Background noise cancellation of manatee ... - Semantic Scholar

Background noise cancellation of manatee vocalizations using an adaptive line enhancer Zheng Yan Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida 32611-6250

Christopher Niezreckia兲 Department of Mechanical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854

Louis N. Cattafesta III Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, Florida 32611-6250

Diedrich O. Beusse College of Veterinary Medicine, University of Florida, P.O. Box 100126, Gainesville, Florida 32610-0126

共Received 29 November 2005; revised 10 April 2006; accepted 11 April 2006兲 The West Indian manatee 共Trichechus manatus latirostris兲 has become an endangered species partly because of an increase in the number of collisions with boats. A device to alert boaters of the presence of manatees is desired. Previous research has shown that background noise limits the manatee vocalization detection range 共which is critical for practical implementation兲. By improving the signal-to-noise ratio of the measured manatee vocalization signal, it is possible to extend the detection range. The finite impulse response 共FIR兲 structure of the adaptive line enhancer 共ALE兲 can detect and track narrow-band signals buried in broadband noise. In this paper, a constrained infinite impulse response 共IIR兲 ALE, called a feedback ALE 共FALE兲, is implemented to reduce the background noise. In addition, a bandpass filter is used as a baseline for comparison. A library consisting of 100 manatee calls spanning ten different signal categories is used to evaluate the performance of the bandpass filter, FIR-ALE, and FALE. The results show that the FALE is capable of reducing background noise by about 6.0 and 21.4 dB better than that of the FIR-ALE and bandpass filter, respectively, when the signal-to-noise ratio 共SNR兲 of the original manatee call is −5 dB. © 2006 Acoustical Society of America. 关DOI: 10.1121/1.2202885兴 PACS number共s兲: 43.30.Sf, 43.60.Bf 关EJS兴

I. INTRODUCTION

According to the United States Coast Guard, the number of registered boats in Florida has grown to over 900 000 as of 2001 共United States Coast Guard, 2002兲. The population of the West Indian manatee 共Trichechus manatus latirostris兲 has also increased slightly in recent years, reaching an estimated population of 3276. Between 1995 and 2002 the percentage of mortalities of the West Indian manatee due to watercraft strikes has risen from 22% to 31% 共Florida Department of Environmental Protection, Division of Marine Resources, 1996; Florida Fish and Wildlife Conservation Commission, 2002兲. This has led to increased research into manatee avoidance technologies. A spatially fixed system to alert boaters of the presence of manatees is desired, as opposed to a device mounted on an individual boat that would be cost prohibitive to boaters and not likely adopted by the boating community. Once the presence of one or more manatees is detected within a navigable channel, their presence, and thus the need for boaters to slow to idle speed, could be

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signaled via a variety of methods, such as brilliant flashing strobe lights atop strategically placed marker pilings straddling that section of channel where manatees are present. Several methods to detect manatees have been proposed and include 共1兲 a passive acoustic based detection system 共Herbert et al., 2002; Mann et al., 2002; Niezrecki et al., 2003兲, 共2兲 an above water infrared detection system 共Keith, 2002兲, and 共3兲 an underwater active sonar based system 共Bowles, 2002兲. A more detailed literature review of manatee vocalizations can be found in the paper by Niezrecki et al. 共2003兲. It is important to point out that in some situations manatees can exhibit long periods of silence when no vocalizations are made 共Nowacek et al., 2003兲. The implementation of a passive acoustic detection system will certainly have to account for periods when a manatee is not vocalizing. Assuming the detection ranges are sufficiently large for feasibility, the system could be implemented to slow boats down for a long period of time. If a manatee is detected, the warming system could be configured to indicate that a manatee is present in the waters for a period of hours or even a day.

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J. Acoust. Soc. Am. 120 共1兲, July 2006

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Therefore, calling rates are probably not as important to address as the manatee vocalization detection range and are not likely to limit the feasibility of a system. Previous research has shown that background noise limits the manatee vocalization detection range of acousticbased detection systems 共Phillips et al., 2005, Yan et al., 2005兲. Primary examples include boat and snapping shrimp noise. Secondary noise sources are generated by wind, rain, water movement, and other biological species. Having a system with a large detection range is critical for practical implementation. By improving the signal-to-noise ratio 共SNR兲 of the manatee vocalization signal, it is possible to extend the detectable range of manatee vocalizations while also reducing the false alarm rate and the number of missed calls. An efficient method to improve the SNR of manatee calls using a finite impulse response 共FIR兲 adaptive line enhancer 共ALE兲 was previously proposed by Yan et al. 共2005兲. However, the performance of the FIR-ALE is limited by several factors. First, the misadjustment, defined as the dimensionless ratio of the average excess mean square error to the minimum mean square error, is given by Widrow et al. 共1976兲. M = ␮L␾xx共0兲

