The DTMF coder and decoder are basis of encoding standard
function. In the process two audible sinusoidal signals were considered for the
frequencies (Materias. fi. uba. ar,
2018).
The high and low components of frequency are identified for the discrete time
signals. The objective of the present work is to determine the code sequence of
4 digit DTMF as embedded in the .wav file (Materias. fi. uba. ar,
2018).
MATLAB software is used exclusively to work on the
individual project. The relation between time domain and frequency domain is
provided by the unaltered signal. The initial process was design of filter bank
by using the Butterworth filters. The filters works for the detection of DTMF signals.
The design mechanism was implemented in the MATLAB and detected the DTMF tones
as embedded in the signal (Materias. fi. uba. ar,
2018).
The report provides analysis for the type of function,
order, and Wn of the transfer function. The transfer function is designed by
using the design command. The frequency response of the filter is provided for
the signal to the filter bank. The visual comparison is considered for the
pre-filter and post-filter signals. The low and high frequency pairs are
identified for the discrete time signal. In the process the dual tone, symbols
and digits are expressed on the form of discrete time signal. The sampling
frequency was selected at 8 kHz and dual encoded samples were corresponding to
the DTMF signals (Materias. fi. uba. ar,
2018).
Implementation of the DTMF encoder
The MATLAB function dtmf.m is used for the implementation of
DTMF encoder. The implementation of the digital oscillator is related to the
generation of sinusoidal tones at different frequencies
and the
response of the input signal is provided by the causal filter (Materias.
fi. uba. ar, 2018).
Input signal of DTMF transfer function
Causal filter of DTMF transfer function
The impulse response of the signal is provided by the
digital oscillator and the system oscillates for the sinusoidal plot at H [n].
Implementation of the DTMF decoder
The input of the decoder is mainly a vector that contains
the DTMF tones and it is encoded by using the encoder. The FIR (Finite Impulse
Response) is the band pass filter that can be implemented for the centered
frequencies and to decode the keys (Materias. fi. uba. ar,
2018).
The decoding process is based on the iterative process and initiates from the
row 1 and ends on the row 4. The iteration of the FIR band pass filter is
associated with the Fh and the signal strength must be approximately equal to
the threshold (Edgefxkits. com, 2018). The mean amplitude
for the output of the filter is adjusted according to the threshold
frequency. The iteration of FIR bandpass
is centralized for the sinusoidal calculation.
The cancellation is prevented by the mean values and squared value for
the signal. After the detection of row
and column pairs the digits are encoded.
The approach is uniquely identified for the encoded signals and effect
of noise on the output graph.
Implementation of DTMF decoder have significant advantages for the
bandpass filter coefficients. The
coefficients are individually manipulated to produce the narrower filters. The general filters can enhance the presence
of noise through the detection process (Materias. fi. uba. ar,
2018).
Figure 1: Signal obtained by MATLAB
In order to decode all the digits, the encoder is added to
the signal to improve the dual tone. The step assumes for the dual tone and
signals can be calculated. The decoding
step loops are built on the length of signal and dual tone length (Edgefxkits.
com, 2018). The loops of decoding steps computer quoted
according to the length of signal. The
signals are divided into different steps for the dual tone length and sinus
length. The decoding part is based on
the iteration process and waveform for each iteration (Engr. mun. ca, 2018).
Analysis Process
The analysis process for the detection of the digits was
comprised of FI at 770 and Fh ant the 1209. The filter coefficients and the
frequency responses were related to the FIR filter and the center frequency
band pass (Materias. fi. uba. ar,
2018).
The coefficient of frequency are measured by the band pass FIR filter
frequency. The implementation for the band pass filter is based on the
iteration process and the band pass filter diagram of decoded digits in the 4
bits are provided below. The modified algorithm was used to generate the DTMF
signals (Materias. fi. uba. ar,
2018).
The noise in the system performance was neglected for the band pass filter
process. The decoded measures in the DTMF filter was related to the length of
the filters and can be manipulated in the noisy environments (Materias. fi. uba. ar,
2018).
The higher values for the detections of the lower amplitude increased the error
detection process. The filter approach was reduced for the band detection as
related to the mean square error for the band detection. The system was
designed and tested for the frequency domain and specifications for the noise
free environments (Materias. fi. uba. ar,
2018).
Conclusion of DTMF transfer function
In the present work MATLAB software was used to measure the
relation between time domain and frequency domain for the provided unaltered
signal. The initial process was to define the design of filter bank and it was
carried out by using the Butterworth filters. The prime objective of the
filters was to detect the DTMF signals. The mechanism used for design was
implemented in the software and later detected by the DTMF tones as embedded in
the signal.
References of DTMF transfer function
Edgefxkits.
com. (2018). DTMF Decoder Application Circuit and Working Procedure. Retrieved
from www.edgefxkits.com:
https://www.edgefxkits.com/blog/dtmf-decoder-application-circuits/
Engr. mun. ca. (2018). DTMF Coder / Decoder Design using FIR
Banks. Retrieved from www.engr.mun.ca:
http://www.engr.mun.ca/~sircar/project1_files/dtmf.pdf
Materias. fi. uba. ar. (2018). Dual-Tone Multi-Frequency
coding. Retrieved from materias.fi.uba.ar:
http://materias.fi.uba.ar/6609/docs/Chapter_14.pdf