Birds Sound Classification Based on Machine Learning Algorithms

Abstract

The bird classifier is a system that is equipped with an area machine learning technology and uses a machine learning method to store and classify bird calls. Bird species can be known by recording only the sound of the bird, which will make it easier for the system to manage. The system also provides species classification resources to allow automated species detection from observations that can teach a machine how to recognize whether or classify the species. Non-undesirable noises are filtered out of and sorted into data sets, where each sound is run via a noise suppression filter and a separate classification procedure so that the most useful data set can be easily processed. Mel-frequency cepstral coefficient (MFCC) is used and tested through different algorithms, namely Naïve Bayes, J4.8 and Multilayer perceptron (MLP), to classify bird species. J4.8 has the highest accuracy (78.40%) and is the best. Accuracy and elapsed time are (39.4 seconds).

A Review of Automated Bioacoustics and General Acoustics Classification Research

“…Most bioacoustics originated from animal vocals (74%) such as frogs croaking [ 40 , 41 , 42 ] or birds chirping [ 9 , 18 , 50 , 58 , 61 ] while a few originated from their locomotion (24%) such as bees [ 56 , 81 , 82 ] or mosquitoes [ 59 ] in flight as shown in Figure 7 . Insects produce locomotion sounds in five different ways: stridulation, percussion, vibration, tymbal mechanism, or air expulsion [ 14 ].…”

Predicting Major Adverse Cardiovascular Events in Acute Coronary Syndrome: A Scoping Review of Machine Learning Approaches

“…Hypertension (HTN), 18 systolic blood pressure (SBP), 15 heart rate (HR), 14 electrocardiography (ECG) findings 13 (STEMI), 8 ST-segment depression, 3 NSTEMI, 2 T-wave inversions, transient ST-segment elevation, right bundle branch block (RBBB), 2 left bundle branch block (LBBB) 2 , current smoking, 13 diastolic blood pressure (DBP), 11 Killip Class 9 (Class I—II, Class III, Class IV), Echocardiographic finding 9 left ventricular ejection fraction (LVEF) 8 ), cardiogenic shock, 7 chest pain, 6 heart rhythm, 6 dyspnea, 4 body mass index (BMI), 3 sweat, 2 bleeding, 2 abdominal circumference, vertigo and systemic weakness, awareness, estimated glomerular filtration rate (EGFR), ischemia location, mitral regurgitation grade, waist-to-hip ratio (WHR)…”

“…Diabetes mellitus, 18 chronic renal disease, 8 dyslipidemia, 6 cancer, 4 coronary heart disease (CHD), 4 chronic lung disease, 4 arteriosclerosis, 3 chronic liver disease, valvular heart disease…”

“…ML is generally divided into three types, i.e., supervised, unsupervised, and semi-supervised learning methods. 18 Labeled and unlabeled data are used in supervised and unsupervised learning, respectively, while both types of data can be employed in semi-supervised learning. 19 However, supervised ML, due to the heterogeneity of the medical data, is preferred in medical settings.…”

“…19 The performance of ML is enhanced according to the number of high-quality samples. 14 18 19 20 Suitable ML approaches offer generalizable analysis and interpretation of complex variables. 21 In fact, ML approaches apply algorithms to detect trends and patterns not identified through traditional statistical approaches.…”