共1兲

where ␮ represents the step size of the adaptive filter, L is the order 共i.e., number of weighting coefficients兲 of the adaptive filter, and ␾xx is the autocorrelation function for the input x共k兲 of the ALE,

␾xx共j兲 = E关x共k兲x共k + j兲兴,

共2兲

where E关兴 represents the expected value. Increasing L will narrow the filter pass band about the harmonics, thus improving the estimate of signal amplitude for a given SNR of the input. On the other hand, a small L is desired to minimize the misadjustment, as shown in Eq. 共1兲, and reduce the required computation time. Furthermore, although an increase in M can be compensated for by decreasing the step size ␮, a small step size will unfortunately decrease the convergence rate and the ability of the adaptive filter to track a nonstationary signal such as a manatee vocalization. An alternative approach uses an infinite impulse response 共IIR兲 structure. Although a FIR filter is easier to implement, its performance is generally inferior to an IIR filter with the same order. That is to say, the IIR structure ALE can provide the same performance as FIR-ALE with much lower order 共Chang, 1993兲. The primary challenge associated with an IIR filter is maintaining its stability. Within this paper, a constrained IIR adaptive line enhancer, proposed by Glover and Chang, which is called feedback ALE 共FALE兲, is implemented to reduce the background noise 共Glover and Chang, 1989兲. Additionally, a bandpass filter is used as a baseline to compare the performance of FIR-ALE and FALE. In the previous work by. Yan et al.., only four vocalizations were evaluated 共Yan et al., 2005兲. In this paper a library of 100 manatee calls was created, and each call was placed into one of ten categories. The perfor146

J. Acoust. Soc. Am., Vol. 120, No. 1, July 2006

FIG. 1. Block diagram of the feedback adaptive line enhancer 共FALE兲.

mance of each method is evaluated via the 100 different calls. This allows a more comprehensive evaluation of the various algorithms. The paper is organized as follows. The theoretical development of the FALE algorithm is presented in Sec. II, and the library of the manatee calls is established in Sec. III. The simulation results are shown in Sec. IV, and the conclusions are discussed in Sec. V.

II. THEORETICAL DEVELOPMENT OF THE FEEDBACK ALE

Widrow et al. first proposed the adaptive line enhancer based on the Widrow-Hoff least-mean-square 共LMS兲 adaptive algorithm 共Widrow et al., 1975兲. As stated earlier, the ALE can be classified into two main categories: FIR- 共an all zero filter兲 based and IIR- 共a pole-zero filter兲 based algorithms. The performance of the FIR-ALE implemented by Yan et al. on the background noise cancellation of manatee vocalizations is described in a prior paper 共Yan et al., 2005兲. A constrained IIR adaptive line enhancer called FALE was proposed by Glover et al. and is implemented to reduce the background noise of the manatee calls studied in this paper. The block diagram of the FALE is shown in Fig. 1. The primary input x共k兲 is assumed to be of the form x共k兲 = s共k兲 + n共k兲,

共3兲

where, for the manatee problem, s共k兲 represents the manatee vocalization, n共k兲 represents the background noise, and x共k兲 represents the observed manatee call corrupted by background noise, which is measured by a single hydrophone. The reference input x⬘共k兲 is a delayed version of the signal that is the summation of the scaled version of the primary input x共k兲 and the narrow-band output sˆ共k兲: x⬘共k兲 = ␤sˆ共k − ⌬兲 + 共1 − ␤兲x共k − ⌬兲,

共4兲

where ␤ is the feedback constant and ⌬ represents the delay parameter that decorrelates the background noise. The enhanced narrow-band signal sˆ共k兲 is added to the primary input 共via the feedback path兲. The narrow-band output, when initially fed back, is matched in phase to that in the primary input but is smaller in amplitude and corrupted by residue noise 共Glover and Chang, 1989兲. As the narrow-band output is refiltered, the noise component is progressively reduced. This process improves the correlation between the narrowband signal in the reference and primary inputs. Yan et al.: Background noise reduction of manatee vocalizations

The transfer function between the primary input and the narrow-band output for the algorithm of FALE, H共z兲, is given by H共z兲 =

