**1. Introduction**

It is important to consider that, after vision, the ear is the most important sensory organ of the human body. The ear is an alarm sensor; that is, it is always active to perceive sounds. The sound consists in the change of air pressure that produces waves with a certain periodic frequency. However, within these there are also sound signals that are inarticulate to the human ear and also unpleasant, even damaging human health. Any sound pressure in the air is measured in decibels. A decibel is a logarithmic value (not linear, but exponential) that represents the relationship between a measured value and a reference value. In any environment there are audible signals that we need to perceive (talks, alarms, etc.) and other unwanted signals that cause the so-called noise pollution. The said contamination is defined as the presence of noise caused by an acoustic emitter that implies discomfort or harm to the health of people.

Some of the noises to which the human auditory system is exposed and that are part of the noise pollution are the following:


Noise pollution significantly interferes with interpersonal communication and increases workplace accidents and traffic accidents. **Table 1** shows the noise levels and their effects on the human ear.

A filter may consist of hardware or software applied to a set of noisy or contaminated data in order to extract information of interest. Noise can be generated by any source. In this chapter, a digital solution is proposed that allows the attenuation of noise attached to audible signals that are perceived by the human ear. An adaptive filter is a device that is useful for processing an input signal by blocking and/or allowing some parts of it (unwanted noise or vibration).

On the other hand, the adaptive filter [1] has a feedback loop that has the ability to minimize the error produced by the comparison between the output signal (after processing) and a reference signal (expected signal). The above allows to guarantee that the human ear is not contaminated with the unwanted signals and only to rescue the information that needs to be received. **Figure 1** shows the structure of an adaptive filter.

The operation of the algorithm that will represent the filter [2] is as follows:

1.The input signal will be picked up (in the scheme it is labeled as x(n)). This signal is the one that is in the environment and that contains what you want to

2.There is a reference signal labeled with d(n). This signal is what is expected to be perceived by the human ear, for example, the conversation you have with

3.The signal x(n) is processed in order to attenuate unwanted signals (noises

4.The output signal y(n) and the desired d(y) are compared in order to obtain an

5.Based on the error generated, the process will be carried out again to ensure that the signal obtained after the filter is as close as possible to the reference signal.

In general, the filter [3] will be used to inhibit signals that are not desired (technically this process is known as attenuating the amplitude of the noise signal) to "clean" the signal that must be perceived by the human ear. The proposed technique adapts to the conditions of the environment in which it is being implemented.

Section 2 shows the development of the proposal; the equipment required for its

The design of the proposal will be partitioned in stages. Each stage involves material and equipment required for the tests, measurements, and results obtained. **Figure 2** shows the process diagram that will follow the treatment of the signal

The general processing diagram shown in **Figure 2** consists of five stages which

**Stage A.** It consists of the acquisition of the audible signal that is recovered from the environment. The use of an omnidirectional microphone is feasible here, the foregoing because the audio signal can be obtained from any point but not from a specific one. A microphone is a transducer device that converts sound waves into electrical voltage changes. In simple words, the microphone will record all the signals captured from any side from where they are received since the entire microphone structure is sensitive to sound. Next, **Figure 3** shows the pickup pattern of an omnidirectional microphone.

that may be high or low frequencies) in order to obtain y(n).

be perceived and the adhered noise.

*Attenuation of Environmental Noise through Digital Filtering*

*DOI: http://dx.doi.org/10.5772/intechopen.91784*

**Figure 1.**

*Generic structure of an adaptive filter.*

the person with whom you are talking.

error that should be the minimum possible.

implementation and its constituent parts are defined.

captured from the environment.

will be explained below:

**39**

**2. Design of the proposed algorithm and scheme**

The signal d(n) is the expected audio signal to be perceived by the human ear, x (n) is the input signal to the filter (what is perceived from the environment), y(n) is the response signal that grants the filter, and e(n) is the error required to adapt the filter parameters by comparing the desired signal with that obtained. This comparison is the difference between the desired (reference) signal with respect to the signal obtained at the filter output.


