An Efficient Model for a Vast Number of Bird Species Identification Based on Acoustic Features

Wang, Hanlin; Xu, Yingfan; Yan, Yu; Lin, Yucheng; Ran, Jianghong

doi:10.3390/ani12182434

Cited by 22 publications

(8 citation statements)

References 60 publications

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“…For instance, Xie improved ELM by using differential evolution to classify MFCC features of nine birdsongs, with a maximum accuracy of 89.05% [46]. Wang et al fused Mel-spectrogram and MFCC as input features and used LSTM to recognize 264 birdsongs, with an average accuracy of 77.43% [47]. Murugaiya et al combined the improved GTCC feature with probability enhanced entropy to classify twenty bird sounds in Borneo using SVM, with an accuracy of 89.5% [48].…”

Section: Discussionmentioning

confidence: 99%

Improved Broad Learning System for Birdsong Recognition

Lu,

Zhang,

et al. 2023

Applied Sciences

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Birds play a vital and indispensable role in biodiversity and environmental conservation. Protecting bird diversity is crucial for maintaining the balance of nature, promoting ecosystem health, and ensuring sustainable development. The Broad Learning System (BLS) exhibits an excellent ability to extract highly discriminative features from raw inputs and construct complex feature representations by combining feature nodes and enhancement nodes, thereby enabling effective recognition and classification of various birdsongs. However, within the BLS, the selection of feature nodes and enhancement nodes assumes critical significance, yet the model lacks the capability to identify high quality network nodes. To address this issue, this paper proposes a novel method that introduces residual blocks and Mutual Similarity Criterion (MSC) layers into BLS to form an improved BLS (RMSC-BLS), which makes it easier for BLS to automatically select optimal features related to output. Experimental results demonstrate the accuracy of the RMSC-BLS model for the three construction features of MFCC, dMFCC, and dsquence is 78.85%, 79.29%, and 92.37%, respectively, which is 4.08%, 4.50%, and 2.38% higher than that of original BLS model. In addition, compared with other models, our RMSC-BLS model shows superior recognition performance, has higher stability and better generalization ability, and provides an effective solution for birdsong recognition.

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Section: Discussionmentioning

confidence: 99%

Improved Broad Learning System for Birdsong Recognition

Lu,

Zhang,

et al. 2023

Applied Sciences

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“…In Tanttu et al (2003), tracking of the first harmonic components of the spectrogram is used to extract the characteristics and SOM (Unsupervised Learning) for the classification. Wang et al (2022) performs the Melspectrogram and MFCC of the songs, these are the inputs for the deep learning model, Long short-term memory (LSTM) is a variant of recurrent neural networks (RNN), which integrates specific gates to recover the shortterm or long-term context of the input, LSTM is used to extract features and classify the song. In Zhang et al (2019), the 3-D convolution kernels of the CNN were used to extract both positional and temporal characteristics from the Melspectrogram and with this improve the classification.…”

Section: Introductionmentioning

confidence: 99%

Traditional and modern processing of digital signals and images for the classification of birds from singing

Luis,

Maira,

Rafa

2024

Int. J. Appl. Sci. Eng.

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Avitourism depends on the understanding of birds and the intention of birdwatchers to see or hear a specific species. Increase of this activity strengthens the economy of communities and helps finance biodiversity conservation projects. Technological products that incorporate traditional and modern tools for signal and image processing facilitate the tracking, classification and observation of birds. This article has two approaches. The first one proposes and evaluates a lightweight classification model that uses feature vector extracted from the bird's song spectrum and is based on comparison of Euclidean distance between sample features and a set vector by species. The second approach adapts and evaluates convolutional neural network architectures for bird classification using the spectrogram of the bird's song. The methodology applied in both approaches consists of: pre-processing, feature extraction, classification and evaluation metrics. The main results are the feasibility of the proposed lightweight classification model with an accuracy of 0.8 and a loss of 2.32, and the feasibility of using convolutional neural networks with an accuracy above 0.9 and a loss of less than 1, in the ResNet50, VGG19, and InceptionV3 architectures, this using as a minimum 30 spectrograms per species during training. It is concluded that the model that best meets the needs of fewer samples and less computational resources required for training is ResNet50. Additionally, it is discussed to combine the two approaches in a hierarchical and hybrid classification model that allows to introduce in the reduction and classification layers of the neural network features of another type and of other sources.

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“…In recent years, advances in machine learning have led to the proposal of deep learningbased bird classification methods, including audio features for classification and recognition [5][6][7] and image features [8][9][10]. Among them, bird classification methods relying on audio features are easily disrupted by environmental noise, necessitating audio denoising as a prerequisite in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5

Yi,

Qian,

et al. 2023

Sensors

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Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took a part-based approach, dividing the identification task into two parts: part detection and identification classification. We proposed an improved bird part detection algorithm based on YOLOv5, which can handle partial overlap and complex environmental conditions between part objects. The backbone network incorporates the Res2Net-CBAM module to enhance the receptive fields of each network layer, strengthen the channel characteristics, and improve the sensitivity of the model to important information. Additionally, in order to boost data on features extraction and channel self-regulation, we have integrated CBAM attention mechanisms into the neck. The success rate of our suggested model, according to experimental findings, is 86.6%, 1.2% greater than the accuracy of the original model. Furthermore, when compared with other algorithms, our model’s accuracy shows noticeable improvement. These results show how useful the method we suggested is for quickly and precisely recognizing different bird species.

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An Efficient Model for a Vast Number of Bird Species Identification Based on Acoustic Features

Cited by 22 publications

References 60 publications

Improved Broad Learning System for Birdsong Recognition

Improved Broad Learning System for Birdsong Recognition

Traditional and modern processing of digital signals and images for the classification of birds from singing

Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5

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