the signal of Radin.wav and Hafis.wav.

Figure 2: The signal of Radin.wav and Hafis.wav that been generated.

As in Figure 2, it shows the Radin and Hafis recorded voice (Radin.wav and

Hafis.wav) in pronouncing the sentence of ‘Department of Electrical and Electronic

Engineering Universiti Putra Malaysia’. 44.1 kHz is the frequency sampling. By studying the

observation from the graph, the signal that been generated have a little bit of noise and it

frequency content has slightly bigger amplitude.

2) Cross Correlation Analysis between Radin and Hafis Voice Signal

Figure 3: The MATLAB command to analyze the correlation between Radin.wav and

Hafis.wav.

Figure 4: The cross correlation signal that been generated for both Radin.wav and Hafis.wav.

Figure 4 shows the cross correlation between two signals A (Radin.wav) and B (Hafis.wav). Cross correlation is a process to compare the similarity of two waveforms as a function of a time lag applied to one of them. It is also known as a sliding inner product or sliding dot product. It has the similarity to the convolution of two functions, where two signals A and B are having similarity the combined together for searching a long signal for short period of time. The amplitude was maximized for cross correlation between signal A and B from range of -1 to 1 into range of -50 to 100 as the signal a and B match.

2) Filtering the Signal and FFT Analysis

Figure 5: The MATLAB command to filtering and FFT analysis for both signal of Radin.wav

and Hafis.wav.

Figure 6: The original signal, filtered signal and FFT analysis for both signal (Radin.wav and

Hafis.wav) that been generated.

Figure 6 shows the comparison between two signals of recorded voice (Radin.wav and

Hafis.wav). The original signal (red signal) that been generated for Radin.wav has less noise

than the signal generated for Hafis.wav. This may occur because of the different of

frequency content of two different students in saying the sentence ‘Department of Electrical

and Electronic Engineering Universiti Putra Malaysia’. The Hafis.wav may got a higher

frequency than Radin.wav.

An 8th order low pass Butterworth filter with cutoff frequency, fc = 500 Hz was

designed to reduce the noise. The sampling frequency is set at 44.1 kHz. The low pass

Butterworth filter is known to have a maximally flat for frequencies in its pass band, meaning

its variation with frequency in the pass band is monotonic and approaches a zero derivative

as the frequency approaches zero. As the filter been implemented, it will block the passage

of some higher frequency energy, while allowing energy at below 500 Hz frequencies to

pass. The resultant filtered signal was shown in Figure 6 where the pattern of the both

waveform can be seen cut-off below 500 Hz. Both Radin.wav and Hafis.wav filtered signal

(yellow signal) seen to reduce the noise.

To get the data efficiently, so less step is required to get the same numerical result.,

Fast Fourier transform (FFT) method was used to analyse both of Radin.wav and Hafis.wav

which contains long data sets where N in the 800 hundreds. The computation time can be

reduced by several orders of magnitude, and the improvement is roughly proportional to

N/log(N) which made the calculation of the Discrete Fourier transform practical. In Figure 6,

it shows the frequency spectrum for both Radin.wav and Hafis.wav that been generated by

using FFT analysis. It can be seen the frequency spectrum of Radin.wav has lower in range

of amplitude from 0 to 1500 V compared to Hafis.wav which slightly higher in range of

amplitude from 0 to 2000 V.

4) Spectrogram Analysis of the Signal

Figure 7: The MATLAB command for spectrogram analysis of both Radin.wav and

Hafis.wav.

Figure 8: The spectrogram that been generated for signal Radin.wav and Hafis.wav.

A spectrogram is a visual representation of the spectrum of frequencies in a sound or

other signal as they vary with time or some other variable. Spectrograms are sometimes

called spectral waterfalls, voiceprints, or voicegrams. Spectrograms can be used to identify

spoken words phonetically, and to analyse the various calls of animals. They are used

extensively in the development of the fields of music, sonar, radar, and speech processing,

seismology. The instrument that generates a spectrogram is called a spectrograph. The

sample outputs on the right show a select block of frequencies going up the vertical axis,

and time on the horizontal axis.

By using this spectrogram, the Radin.wav and Hafis.wav can be analysed as shown in

Figure 8. All the characteristic of the sound can be display that contains overlap, blackman

and rectwin. The blue color spectral indicates low energy portion of the spectrum, with red

color spectral indicating the most energetic portions. By doing some observation towards

spectrogram, the blue color spectral that been generated in spectrogram for both signal has

slightly similar low energy portion. The different between his two signals are in their red color

spectral which indicates the most energetic portions. It can be seen that Hafis.wav has the

most energetic portions compared to Radin.wav.

CONCLUSION

From the assignment, certain of criteria had been observed. It can be done by

analysing own voice with colleague voice so the comparison between different in frequency

content of two signals as we can see in correlation of signals. The characteristic of signal like

energy can be display which contains certain information as we can see in the spectrogram.

DEPARTMENT OF ELECTRICAL AND ELECTRONICS ENGINEERING

EEE 3501

SIGNAL PROCESSING

SEM II 2013/2014

COURSE : BACHELOR OF ELECTRICAL AND ELECTRONIC ENGINEERING

NAME (MATRIC NO.)

: RADIN YUSOF BIN RADIN SADI (152980)

LECTURER’S NAME : IR. DR. RAJA MOHD. KAMIL BIN RAJA AHMAD

DUE DATE : 9TH MAY 2014

ASSIGNMENT 1

FREQUENCY ANALYSIS OF SIGNAL

U N I V E R S I T I P U T R A M A L A Y S I AFAKULTI KEJURUTERAAN

Faculty of Engineering