Survey study on the methods of bird vocalization classification

Rassak, Simna; Nachamai, M; A, Krishna Murthy

doi:10.1109/icctac.2016.7567337

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…cage noises, wing flaps). Following Rassak et al (2016), the neural network used is a multi-layer perceptron with one hidden layer composed of 300 neurons with two neurons in the output layer corresponding to one of two possible categories: bird call or cage noise. If the sound belongs to the first category, then the system broadcasts a prerecorded call from the speaker placed inside the robot.…”

Section: Interactive Vocal Loops Between the Bird And The Robotmentioning

confidence: 99%

Design of a robotic zebra finch for experimental studies on developmental song learning

Araguas

Guellaï

Gauthier

et al. 2022

Journal of Experimental Biology

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Birdsong learning has been consolidated as the model system of choice for exploring the biological substrates of vocal learning. In the Zebra Finch (Taeniopygia guttata), only males sing and they develop their song during a sensitive period in early life. Different experimental procedures have been used in the laboratory to train a young finch to learn a song. So far, the best method to get a faithful imitation is to keep a young bird singly with an adult male. Here we present the different characteristics of a robotic zebra finch that was developed with the goal to be used as a song tutor. The robot is morphologically similar to a real-size finch: it can produce movements and sounds contingently to the behaviours of a live bird. We present preliminary results on song imitation, and other possible applications beyond the scope of developmental song learning.

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Section: Interactive Vocal Loops Between the Bird And The Robotmentioning

confidence: 99%

Design of a robotic zebra finch for experimental studies on developmental song learning

Araguas

Guellaï

Gauthier

et al. 2022

Journal of Experimental Biology

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“…When the features that you want to expand are conditionally independent of each other, the Bayesian classifiers are efficient [57]. Additionally, in instances such as the estimation of bird weight, we use energy and pitch features that are independent of each other should be regarded as they do not influence the estimation [58].…”

Section:  Naïve Bayesmentioning

confidence: 99%

Birds Sound Classification Based on Machine Learning Algorithms

Mehyadin

Abdulazeez

Hasan

et al. 2021

AJRCoS

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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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“…The marine mammal sound classification and its impact on marine life have been studied in [4,5]. Several studies [6][7][8][9][10][11][12] have focused on bird sound identification, classification, and its challenges. The study in [13] has performed the insect species classification based on their sound signals.…”

Section: Introductionmentioning

confidence: 99%

Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets

2018

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The domestic cat (Feliscatus) is one of the most attractive pets in the world, and it generates mysterious kinds of sound according to its mood and situation. In this paper, we deal with the automatic classification of cat sounds using machine learning. Machine learning approach for the classification requires class labeled data, so our work starts with building a small dataset named CatSound across 10 categories. Along with the original dataset, we increase the amount of data with various audio data augmentation methods to help our classification task. In this study, we use two types of learned features from deep neural networks; one from a pre-trained convolutional neural net (CNN) on music data by transfer learning and the other from unsupervised convolutional deep belief network that is (CDBN) solely trained on a collected set of cat sounds. In addition to conventional GAP, we propose an effective pooling method called FDAP to explore a number of meaningful features. In FDAP, the frequency dimension is roughly divided and then the average pooling is applied in each division. For the classification, we exploited five different machine learning algorithms and an ensemble of them. We compare the classification performances with respect following factors: the amount of data increased by augmentation, the learned features from pre-trained CNN or unsupervised CDBN, conventional GAP or FDAP, and the machine learning algorithms used for the classification. As expected, the proposed FDAP features with larger amount of data increased by augmentation combined with the ensemble approach have produced the best accuracy. Moreover, both learned features from pre-trained CNN and unsupervised CDBN produce good results in the experiment. Therefore, with the combination of all those positive factors, we obtained the best result of 91.13% in accuracy, 0.91 in f1-score, and 0.995 in area under the curve (AUC) score.

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Survey study on the methods of bird vocalization classification

Cited by 5 publications

References 25 publications

Design of a robotic zebra finch for experimental studies on developmental song learning

Design of a robotic zebra finch for experimental studies on developmental song learning

Birds Sound Classification Based on Machine Learning Algorithms

Domestic Cat Sound Classification Using Learned Features from Deep Neural Nets

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