**Table 1.** *Noise levels and their effects.*

*Attenuation of Environmental Noise through Digital Filtering DOI: http://dx.doi.org/10.5772/intechopen.91784*

#### **Figure 1.**

Noise pollution significantly interferes with interpersonal communication and increases workplace accidents and traffic accidents. **Table 1** shows the noise levels

A filter may consist of hardware or software applied to a set of noisy or contaminated data in order to extract information of interest. Noise can be generated by any source. In this chapter, a digital solution is proposed that allows the attenuation of noise attached to audible signals that are perceived by the human ear. An adaptive filter is a device that is useful for processing an input signal by blocking and/or

On the other hand, the adaptive filter [1] has a feedback loop that has the ability to minimize the error produced by the comparison between the output signal (after processing) and a reference signal (expected signal). The above allows to guarantee that the human ear is not contaminated with the unwanted signals and only to rescue the information that needs to be received. **Figure 1** shows the structure of an

The signal d(n) is the expected audio signal to be perceived by the human ear, x (n) is the input signal to the filter (what is perceived from the environment), y(n) is the response signal that grants the filter, and e(n) is the error required to adapt the filter parameters by comparing the desired signal with that obtained. This comparison is the difference between the desired (reference) signal with respect to the

and their effects on the human ear.

*Noise and Environment*

signal obtained at the filter output.

adaptive filter.

**Table 1.**

**38**

*Noise levels and their effects.*

allowing some parts of it (unwanted noise or vibration).

*Generic structure of an adaptive filter.*

The operation of the algorithm that will represent the filter [2] is as follows:


In general, the filter [3] will be used to inhibit signals that are not desired (technically this process is known as attenuating the amplitude of the noise signal) to "clean" the signal that must be perceived by the human ear. The proposed technique adapts to the conditions of the environment in which it is being implemented.

Section 2 shows the development of the proposal; the equipment required for its implementation and its constituent parts are defined.

## **2. Design of the proposed algorithm and scheme**

The design of the proposal will be partitioned in stages. Each stage involves material and equipment required for the tests, measurements, and results obtained. **Figure 2** shows the process diagram that will follow the treatment of the signal captured from the environment.

The general processing diagram shown in **Figure 2** consists of five stages which will be explained below:

**Stage A.** It consists of the acquisition of the audible signal that is recovered from the environment. The use of an omnidirectional microphone is feasible here, the foregoing because the audio signal can be obtained from any point but not from a specific one. A microphone is a transducer device that converts sound waves into electrical voltage changes. In simple words, the microphone will record all the signals captured from any side from where they are received since the entire microphone structure is sensitive to sound. Next, **Figure 3** shows the pickup pattern of an omnidirectional microphone.

perform its function. The original signal has to be subjected to three basic opera-

a. Amplitude: reflects the change in pressure from the highest peak to the minimum. A waveform with large amplitude has a volume of equal

c. Frequency: describes the number of cycles produced in a second. If the

If the frequency of a sound signals increases, the sound will be perceived as more acute because the wavelength decreases. Otherwise, when the frequency is low, the repetitions decrease, the wavelength increases, and, therefore, the sound tends to be

The first step to digitize the original signal consists of the sampling operation. The sampling of a sound signal consists of taking small representative pieces of the

**Figure 4** shows the representation of two analog signals of different

b. Cycle: describes a single repeated sequence of pressure changes.

frequency is high, the tone of the sound will be higher.

signal so that they are then encoded in binary digits to digitize them.

Sound, by nature, is an analog signal. It is produced by vibrations in the air that force the union of nearby molecules in the air by slightly raising its pressure. Such pressure changes reach the ear by vibrating the receptors and decoding to produce the sound. Some of the characteristics of the vibrations (in waveforms) are the following:

tions to digitize it: sampling, quantization, and coding.

*Attenuation of Environmental Noise through Digital Filtering*

*DOI: http://dx.doi.org/10.5772/intechopen.91784*

magnitude; otherwise, the volume is quieter.

frequencies each.

a high-pitched tone.

**Figure 4.**

**41**

*Analogic signals with different frequencies.*

**Figure 3.** *Omnidirectional pickup pattern in a microphone.*

It should be mentioned that an omnidirectional pickup pattern captures the sound obtained from any direction.