Sˆ共z兲共1 − ␤兲z−⌬F共z兲 = , X共z兲 1 − ␤z−⌬F共z兲

共5兲

L−1 akz−k is the z transform of the weights of where F共z兲 = 兺k=0 the adaptive FIR filter. z−⌬F共z兲 represents the transfer function of the FIR filter with the line delay included. 兩F共z兲兩 is less than 1 on the unit circle and increases to infinity at the origin because the poles of the FIR structure are located at the origin. Since 0 ⬍ ␤ ⬍ 1, and 1 / ␤ ⬎ 1, the roots of the denominator of H共z兲 must lie within the unit circle. Therefore, FALE is stable as long as 兩F共z兲兩 is less than 1 on the unit circle 共Chang, 1993兲. The convergence rate of an adaptive filter is an important factor for tracking a nonstationary signal. However, the poles of the IIR filter decrease the convergence rate compared to an FIR filter. Therefore, a larger step size is needed for the FALE to track nonstationary signals. However, the feedback structure of the FALE moves the zeros and poles closer to the unit circle as ␤ → 1, which makes the bandwidth narrower. Therefore, a small disturbance in the weights may cause a large fluctuation in the frequency response of the FALE, and a smaller step size is required to maintain a stable adaptation process of the FALE. Hence, there exists a tradeoff between the stability and tracking ability of the FALE. Marshall suggested that if the FALE is able to track a nonstationary signal, increasing the amount of feedback ␤ can improve the accuracy of its instantaneous frequency estimate 共Marshall, 1994兲. However, the simulation results presented in Sec. IV show that if the FALE cannot track a nonstationary signal, its performance is worse than that of the FIR-ALE. Therefore, the step size should be large enough to track the nonstationary signal of interest yet small enough to maintain stability. If ␤ = 0, then the FALE reduces to the FIR-ALE. There exists a range of ␤ that presumably makes the FALE superior to the FIR-ALE. The simulations of Glover and Chang show that the “optimum” value of ␤ varies from 0.4 for high SNR cases to somewhat less than 0.9 for lower SNR cases. For ␤ ⬍ 0.4, the impact of the feedback within the systems is not readily apparent, while for ␤ ⬎ 0.9, the estimation error is increased by the adaptation oscillations due to feedback 共Glover and Chang, 1989兲. The feedback constant ␤ is therefore set to 0.85 in this paper. Since the fundamental frequency of a manatee calls typically lies between 2 and 5 kHz, a bandpass filter 共tenth-order Butterworth IIR filter兲 is used as baseline system to compare and evaluate the performance of the FIR-ALE and FALE. The pass band of the filter is given by

f1 ⬍ f ⬍ f2,

III. ESTABLISHMENT OF THE LIBRARY OF THE MANATEE CALLS

O’Shea and the United States Geological Survey created an extensive library of manatee recordings between 1981 and 1984 共O’Shea, 1981–1984兲. Yan et al. used these recordings to quantify the performance of the FIR-ALE algorithm. However, only four manatee calls were used in their simulations which are not enough to fully evaluate the performance of the FIR-ALE versus the FALE 共Yan et al., 2005兲. In order to better test the performance of the different algorithms a library of the manatee calls is established. The library consists of ten different categories that include a total of 100

共6兲

where f 1 = 1.2 kHz and f 2 = 20 kHz. The bandpass filter is also used to preprocess the data before applying the adaptive filter. This greatly reduces the energy of noises at low frequencies that may degrade the performance of the ALE. In order to reduce the noise with low frequencies, the J. Acoust. Soc. Am., Vol. 120, No. 1, July 2006

value of f 1 cannot be set too small. Since the highest frequency of the manatee calls is less than 20 kHz, the value of f 2 is set to 20 kHz. Experience has shown that a significant portion of the low-frequency noise can be reduced without significantly affecting a manatee call by preprocessing the signal with a bandpass filter. It should be mentioned that the fundamental frequency of some manatee calls may be as low as 600 Hz and the manatee calls without harmonics are generally of higher frequency, around 4 or 5 kHz 共Schevill and Watkins, 1965兲. The manatee calls with a fundamental frequency around 600 Hz generally have most of their energy in the higher harmonics and so they are likely to be passed by the high-pass filter.

