Sound Analyser for Bioacoustic Monitoring System Using LabVIEW

M.H.M Razali a, N.Azmi, A. Zakaria a , L. M. Kamarudin a

S.M.Mamduh, K. Kamarudin a, S.A.A. Shukora, A. Y. M. Shakaff a, N.A. Rahima, F.S.A. Saada a Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis

02600 Jejawi, Perlis, Malaysia mohdhafiez.unimap@gmail.com

Abstract— Traditionally, the variety of animal species are accessed using conventional method that is normally costly, consumed a lot of time and the most important missing element is that it does not equipped with any permanent recording device. In order to replace this conventional method, an automated sound recording device is developed. This device has the capability to record the animal vocalization and has the power system to be used in remote area. This paper presents the work of sound analyzers for acoustic and health monitoring of the forest. The sound analyzer is important for the researcher as it can be used for further analysis such as frequency of animal vocalization and pattern recognition. The developed system uses the LabVIEW (Laboratory Virtual Instrument Engineering Workbench) platform.

Keywords—animal vocalization; labview; sound analyser.

I. INTRODUCTION Most of animal monitoring procedure needs the expertise

in the field because the data are often obtained through indirect cues which is animal vocalization. Many problems will occur in this situation. The first problem is, there are only a few of experts that are able to identify the animal based on the sound emitted. The second problem is, the experts vary in their abilities to identify the species and this may lead to observer bias[1]. In most filed studies, the researchers will go through a time and resource consuming learning process or sometimes, need to rely on artificial marking, such as ring and radio-tracking, which can be difficult to administer and almost invasive for the animal[2].

The interested in the sound recording of wildlife has grown

fast for the last few years. Bioacoustic methods have become a powerful tool for monitoring biodiversity. Their specific potential lies in the detection of cryptic vocalizing animals, even in the absence of an observer. Birds are the example of good subjects for bioacoustic monitoring since most of the species use vocalizations to attract mates and to advertise territories. The aim of monitoring biodiversity is to assess changes in ecological communities through time. Surveys should be designed in such a way that the obtained data are precise enough to identify trends. Good indicators of environmental health are based on quantitative surveys,

allowing estimation of population trends of key species. This means for bioacoustic monitoring, we need an appropriate recording technique, an appropriate data acquisition protocol, effective tools to detect species within the recordings and methods for estimating the number of animals.

Recording birds, frogs, mammals, insects, and other

natural sounds can be very challenging and rewarding. Many animals produce sound for communication which is non-accidental sound or doing an activity such as moving, eating or flying. Non-accidental sound have capabilities to provide an information on species and have been used so many years. Species and even individual recognition based on animal vocalizations is possible for many animals and normally can be implemented as a useful tool or technique in the field study and monitoring of animal species[3]. Most of the previous study on the animal species recognition using sound vocalization only focusing on in-lab environment without the presence of real environmental noise[4].

The number of recorded song or call can be used to

measure the number of an animal in the testing area. The purpose of counting the sound detection is to measure or analysis the health of the forest. However, it is difficult to estimate the absolute number of animals because the call rate is different between species or within species. The call rate of animal being strongly depend on the particular environment condition[5].

II. BACKGROUND STUDY This project is about the development of sound analyzers

for acoustic monitoring in the forest using LabVIEW as the platform. The recorded sound is inserted into this system for feature extraction procedure. In addition, the Fast Fourier transform is used to obtain the frequency of the emitted sound. The spectrogram is used to monitor the element found in the recorded sound. The acoustic monitoring device has been deployed at Hutan Simpan Universiti Utara Malaysia (UUM) Sintok, Kedah as shown in Fig. 1. The device records 30 seconds for every 15 minutes intervals. After 30 seconds, the device will go to sleeping mode to avoid the power drain on the battery. All the recorded sound is saved in micro secure digital (SD) card located at the audio circuit.

2014 IEEE 2014 International Conference on Computer, Communication, and Control Technology (I4CT 2014), September 2 - 4,2014 - Langkawi, Kedah, Malaysia

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III. METHODOLOGY

A. Recording device The recording device is developed using microcontroller

Arduino. There are a few Arduino shields that are used together with this system. They are rugged audio shield, real time clock shield and LCD shield. The rugged audio shield has two inputs and 1 output. This shield has 1 slot for a micro SD card. The real time clock shield has the capability to save the current time even the microcontroller board does not power up. A directionality of microphone is the most important element during a recording process. In addition, omnidirectional microphone has an equally sensitivity to all directions, and enable to produce a more natural sound to a human. If the microphone has more directionality, it is able to focus on target animal or a particular sound source. The microcontroller and shields were put in a weatherproof box to protect them from weather changes in the deployment area. Fig. 2 shows the fully assemble of the acoustic recording device.

This acoustic recording device has been set to record 30 seconds sound for every 15 minutes. After 30 seconds the system will be put in sleep mode to prevent the system drain too much power from the battery. The recorded sound will be saved in micro SD card by using the current record time as the filename. By implementing this, this would ease the researcher or the users to find the file based on the time recorded.

B. Operation of sound analyzer using LabVIEW The sound file will be plotted in time domain to see the

pattern of the sound. Then, the sound is filtered using IIR (Infinite Impulse Response) digital filter. In signal processing, the filter is used to extract useful part of the signal or to remove unwanted part of the audio signal. IIR filter has certain properties, which make this filter the preferred design over FIR (Finite Impulse Response)[6]. The windowing is used before analyzed in fast Fourier transform.