It is important to capture all the sounds of the environment. For example, if a person is going to cross the street, it is important that he consider the sounds of fenced cars and be attentive to all perceived sounds in order to avoid an accident. The proposal to be designed will minimize the sound intensity of these noises but not completely mitigate them.

The proposal will capture all the random noises in the environment and process them, and when it detects that there is one that exceeds the threshold of intensity allowed, it will multiply it by a reduction factor and thus decrease its volume. The process described above is purely adaptive, which means that "n" number of repetitions will be performed until it approaches the expected result.

**Stage B.** The signal captured in **Stage A** is of analog format since it was captured in its natural form. However, for this stage, it is necessary to convert the captured signal into digital format since the computer and the processor require scanning to

### *Attenuation of Environmental Noise through Digital Filtering DOI: http://dx.doi.org/10.5772/intechopen.91784*

perform its function. The original signal has to be subjected to three basic operations to digitize it: sampling, quantization, and coding.

Sound, by nature, is an analog signal. It is produced by vibrations in the air that force the union of nearby molecules in the air by slightly raising its pressure. Such pressure changes reach the ear by vibrating the receptors and decoding to produce the sound. Some of the characteristics of the vibrations (in waveforms) are the following:


**Figure 4** shows the representation of two analog signals of different frequencies each.

If the frequency of a sound signals increases, the sound will be perceived as more acute because the wavelength decreases. Otherwise, when the frequency is low, the repetitions decrease, the wavelength increases, and, therefore, the sound tends to be a high-pitched tone.

The first step to digitize the original signal consists of the sampling operation. The sampling of a sound signal consists of taking small representative pieces of the signal so that they are then encoded in binary digits to digitize them.

**Figure 4.** *Analogic signals with different frequencies.*

It should be mentioned that an omnidirectional pickup pattern captures the

It is important to capture all the sounds of the environment. For example, if a person is going to cross the street, it is important that he consider the sounds of fenced cars and be attentive to all perceived sounds in order to avoid an accident. The proposal to be designed will minimize the sound intensity of these noises but

The proposal will capture all the random noises in the environment and process them, and when it detects that there is one that exceeds the threshold of intensity allowed, it will multiply it by a reduction factor and thus decrease its volume. The process described above is purely adaptive, which means that "n" number of

**Stage B.** The signal captured in **Stage A** is of analog format since it was captured in its natural form. However, for this stage, it is necessary to convert the captured signal into digital format since the computer and the processor require scanning to

repetitions will be performed until it approaches the expected result.

sound obtained from any direction.

*Omnidirectional pickup pattern in a microphone.*

**Figure 2.**

**Figure 3.**

**40**

*General processing diagram.*

*Noise and Environment*

not completely mitigate them.

The condition that the signal has to remain representative of the original must be considered. To cover the previous condition, the following equation known as Nyquist's theorem must be followed:

$$f \succeq \mathfrak{Z} \mathfrak{f} o \tag{1}$$

foregoing because the noise will have a modulation in its amplitude in order to

applications especially in people suffering from hearing loss.

adaptively manipulate its sound intensity.

one in order to obtain an error.

minimum tolerated error.

prevailing in the environment:

**43**

1.Detection of the desired signal mixed with the ambient noise.

There is a wide variety of algorithms that can be classified into algorithms of low computational complexity with low convergence speed and high convergence speed algorithms with high computational cost. Some of the most used algorithms due to their low computational complexity are the averaged least squares algorithm (LMS, least mean square) and its normalization (NLMS). These algorithms have been successfully implemented in different systems. However, the convergence speed of these algorithms is slow. This means that the processing turns out to be so slow that it reduces the tests in real time, which would be not feasible for noise reduction

The proposed scheme of **Figure 6** consists of the following stages for the process:

2.Attenuation of the environmental noise signal at a rate of 1/x, in order to reduce its amplitude and thus mitigate it. The above will happen only if the signal amplitude exceeds the allowed threshold. The frequency of it is not a factor to consider because we do not intend to distort the noise, only

3.The desired signal is subtracted from the amplitude-modulated noise signal.

