Classify bird species audio by augment convolutional neural network

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dc.contributor.authorJasim, Hasan Abdullah
dc.contributor.authorAhmed, Saadaldeen R.
dc.contributor.authorIbrahim, Abdullahi Abdu
dc.contributor.authorDuru, Adil Deniz
dc.date.accessioned2022-08-08T13:25:20Z
dc.date.available2022-08-08T13:25:20Z
dc.date.issued2022en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractUsing convolutional neural networks, this thesis aims to create a system for fully automated identification of bird species based on spectrogram images. Spectrogram analysis is more difficult when trying to make an advance identification of a bird species. On a publicly available dataset of 8000 audio examples, we've begun by analyzing the challenges of bird species detection, segmentation, and classification to achieve our goal. It has been determined also that deep learning-based technique CNN with Fully convolutional learning calls for easier results because it eliminates the possible future modelling error caused by an imprecise knowledge of bird species and works well on coding in cohesion with the spectral analysis kernel using the librosa library. We have concluded. After obtaining the dataset from the open-source repository, it is then processed locally. For training, testing, and validation we used a subset of the dataset of 8000 sound samples. We offered a method relying on a CNN reset learned that proved to be very quick and optimum because it was first needing the spectrogram analytic kernel to learn what to class in bird species, and then it gets the system trained on features extracted. In a novel 9-step implementation, a bird species spectrogram can be detected from an audio sample. There was a loss of less than 0.0063, and the conditioning workouts accuracy is 0.9895 for the system, 0.9 as precision, and training and validation use 50 epochs in system.en_US
dc.identifier.citationJasim, H. A., Ahmed, S. R., Ibrahim, A. A., Duru, A. D. (2022). Classify bird species audio by augment convolutional neural network. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), IEEE.en_US
dc.identifier.isbn9781665468350
dc.identifier.scopus2-s2.0-85133958958
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/2816
dc.indekslendigikaynakScopus
dc.institutionauthorJasim, Hasan Abdullah
dc.institutionauthorIbrahim, Abdullahi Abdu
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.isversionof10.1109/HORA55278.2022.9799968en_US
dc.relation.publicationcategoryKonferans Öğesi - Ulusal - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAudioen_US
dc.subjectBirdsen_US
dc.subjectClassificationen_US
dc.subjectCNNen_US
dc.subjectIdentificationen_US
dc.subjectLibrosaen_US
dc.subjectSpectrogramen_US
dc.subjectXeno-Cantoen_US
dc.titleClassify bird species audio by augment convolutional neural network
dc.typeConference Object

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