According to the system interface design, the system used many controls like button, waveform and indicators. Therefore, it can provide the better human Machine Interface[7].

C. Sendig Audio signal via wireless transmisson Each application often has different requirement in terms of data rates, latency, payload size, transmission bandwidth, transmission coverage, lifetime, and power consumption [8] [9] [10]. In this study, data rates, latency, and lifetime play an important role in ensuring data reliability [11]. Data rates for wireless transmission in this study depend on the application setup. As stated previously, the 30 seconds recorded audio signal first stored in the SD card and then the data is ready to be transmitted, thus, a larger data payload is required in order to transmit the recorded signal. In comparison with other wireless network such as wireless sensor network, and Bluetooth as illustrated by Table I, WI-FI offer a greater transmission bandwidth and larger payload size [12] [13].

This study attempt to employ the wireless transmission

using WI-FI module (WiFly RN-171) as depicted in the following Fig. 3. Power consumption is an important challenge that must be taken into account in designing this system because Wi-Fi consumes high power compared to other system. Moreover, this system is deployed in the forest, thus, alternative energy such as solar harvesting might not be a reliable option.

Fig 3. Wi-Fi module for wireless transmission.

Fig. 2 Acoustic recording device

Fig 1. Deployment process.

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Table I : Comparison between Wi-Fi, Bluetooth and Zigbee

Wireless technology

IEEE802.11/ Wi-Fi

IEEE802.15.1/ Bluetooth

IEEE802.15.4 /Zigbee

Range ~ 10 – 100 m ~ 10 – 100 m ~ 10 - 30 m

Data rates 11Mbps, 54Mbps 1Mbps 250Kbps

Bandwidth required 22 MHz 15 MHz (Dynamic) 3 MHz (Static)

Transmit Power 100mW 1-100mW 1mW

Power Consumption Medium Low Ultra Low

IV. RESULT AND DISCUSSION

A. Spectrogram of raw sound Fig. 4 shows the spectrogram plot of the raw recorded

sound. The spectrogram is plot based on time, amplitude and frequency. There are 2 detections of sound in the spectrogram. The frequency ranges between 3000 Hz to 4000 Hz is cricket sound while 5000 Hz to 6000 Hz is cicadas sound.

The background noise of the forest lies in the ranges between 0 Hz to 2500 Hz as shown in Fig. 5 In the red box, there is a sound detection in between 5000 Hz to 6500 Hz. Every animal in the forest has their own sound frequency.

B. Filter sound using IIR Filter The raw recorded sound is filter using an IIR filter to

extract the animal sound from the background noise. The specifications for this filter are given as:

Topology : Butterworth Type : Bandpass Order : 20 Lower frequency : 2 KHz Upper frequency : 7 KHz Passband ripple : 15 Stopband attenuation : 100

This specification is based on the sound detection in the obtained spectrogram. Figure 6 shows blue line indicates the raw sound while the red line indicates the output sound after an IIR filter is applied. The sound signal shows the sound of crickets. There is a repetition of the same sound in 30 seconds recording. The Butterworth is the only filter that maintains its shape for higher order and a steeper decline in the stopband.

Figure 7 shows the sound detection in time domain is same with the sound detection in the spectrogram. The sound that is detected in the spectrogram is produced by the birds.

Fig. 4 Spectogram of cricket and cicadas.

Fig. 5 Spectrogram of bird.

Fig. 6 Raw sound and filtered sound waveform.

Fig. 7 Sound detection in spectrogram.

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C. Windowing and Fast Fourier Transform After the sound is filtered, the windowing and FFT

calculation are applied to get the exact frequency of the sound. Fig. 8 and fig. 9 show the pattern of spectrum before and after the windowing is applied. The windowing in the sound signal makes some improvement in the frequency resolution and it makes easier to detect the exact peak frequency in the spectrum. Based on the Fig. 9, the peak frequency of the cricket is between 3000 Hz to 3500 Hz. Hanning window is the preferred windowing because it has a good frequency resolution and reduce spectral leakage. The Hann window is satisfactory in 95 % cases[14].

CONCLUSION

Generally, most previous study focused on specific species of an animal in the forest. Bioacoustics classification across different species is still new and needed to be developed. There is a need to increase more study on the limitation of processing capacity of sensors since little work has been done. It is important to have an integrated online monitoring for bioacoustic monitoring system sound analyzer as the medium in order to analyze the recorded data from the forest. By having this, the system is able to replace the observer bias occurred among the expertise in animal sound vocalization.

Bioacoustics sound differentiation was able to achieve using spectrogram projection. The raw recorded sound was subjected to IIR Butterworth filter and Hanning windowing to remove baseline and white noise. The battery lifetime was optimized by implementing 30 seconds sound recording at 15 minutes intervals. 15 repetitions of crickets chirping were observed in the recordings. Thus, it was evident that 30 seconds duration of recording at 15 minutes interval are surplus and enough to reflect the bioacoustics component in the forest.

ACKNOWLEDGMENT This work is supported by the ERGS grant 9010-00036

and by the Ministry of Higher Education, Malaysia. Special thanks to the Centre of Excellence for Advanced Sensor Technology (CEASTech), Universiti Malaysia Perlis (UniMAP) for providing financial support in this research and collaboration of National Instrument and NI- Unimap COE to make this project successful.

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Fig. 8 Power spectrum before windowing

Fig. 9 Power spectrum after windowing

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