4.The signal obtained after the process is compared with respect to the desired

Next, the process to follow for the mathematical analysis of the system is shown:

1.The desired signal with the adhered noise is picked up by the system. It is worth mentioning that the desired sound signal is contaminated by the noise

5.Based on the error obtained, the system is adapted to decide whether to reperform the process or has already converged to the permissible and

eradicate it as well as possible.

*Scheme of the proposal to cancel environmental noise.*

*Attenuation of Environmental Noise through Digital Filtering*

*DOI: http://dx.doi.org/10.5772/intechopen.91784*

**Figure 6.**

It is worth mentioning that if the sampling frequency is high, that is, if more samples are taken from the original signal in a certain time, the collection of significant parts for later digitization will allow a better fidelity of the original signal but now in a digital format and ready for computational processing.

**Figure 5** represents the two signals shown in **Figure 4** but now applying the sampling theorem; that is, small samples (pieces) representative of the signal are taken to be the "objects" to which binary codes will be assigned and thus digitized.

The quantization is the second step. It consists in assigning amplitude values to each sample obtained in the previous step, the foregoing in order to identify each sample that will be encoded.

Finally, and based on the amplitude assigned to each sample, a binary combination is correlated to each of them to be identified. Samples that have the same amplitude will have the same binary code.

**Stage C.** In this step the adaptive filter algorithm will be implemented in the dsPIC3020F10 in order to perform the tests in real time. The processor will receive the signal in digital format (under previous scanning in **Stage B**) in order to process it to block the noise and allow the desired signal to pass through.

A DsPIC is a type of microprocessor known as a digital signal processor. It is responsible for real-time processing, a feature that is essential when non-tolerance of delays is required. Basically, a DsPIC [4] acquires a digital signal and processes it to improve it (in the case of audio, a clearer and sharper sound).

The algorithm to be implemented is the noise amplitude modulation least mean square (NAMLMS) which consists in a modification of the LMS algorithm, the

**Figure 5.** *Sampled analogic signals.*

*Attenuation of Environmental Noise through Digital Filtering DOI: http://dx.doi.org/10.5772/intechopen.91784*

The condition that the signal has to remain representative of the original must be

It is worth mentioning that if the sampling frequency is high, that is, if more samples are taken from the original signal in a certain time, the collection of significant parts for later digitization will allow a better fidelity of the original signal

**Figure 5** represents the two signals shown in **Figure 4** but now applying the sampling theorem; that is, small samples (pieces) representative of the signal are taken to be the "objects" to which binary codes will be assigned and thus digitized. The quantization is the second step. It consists in assigning amplitude values to each sample obtained in the previous step, the foregoing in order to identify each

Finally, and based on the amplitude assigned to each sample, a binary combina-

**Stage C.** In this step the adaptive filter algorithm will be implemented in the dsPIC3020F10 in order to perform the tests in real time. The processor will receive the signal in digital format (under previous scanning in **Stage B**) in order to process

A DsPIC is a type of microprocessor known as a digital signal processor. It is responsible for real-time processing, a feature that is essential when non-tolerance of delays is required. Basically, a DsPIC [4] acquires a digital signal and processes it

The algorithm to be implemented is the noise amplitude modulation least mean

square (NAMLMS) which consists in a modification of the LMS algorithm, the

tion is correlated to each of them to be identified. Samples that have the same

*fs*≥2*fo* (1)

considered. To cover the previous condition, the following equation known as

but now in a digital format and ready for computational processing.

it to block the noise and allow the desired signal to pass through.

to improve it (in the case of audio, a clearer and sharper sound).

Nyquist's theorem must be followed:

*Noise and Environment*

sample that will be encoded.

**Figure 5.**

**42**

*Sampled analogic signals.*

amplitude will have the same binary code.

**Figure 6.** *Scheme of the proposal to cancel environmental noise.*

foregoing because the noise will have a modulation in its amplitude in order to eradicate it as well as possible.

There is a wide variety of algorithms that can be classified into algorithms of low computational complexity with low convergence speed and high convergence speed algorithms with high computational cost. Some of the most used algorithms due to their low computational complexity are the averaged least squares algorithm (LMS, least mean square) and its normalization (NLMS). These algorithms have been successfully implemented in different systems. However, the convergence speed of these algorithms is slow. This means that the processing turns out to be so slow that it reduces the tests in real time, which would be not feasible for noise reduction applications especially in people suffering from hearing loss.