FIG. 2. The procedure used to categorize manatee calls. Yan et al.: Background noise reduction of manatee vocalizations

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FIG. 3. The method used to compute the SNR of an original manatee call. 共a兲 Pure manatee call and 共b兲 background noise.

different manatee calls. Each category contains ten calls that were obtained from the extensive library of recordings created by O’Shea. Therefore, a total of 100 manatee calls are used to evaluate the performance of these three algorithms, i.e., bandpass filter, FIR-ALE, and FALE. It is important to point out that categorization is performed purely from a signal detection perspective. No attempt is being made to infer what the significance of each category indicates in terms of manatee behavior. The categorization procedure is shown in Fig. 2. The first level of categorization discriminates a vocalization that either does or does not have some discernable harmonic content in which the dominant frequencies are at least 20 dB larger than the neighborhood frequencies. Likewise, if the powers of several frequencies of an individual manatee call are at least 20 dB larger than their neighborhood harmonic frequencies and the difference between them is less than 20 dB, all of these frequencies of the manatee call can be called dominant frequencies. Most manatee vocalizations do have harmonic structure. For the calls that do possess harmonics, a further subdivision is to identify calls that either have one, two 共or three兲, or more than three dominant harmonics. The next level of categorization is to identify if the calls have a dominant frequency change. A frequency change is defined as a frequency shift of the strongest harmonic 共for an individual manatee vocalization兲 in excess of 10%. These types of vocalizations can be used to test the frequency tracking ability of these two ALE algorithms. For those calls that do have a dominant frequency change, the last level of decomposition categorizes the frequency change as being continuous or discrete. The discrete frequency change is defined as a frequency shift in excess of 10% within a duration of 10 ms. In order to discriminate between categories, a labeling system is adopted. The codes 0, 1, and 2 are used to repre148


sent different categories, and each manatee vocalization is categorized by a four digit number. For example, a manatee call with code 1111 indicates that it has clear harmonic frequency content, the number of dominant frequencies is two or three, and the dominant frequency changes discretely with time. The ten different possible categories in the flow chart are represented within the library by ten different manatee calls that all have the same characteristics. For a particular category, the following characteristics may be different from one call to the next: 共1兲 the location of the dominant frequency, 共2兲 the shape of the envelope of each manatee vocalizations in the time domain, 共3兲 the power distribution of the harmonics, and 共4兲 the overall power of the manatee call. In some manatee calls, no distinct harmonic frequencies occur, but these calls can still be regarded as narrow-band

FIG. 4. The method used to compute the SNR of the manatee call after filtering 共using the FALE in this case兲. Yan et al.: Background noise reduction of manatee vocalizations

FIG. 5. 共a兲 Pure manatee call, 共b兲 natural noise, 共c兲 boat-dominated noise, 共d兲 snapping shrimp noise, and 共e兲 superposition of manatee calls, natural noise, boat-dominated noise, and snapping shrimp noise.

signals when compared with the background noise. The narrower the bandwidth of the manatee calls and the wider the background noise, the better the performance of the ALE 共Yan et al., 2005兲. Therefore, for the manatee calls without distinct harmonic frequency, the performance of the ALE may degrade to some degree. Although the number of dominant frequencies of the manatee call may be one, two, three, or more, the energy of most manatee calls is dominated by one or two harmonics.

FIG. 6. 共a兲 Superposed signal after the bandpass filter is applied, 共b兲 superposed signal after FIR-ALE is applied, and 共c兲 superposed signal after FALE is applied.

IV. SIMULATION RESULTS

Although there are many sources of underwater noise, the two primary sources of background noise that typically corrupt a manatee call are boat noise and snapping shrimp noise. In practice, a manatee call also may be corrupted by noise created by rain, wind, fish, marine mammals, human activity, wave motion, etc. Noise other than snapping shrimp noise and boat noise are classified as “natural noise” in these simulations. Therefore, three types of noise are considered: boat noise, snapping shrimp noise, and natural noise. The performance of each algorithm is compared using the SNR. However, after filtering, the residue background noise cannot be separated from the manatee call; hence it is not possible to distinguish the noise and the manatee call during the time interval of the manatee call. Therefore a modified definition of SNR is used in this work. Five pure manatee calls and superposed background noise are shown in Figs. 3共a兲 and 3共b兲, respectively. SNRori represents the estimated SNR of the original manatee call corrupted by noise. As shown in Fig. 3共a兲, SNRori is computed by taking the root mean square 共rms兲 value of the time domain signal in the region where the pure manatee call is present and dividing that value by the rms value over the same time interval just prior to the call where only the background noise is present. J. Acoust. Soc. Am., Vol. 120, No. 1, July 2006

