Birdsong Recognition Based on MFCC combined with Vocal Tract Properties

Lv, Danju; Zhang, Yan; Fu, Qinjian; Xu, Haifeng; Liu, Jiang; Zi, Jiali; Huang, Xianbin

doi:10.1109/icmcce51767.2020.00334

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…About the application of MFCC, applying the MFCC to find elements of bird song is one example of how it is frequently used to extract features in speech recognition systems. [17] Since normalizing the values reduces the impact of noise in speech recognition systems, MFCC also has the drawback of having values that are not very stable in the face of additive noise.…”

Section: Feature Extractionmentioning

confidence: 99%

Speech emotion recognition using multiple classification models based on MFCC feature values

Chen,

Wu,

et al. 2023

ACE

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In everyday life, people are often influenced by emotions in our behaviour and language. When people use different emotions to articulate the same text, it can have a completely different effect. With the increasing demand for speech emotion recognition (SER), more machine learning and deep learning methods are being used to perform SER. matlab was used as the experimental tool in this study. The Berlin Database of Emotional Speech was used as the database. Feature extraction was performed by Mel Frequency Cepstrum Coefficients (MFCC) and based on these feature values, Support Vector Machines (SVM), K-Nearest Neighbors Algorithm (KNN), Semi-supervised graph-based classifier, ECOC classification model, Naive Bayes model and long short-term memory for predictive classification. The results surface that among the six classifiers, the best sentiment recognition method is LSTM, with 93.2% and 73.03% accuracies in the training and test groups respectively.

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Section: Feature Extractionmentioning

confidence: 99%

Speech emotion recognition using multiple classification models based on MFCC feature values

Chen,

Wu,

et al. 2023

ACE

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“…The data curation mainly includes the following steps: (1) original audio preprocessing, which includes resampling, conversion of the original audio format, audio truncation, bird song detection, audio merging, spectrogram generation, accurate data annotation, and data partitioning for training, verification and evaluation; and (2) feature extraction. Using Librosa (https://librosa.org/ (accessed on 20 May 2023)) to extract features and mixing the Mel spectrum and Mel frequency cepstrum coefficient (MFCC) [20] with dynamic features to enhance the effectiveness of bird song features (as shown in Figure 1). Subsequently, the Librosa library is used for the feature extraction of the Mel spectrum and MFCC.…”

Section: Datasetmentioning

confidence: 99%

A Novel Concept-Cognitive Learning Method for Bird Song Classification

Lin,

Wen,

Liao

2023

Mathematics

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Bird voice classification is a crucial issue in wild bird protection work. However, the existing strategies of static classification are always unable to achieve the desired outcomes in a dynamic data stream context, as the standard machine learning approaches mainly focus on static learning, which is not suitable for mining dynamic data and has the disadvantages of high computational overhead and hardware requirements. Therefore, these shortcomings greatly limit the application of standard machine learning approaches. This study aims to quickly and accurately distinguish bird species by their sounds in bird conservation work. For this reason, a novel concept-cognitive computing system (C3S) framework, namely, PyC3S, is proposed for bird sound classification in this paper. The proposed system uses feature fusion and concept-cognitive computing technology to construct a Python version of a dynamic bird song classification and recognition model on a dataset containing 50 species of birds. The experimental results show that the model achieves 92.77% accuracy, 92.26% precision, 92.25% recall, and a 92.41% F1-Score on the given 50 bird datasets, validating the effectiveness of our PyC3S compared to the state-of-the-art stream learning algorithms.

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