The proposed scheme of **Figure 6** consists of the following stages for the process:


Next, the process to follow for the mathematical analysis of the system is shown:

1.The desired signal with the adhered noise is picked up by the system. It is worth mentioning that the desired sound signal is contaminated by the noise prevailing in the environment:

$$s(n) = d(n) + \frac{1}{\mathfrak{x}} \nu(n) \tag{2}$$

where

s (n) = signal captured;


It is worth mentioning that the modulation coefficient will also be submitted to the adaptation algorithm in order to serve to attenuate the noise signal generated in the environment.

2.The correlation between both signals must be equal to zero. The above explains the fact that both are linearly independent:

$$r\_{sv} = \frac{cov(s, v)}{cov(s) \* cov(v)} = \mathbf{0} \tag{3}$$

3. Subsequently, the signal obtained is processed by the adaptive filter to produce the output:

$$\mathcal{Y}(n) = \sum\_{k=0}^{M-1} w\_k(n) \* \upsilon(n-k) \tag{4}$$

where

w (n) = the values of the adjustable coefficients of the adaptive filter; and. k = iteration for each adaptation.

4.The filter output y (n) is subtracted from the main signal s (n). The above defines the error signal:

$$
\sigma(n) = \mathfrak{s}(n) - \frac{1}{\mathfrak{x}} \nu(n) \tag{5}
$$

The blue graph shows the desired audio signal and used as a reference to compare what you want the system to throw at the output. The green signal is a type of noise captured in the environment, it could be said to be a random signal since it does not have a defined pattern. Finally, the red signal is the mixture of the

in order to attenuate the noise signal and allow the sound signal to pass through. The designed filter adapts to the conditions of the environment in which it is implemented. Next, the following figures will show, in parts, the analysis obtained for the proposed algorithm and its comparison with others established in the

is a slope that could establish a tolerance margin at lower frequencies.

The objective is to submit the mixed signal (red) to the proposed adaptive system

**Figure 8** shows the frequency response obtained for the passage of frequencies above the 10 kHz frequency which, on average, is the frequency at which a desirable sound oscillates. It is worth mentioning that, as a result of the adaptation, there

As can be seen in **Figure 8**, the response of the NAMLMS algorithm shows a slight hesitation in stability but achieves greater convergence with respect to the responses of the other algorithms. The proposed algorithm was based on the LMS algorithm that results in obvious instability and convergence, concluded, but showing latency.

**Figure 9** shows the response for the part of the filter that allows low frequencies to pass through. The comparison with the responses of other filtering algorithms is

previous ones.

**45**

**Figure 7.**

existing literature related to the subject.

*Combination between the original (desired) signal and the noise signal.*

*Attenuation of Environmental Noise through Digital Filtering*

*DOI: http://dx.doi.org/10.5772/intechopen.91784*


$$y(n) = d(n) \text{ and } \text{with } e(n) = \mathbf{0} \tag{6}$$

In this case, the system output is noise-free, and the noise cancelation is perfect. Correspondingly, the signal to noise ratio of the output is infinitely large.

**Stage D.** It consists of validating the filter response and also verifying that it inhibits unwanted frequencies (adhered noise). It is important to consider that the frequencies that will correspond to the desired signal must be the sounds that are desired and everything else must be blocked.

**Stage E.** It is the inverse process of **Stage B**. The sound has an analog nature, so it must be converted to that format so that the receiver can decode it.

#### **3. Results of the implementation**

In the environment, the desired sound signals are mixed with the unwanted signals (noise) [5]. **Figure 7** shows the acquisition of the desired signal and the mixed noisy signal.

*Attenuation of Environmental Noise through Digital Filtering DOI: http://dx.doi.org/10.5772/intechopen.91784*

*s n*ð Þ¼ *d n*ð Þþ <sup>1</sup>

where

*Noise and Environment*

the environment.

the output:

k = iteration for each adaptation.

signal to noise ratio of the system output.

desired and everything else must be blocked.