It is assumed that the background noise levels do not vary significantly over the duration of a manatee call. Hence, the same method is used to estimate the SNR of the manatee call after filtering 共see Fig. 4兲. As a result, the residue noise during the interval of the manatee call is unavoidably added to the true signal power. Therefore, the SNR of the manatee call after filtering is a little biased. In order to compare the performance of the FIR-ALE and FALE, the orders for these two algorithms are both set to 20 and the step sizes are set to 0.05 and 0.5, respectively. Typical time domain measurements of pure manatee calls 共category 1000兲, natural noise, boat noise, snapping shrimp noise, and the superposition of these four signals are shown in Figs. 5共a兲–5共e兲, respectively. The superposed signal after the bandpass filter, FTR-ALE, or FALE is applied are shown in Figs. 6共a兲–6共c兲, respectively. A purely qualitative visual comparison of these results indicates that FALE is most effective at improving the SNR. Next, the library of 100 manatee calls is used in the simulations to rigorously test these three algorithms. In simulations, the database of the manatee call is organized into 20 recordings. Each recording has five manatee calls. Therefore, each category has two recordings. In order to equally test Yan et al.: Background noise reduction of manatee vocalizations

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proper step size for FALE, which not only facilitates tracking a nonstationary signal but also maintains stability. Using the notation that the average SNR of each original manatee call category is represented by SNRori, the performance of the bandpass filter, FIR-ALE, and FALE for the manatee calls in each category and the overall average are shown in Table I. The average SNR of the original manatee call for each category is given by

冋冉冊冒冉兺冊册 10

SNRori = 10 ⫻ log10

10

兺 P2i

Q2j

i=1

FIG. 7. SNR improvement of the FALE compared to FIR-ALE for each manatee call when SNRori is equal to −5 dB.

each category, the background noise used is the same for each recording. However, the background noise is different for five manatee calls within one recording. In order to equally compare performance, the SNRori is set to −5 dB for all manatee calls. The difference in the a SNR of the FALE versus the FIR-ALE for each manatee call is shown in Fig. 7. There are only six manatee calls 共out of 100兲 for which the performance of the FALE is worse than that of the FIR-ALE. A qualitative inspection suggests that this is caused by the relatively large changes in the characteristic frequencies for three of the manatee calls and no clearly discernable harmonic frequencies for the other three manatee calls. The chosen settings of the FALE cannot adequately track the variation of the manatee call, especially when the SNR of the manatee call after bandpass filter is very low. As discussed earlier, the tracking ability of the FALE is worse than that of the FIR-ALE because of the feedback constant. From Eq. 共4兲, the weight in front of the primary input is small when the feedback constant ␤ is set to a large value. Thus, it is no surprise that each manatee call has a different optimal step size and feedback constant. Due to practical considerations, it is very important to select a fixed ␤ with a corresponding

,

共7兲

j=1

where Pi represents the rms value of the ith manatee call for a particular category and Q j represents the rms value of the jth noise component. Likewise, the method to compute the average SNR of the processed manatee calls after these three enhancement algorithms are applied is the same as that used to compute SNRori. GFIR-BPF and GFALE-BPF represent the gain or SNR improvement of FIRALE and FALE over the bandpass filter, respectively. GFALE−FIR represents the SNR improvement of the FALE over FIR-ALE. These simulation results show that both the FIR-ALE and FALE are effective at reducing the background noise of a manatee call. The average performance of the FALE is about 21.4 and 6.0 dB better than that of the bandpass filter and FIR-ALE, respectively. From Table I, it is seen that the performance of these two algorithms for category 0000 共manatee calls with no clearly discernable harmonics兲 is a little worse than the others. The improvement of FALE over FIR-ALE for this category is also smaller than others categories of calls. To further assess the performance of each filter for different levels of background noise, the bandpass filter, FIRALE, and FALE are compared when SNRori varies from −15 to 0 dB. The performance of each filter are shown in Figs. 8共a兲, 8共b兲, and 8共c兲, respectively. The results indicate that as the SNR of the original manatee call is reduced, the noisereduction performance of all algorithms is also reduced. The best performance is again achieved by the FIR-ALE and the FALE for manatee calls with one dominant frequency, while the worst performance is achieved for manatee calls without distinct harmonic frequencies. As shown in Fig. 8共a兲, the

TABLE I. The average performance of bandpass filter, FIR-ALE, and FALE for the manatee calls corresponding to each category 共dB兲.

