International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 07 | July 2022
p-ISSN: 2395-0072
www.irjet.net
ILLEGAL LOGGING DETECTION BASED ON ACOUSTICS Vinaya Raj1, Ann Rose K Jose2, Arun George3 , Athul K4 1Student(B.Tech.),
Electrical Department, Mar Athanasius College of Engineering, Kothamangalam, India Electrical Department, Mar Athanasius College of Engineering, Kothamangalam, India ---------------------------------------------------------------------***--------------------------------------------------------------------memory to capture 60 seconds of audio at a time, and the Abstract - An acoustic experiment based on the discovery of 2Student(B.Tech.),
Arduino Nano 33 BLE Sense has enough memory for 16 seconds.
illegal logging in the forest. A framework for the automatic detection of a number of forest activities including illegal logging, poaching and any other illegal activities using audio surveillance is presented. It incorporates audio recording surveillance channels using a microphone and receives audio samples that are then processed and classified into machine learning into incoming and outgoing sounds. The sound of various functions such as tree falling, chainsaw sound, human voice and natural sounds of wind, animals and birds are also recorded in the system and unwanted sounds from this are eliminated using ML technology. This method is modular, easy to produce and energy efficient as it relies on audio evidence and uses powerful ML algorithms. The system can be adapted to different forest features and can be used equally during the day and night.
Training is done using edge impulse software. After the collection of raw data processing and reading can be done using Mel-Frequency Energy to separate the data. A second sound sample will suffice to determine whether the wood cutting sound, natural sound or animal noise, so you should make sure the window size is set to 1000 ms. Each green sample is cut into multiple windows, as well as a window Upgrade field controls the removal of each subsequent window from the first. For example, an increase in Window value of 1000 ms can cause each window to start 1 second after the start. 1.2 Model Training
Key Words: Machine Learning, Acoustics, Logging, Audio
The Artificial Neural Network (ANN) is an algorithm used for machine learning of sound segregation, in which each database is cut into chunks and transferred to a processing block. The result of the MFE block is a spectrogram, which is also given a reading block. The study block contains a neural network component similar to a biological neuron. The model will learn individual sound samples that are similar to the individual.
evidence, Forest.
1. INTRODUCTION Forests are a natural resource that has many important benefits for biodiversity. There are many factors that affect the existence and sustainability of forests. The biggest threat is illegal logging that can lead to uncontrolled and irreversible deforestation. In addition, illegal logging is considered a major threat to biodiversity, as forests support about 90 percent of the world's biodiversity. Over the decades, advances in remote sensing technology, as well as advances in information and communication technology (ICT) have led to the use of automated or automated surveillance solutions in a wide range of areas such as like forests. A method based on acoustic experiments to find deforestation in the forest introduces. The method presented is modular and as it depends on sound evidence, it can be adapted to suit forest features and can be used equally during the day and night.
3. DEPLOYMENT OF MODEL 3.1 CONTINUOUS INTERFACING When audio classification is performed to detect sounds in real time it is necessary to ensure that the entire piece of information is recorded and analyzed, in order to avoid missing events. The device needs to capture audio samples and analyze them at the same time. Through continuous defrosting, small sample baths or fragments are used and transferred to the determination process. In the process of determining the baths are set in chronological order in FIFO (First In First Out) corresponding to the size of the model. After each repetition, the oldest piece is removed from the end of the bath and a new piece is inserted at the beginning. In each piece, the concept is used several times depending on the number of pieces used in the model. So much so that considering a model with a 1000ms model window and the pieces of each model set to 4 results in a piece size of 250ms.
2. SOFTWARE SIMULATION 1.1 Creating Dataset The raw data set is taken and divided into three main labels such as "wood cutting sound", natural sounds "and" animal sounds "to hear each sound separately. The main goal is to detect the sound of wood cutting. The simultaneous audio recording varies depending on the device memory. The ST B-L475E-IOT01A developer board has enough
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