**3. Results of the implementation**

mixed noisy signal.

**44**

6.The adaptive filtering operation is perfect when:

defines the error signal:

where

s (n) = signal captured; d (n) = desired signal;

v (n) = adhered environmental noise; and x = modulation coefficient for attenuation.

the fact that both are linearly independent:

*y n*ð Þ¼

*x*

It is worth mentioning that the modulation coefficient will also be submitted to the adaptation algorithm in order to serve to attenuate the noise signal generated in

2.The correlation between both signals must be equal to zero. The above explains

3. Subsequently, the signal obtained is processed by the adaptive filter to produce

w (n) = the values of the adjustable coefficients of the adaptive filter; and.

4.The filter output y (n) is subtracted from the main signal s (n). The above

*x*

*y n*ð Þ¼ *d n*ð Þ and with *e n*ð Þ¼ 0 (6)

5.The error signal is the one used to adjust the coefficient values of the adaptive filter and control loop around filtering operations and subtraction are related. Minimizing the mean square value of the error signal means maximizing the

In this case, the system output is noise-free, and the noise cancelation is perfect.

**Stage E.** It is the inverse process of **Stage B**. The sound has an analog nature, so

In the environment, the desired sound signals are mixed with the unwanted signals (noise) [5]. **Figure 7** shows the acquisition of the desired signal and the

**Stage D.** It consists of validating the filter response and also verifying that it inhibits unwanted frequencies (adhered noise). It is important to consider that the frequencies that will correspond to the desired signal must be the sounds that are

Correspondingly, the signal to noise ratio of the output is infinitely large.

it must be converted to that format so that the receiver can decode it.

*e n*ð Þ¼ *s n*ð Þ� <sup>1</sup>

*rsv* <sup>¼</sup> *cov s*ð Þ , *<sup>v</sup>*

*M* X�1 *k*¼0

*v n*ð Þ (2)

*cov s*ð Þ <sup>∗</sup> *cov v*ð Þ <sup>¼</sup> <sup>0</sup> (3)

*wk*ð Þ *n* ∗ *v n*ð Þ � *k* (4)

*v n*ð Þ (5)

**Figure 7.** *Combination between the original (desired) signal and the noise signal.*

The blue graph shows the desired audio signal and used as a reference to compare what you want the system to throw at the output. The green signal is a type of noise captured in the environment, it could be said to be a random signal since it does not have a defined pattern. Finally, the red signal is the mixture of the previous ones.

The objective is to submit the mixed signal (red) to the proposed adaptive system in order to attenuate the noise signal and allow the sound signal to pass through.

The designed filter adapts to the conditions of the environment in which it is implemented. Next, the following figures will show, in parts, the analysis obtained for the proposed algorithm and its comparison with others established in the existing literature related to the subject.

**Figure 8** shows the frequency response obtained for the passage of frequencies above the 10 kHz frequency which, on average, is the frequency at which a desirable sound oscillates. It is worth mentioning that, as a result of the adaptation, there is a slope that could establish a tolerance margin at lower frequencies.

As can be seen in **Figure 8**, the response of the NAMLMS algorithm shows a slight hesitation in stability but achieves greater convergence with respect to the responses of the other algorithms. The proposed algorithm was based on the LMS algorithm that results in obvious instability and convergence, concluded, but showing latency.

**Figure 9** shows the response for the part of the filter that allows low frequencies to pass through. The comparison with the responses of other filtering algorithms is

**Figure 8.** *Frequency response of the high-pass filter.*

visualized, and the rapid convergence and adaptation of the response offered by the

**Figure 10** shows the response of the system in general. It can be seen that the union of the two previous frequency responses generates the response of a frequency range through the filter. The objective is to suppress very low frequencies and very high frequencies that can be considered as noise and distortions that affect

**Figure 11** shows the signal obtained as a response from the system. When compared with the reference signal, a correlation value of 0.912 is calculated which, according to the theory, indicates that there is a strong correlation between both signals and that, although the noise is slightly perceived, the desired signal is clearly