150

Category

SNRori

After bandpass

After FIR-ALE

After FALE

GFIR−BPF

GFALE−BPF

GFALE−FIR

0000 1000 1010 1011 1100 1110 1111 1200 1210 1211 Average

−5.0 −5.0 −5.0 −5.0 −5.0 −5.0 −5.0 −5.0 −5.0 −5.0 −5.0

3.3 3.5 3.8 3.4 3.3 3.3 3.8 3.6 3.9 3.7 3.6

12.6 21.7 18.8 19.6 18.2 16.7 19.7 19.4 16.7 20.4 18.9

15.0 28.1 25.0 26.0 24.8 22.1 25.0 25.9 22.7 26.2 25.0

9.3 18.2 15.0 16.2 14.9 13.4 15.9 15.8 12.8 16.7 15.4

11.7 24.6 21.2 22.6 21.5 18.8 21.2 22.3 18.8 22.5 21.4

2.4 6.4 6.2 6.4 6.6 5.4 5.3 6.5 6.0 5.8 6.0


Yan et al.: Background noise reduction of manatee vocalizations

FIG. 9. 共a兲 Overall performance comparison between the bandpass filter, FIR-ALE, and FALE as a function of the SNR. 共b兲 Performance gains of the various algorithms using the same bandpass filter as a baseline as a function of the SNR.

Additional simulations are performed with the average SNR over ten categories to further evaluate the relative performance of the FIR-ALE and FALE algorithms. The overall average SNR of the original manatee calls, for all ten categories, is given by

冋冉冊冒冉兺冊册 100

Average of SNRori = 10 ⫻ log10

兺 P2i i=1

100

Q2j

.

j=1

共8兲

FIG. 8. 共a兲 SNR of manatee calls after the bandpass filter is applied for each category and various SNR. 共b兲 SNR of manatee calls after FIR-ALE is applied for each category and various SNRs. 共c兲 SNR of manatee calls after FALE is applied for each category and various SNRs.

SNR improvement of the bandpass filter does not vary significantly from one category to the next as the background noise level is changed. However the FIR-ALE and FALE is dependent on the category of the manatee call selected as the background noise level is changed 关see Figs. 8共b兲 and 8共c兲兴. J. Acoust. Soc. Am., Vol. 120, No. 1, July 2006

Likewise, the method to compute the average SNR of the processed manatee calls after these three enhancement algorithms are applied is the same as that shown in Eq. 共8兲. The performance comparison between the bandpass filter, FIRALE, and FALE are shown in Fig. 9共a兲 when the average of SNRori over ten categories varies from −15 to 0 dB. From this figure, the average SNR of the bandpass filter, FIRALE, and FALE are seen to be 0.6, 4.2, and 7.1 dB, respectively, when the average of SNRori is reduced to −15 dB. Yan et al.: Background noise reduction of manatee vocalizations

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A comparison of the performance achieved by the FIRALE and FALE compared to the baseline bandpass filter is shown in Fig. 9共b兲 when the average of SNRori varies from −15 to 0 dB. From this figure it is seen that the SNR improvement achieved by the FIR-ALE and FALE compared to the bandpass filter increases as the average of SNRori is increased. The improvement of the FALE over FIR-ALE becomes progressively larger when the average of SNRori is increased for low values of SNR up to −7 dB. This result is in agreement with the results by Chang 共1993兲. V. CONCLUSIONS AND FUTURE WORK

The practical implementation of an acoustic based manatee warning system is dependent on the minimum hydrophone spacing for the system. The required hydrophone spacing depends on the manatee vocalization strength, the decay of the acoustic signal strength with distance, and the background noise levels. In this paper, a feedback adaptive line enhancer algorithm 共FALE兲 is used to reduce the underwater background noise in order to improve the SNR of a library of 100 manatee calls spanning ten different signal categories. Simulations over a range of SNR indicate that the FALE and FIR-ALE are much more effective than simply using a bandpass filter. The FALE is capable of improving the SNR by, on average, an additional 6 dB compared to the FIR-ALE when the original SNR exceeds −5 dB. Exceptions are found in instances when the FALE cannot track the rapid frequency changes of some manatee calls. The simulations also show that the feedback and step size are two important factors that affect the stability, tracking ability, and the performance of the FALE. The improved SNR can presumably be used to extend the detection range of manatee vocalizations and reduce the number of false alarms and the number of missed calls. The results of this work may ultimately be used to realize a practical system that can warn boaters of the presence of manatees. ACKNOWLEDGMENTS

The authors would like to express their sincere appreciation to the Florida Sea Grant, Florida Fish and Wildlife Conservation Commission, and University of Florida Marine Mammal Program in supporting this research.

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Yan et al.: Background noise reduction of manatee vocalizations