Machine Learning Approach to Classify Birds on the Basis of Their Sound

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).

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“…2a, there may be no meaningful detail in the initial signal while, as seen in the other Fig. 2b, may have useful features [45].…”

Section: Pre-processingmentioning

confidence: 96%

Birds Sound Classification Based on Machine Learning Algorithms

Mehyadin

Abdulazeez

Hasan

et al. 2021

AJRCoS

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“…It helps the performance of the model when the range of inputs are in a set known scale, as it fixates some certainty in the range of weights that we will be looking at [36,37].…”

Section: Normalizationmentioning

confidence: 99%

Transfer Learning for Audio Waveform to Guitar Chord Spectrograms Using the Convolution Neural Network

Jadhav

Patel²,

Jhaveri

et al. 2022

Mobile Information Systems

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Automatic chord recognition has always been approached as a broad music audition task. The desired output is a succession of time-aligned discrete chord symbols, such as GMaj and Asus2. Automatic music transcription is the process of converting a musical recording into a human-readable and interpretable representation. When dealing with polyphonic sounds or removing certain limits, automatic music transcription remains a difficult undertaking. A guitar, for example, presents a greater challenge, as guitarists can play the same note in a variety of places. The study makes use of CNN functionality to generate the guitar tab; initially, the constant-Q transform was used to turn the input audio file into short time spectrograms that the CNN model utilises to analyse the chord. The paper developed a method for extracting chord sequences and notes from audio recordings of solo guitar performances. For intervals in the supplied audio, the proposed approach outputs chord names and fret-board notes. The model described here has been refined to achieve an accuracy of 88.7%. The model’s ability to properly tag audio clips is an incredible advancement.

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“…Our study will utilize a supervised learning algorithm to detect bogus news. Supervised learning applies to two distinct sorts of problems: classification and regression [24][25][26]. In this endeavor, we classify news as 'false' or 'real'.…”

Section: False Connectionmentioning

confidence: 99%

Identifying Fake Digital Information Using Machine Learning Algorithms: Performance Analysis and Recommendation System

Patel,

Jadhav,

Jhaveri

et al. 2022

Contemp. Math.

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This work focuses on the detection of fake digital information using various machine learning and deep learning algorithms to prevent its spread through Internet of Things (IoT) devices and systems. The research highlights the significance of detecting and preventing false or misleading information in critical areas such as healthcare, public safety, and emergency response. The study compares the performance of several supervised machine learning algorithms and identifies logistic regression as the most accurate (98.03%). The empirical analysis used data from The Indian Express, PolitiFact, and Kaggle and leveraged natural language processing (NLP) to prepare, clean, and model the data. To detect fraudulent posts, the study employed random forest, a supervised machine learning algorithm, which achieved an impressive accuracy rate of 99.71% on a Kaggle dataset. The research also developed a model for detecting false reporting related to COVID-19, utilizing the support vector machine technique, which achieved an accuracy rate of 78.69%. The presented work also determined the authenticity of images through convolutional neural networks (CNNs). Lastly, a content-based recommendation system was developed to enhance people’s security and confidence.

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Machine Learning Approach to Classify Birds on the Basis of Their Sound

Cited by 9 publications

References 14 publications

Birds Sound Classification Based on Machine Learning Algorithms

Birds Sound Classification Based on Machine Learning Algorithms

Transfer Learning for Audio Waveform to Guitar Chord Spectrograms Using the Convolution Neural Network

Identifying Fake Digital Information Using Machine Learning Algorithms: Performance Analysis and Recommendation System

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