This chapter proposes an ambient noise cancelation system that allows to attenuate the noise that is mixed with the desired audible signals. Based on the results obtained, it is verified that the convergence of the algorithm is rapid relative to other existing ones, so it can be useful for use in new-generation cochlear implants

Subjectively, the algorithm has a slight perception of adhered noise but does not affect its acoustic apparatus or the decoding of the messages of the desired sound signal. Objectively, the correlation between the signal obtained after the respective system was calculated to the reference signal (desired), and an almost perfect result was achieved. Obviously a 100% correlation is not possible because the noise adhered to the desired sound and it is impossible not to modify some samples of said

We would like to thank the Universidad del Valle de México for providing us with the necessary means to carry out the research and provide part of the resources

proposed system stands out.

perceived.

**Figure 11.** *Signal resulting.*

signal.

required.

**47**

**Acknowledgements**

**4. Conclusion**

the human ear causing hearing loss.

*Attenuation of Environmental Noise through Digital Filtering*

*DOI: http://dx.doi.org/10.5772/intechopen.91784*

or as treatments against symptoms of hearing loss.

**Figure 9.** *Frequency response of the low-pass filter.*

**Figure 10.** *Frequency response of the band pass filter.*

*Attenuation of Environmental Noise through Digital Filtering DOI: http://dx.doi.org/10.5772/intechopen.91784*

**Figure 8.**

**Figure 9.**

**Figure 10.**

**46**

*Frequency response of the high-pass filter.*

*Noise and Environment*

*Frequency response of the low-pass filter.*

*Frequency response of the band pass filter.*

visualized, and the rapid convergence and adaptation of the response offered by the proposed system stands out.

**Figure 10** shows the response of the system in general. It can be seen that the union of the two previous frequency responses generates the response of a frequency range through the filter. The objective is to suppress very low frequencies and very high frequencies that can be considered as noise and distortions that affect the human ear causing hearing loss.

**Figure 11** shows the signal obtained as a response from the system. When compared with the reference signal, a correlation value of 0.912 is calculated which, according to the theory, indicates that there is a strong correlation between both signals and that, although the noise is slightly perceived, the desired signal is clearly perceived.

#### **4. Conclusion**

This chapter proposes an ambient noise cancelation system that allows to attenuate the noise that is mixed with the desired audible signals. Based on the results obtained, it is verified that the convergence of the algorithm is rapid relative to other existing ones, so it can be useful for use in new-generation cochlear implants or as treatments against symptoms of hearing loss.

Subjectively, the algorithm has a slight perception of adhered noise but does not affect its acoustic apparatus or the decoding of the messages of the desired sound signal.

Objectively, the correlation between the signal obtained after the respective system was calculated to the reference signal (desired), and an almost perfect result was achieved. Obviously a 100% correlation is not possible because the noise adhered to the desired sound and it is impossible not to modify some samples of said signal.

#### **Acknowledgements**

We would like to thank the Universidad del Valle de México for providing us with the necessary means to carry out the research and provide part of the resources required.

On the other hand, we also thank the Instituto Politécnico Nacional that has provided us with its facilities for experimentation and subject to real tests of the system I proposed. Without this valuable help, the development of this work would have been practically impossible.

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*DOI: http://dx.doi.org/10.5772/intechopen.91784*

*Attenuation of Environmental Noise through Digital Filtering*

[2] Bhupendra KA, Miles AC, Chong HC. A sampled analog MOS LSI adaptive filter. IEEE Journal of Solid-State Circuits. 1979;**14**(1):148-154

Frequency-domain Echo cancellation in digital multicarrier modulation systems. IEEE Transactions on Communications.

[4] Ávalos JG, González JM, Velázquez J,

[5] Garcia M, Diego P, Quintana R: DSP implementation of the FxLMS algorithm

for active noise control: Texas instruments TSM320C6713DSK. In: Automatic Control (CCAC), IEEE 2nd Colombian Conference; 2015. pp. 1-6

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Sánchez JC. Implementación del algoritmo LMS con error codificado en el DSP TMS320C6713. In: Congreso Nacional de Ingeniería Electromecánica y de Sistemas, Ciudad de México, México, Noviembre 26–30, 2007; 2007

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