Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

Noda, Juan J.; González, Carmelo Javier León; Sánchez-Rodríguez, David; Alonso-Hernández, Jesús B.

doi:10.3390/app9194097

Cited by 34 publications

(24 citation statements)

References 26 publications

(35 reference statements)

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“…Despite the better results obtained in this work using the fundamental frequency and power features compared with fundamental frequency alone, the more complex spectrogram and MFCC features provided the best performance for genus and sex classification. MFCCs are normally used in applications such as speech recognition [ 57 ] or music information retrieval [ 58 ], and although MFCCs are based on human perception of pitch, they have given good results in sound recognition studies with mosquitoes and other insects [ 34 , 49 , 59 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

González-Pérez

Faulhaber²,

Williams³

et al. 2022

Parasites Vectors

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Background Every year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosquitoes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxonomical identification. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classification of mosquitoes based on their flight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the field, which could lead to significant improvements in vector surveillance. Methods A novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classification of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that have a major impact on public health in many parts of the world. Five features were extracted from each recording to form balanced datasets and used for the training and evaluation of five different machine learning algorithms to achieve the best model for mosquito classification. Results The best accuracy results achieved using machine learning were: 94.2% for genus classification, 99.4% for sex classification of Aedes, and 100% for sex classification of Culex. The best algorithms and features were deep neural network with spectrogram for genus classification and gradient boosting with Mel Frequency Cepstrum Coefficients among others for sex classification of either genus. Conclusions To our knowledge, this is the first time that a sensor coupled to a standard mosquito suction trap has provided automatic classification of mosquito genus and sex with high accuracy using a large number of unique samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance and encourages future use of the sensor for remote, real-time characterization of mosquito populations. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

González-Pérez

Faulhaber²,

Williams³

et al. 2022

Parasites Vectors

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“…Next, these signals were transformed in spectrograms using the Mel-Frequency Cepstral Coefficients technique [22]. In literature, Mel-Frequency Cepstral Coefficients is described as a method that emulates the effective filtering properties of the human ear [39]. This approach has been used as a robust feature extraction method in the context of speaker identification, automatic speech recognition and Parkinson's Disease diagnosis [40,41].…”

Section: Signal's Transformationmentioning

confidence: 99%

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

et al. 2020

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During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

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“…These characteristics are often identified via a previous feature extraction stage on the raw signals. Two features are often targeted: lower frequency regions, identified via Mel frequency cepstral coefficients (MFCC) and higher frequency regions using the linear frequency cepstral coefficients (LFCC) [21]. These coefficients, by themselves or combined, can be later associated with each class and fed to a binary classifier, for instance support vector machine (SVM) for determining class separation [22].…”

Section: Sound and Audio Classification With Machine Learningmentioning

confidence: 99%

Bioacoustic Classification of Antillean Manatee Vocalization Spectrograms Using Deep Convolutional Neural Networks

et al. 2020

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We evaluated the potential of using convolutional neural networks in classifying spectrograms of Antillean manatee (Trichechus manatus manatus) vocalizations. Spectrograms using binary, linear and logarithmic amplitude formats were considered. Two deep convolutional neural networks (DCNN) architectures were tested: linear (fixed filter size) and pyramidal (incremental filter size). Six experiments were devised for testing the accuracy obtained for each spectrogram representation and architecture combination. Results show that binary spectrograms with both linear and pyramidal architectures with dropout provide a classification rate of 94–99% on the training and 92–98% on the testing set, respectively. The pyramidal network presents a shorter training and inference time. Results from the convolutional neural networks (CNN) are substantially better when compared with a signal processing fast Fourier transform (FFT)-based harmonic search approach in terms of accuracy and F1 Score. Taken together, these results prove the validity of using spectrograms and using DCNNs for manatee vocalization classification. These results can be used to improve future software and hardware implementations for the estimation of the manatee population in Panama.

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Acoustic Classification of Singing Insects Based on MFCC/LFCC Fusion

Cited by 34 publications

References 26 publications

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

Bioacoustic Classification of Antillean Manatee Vocalization Spectrograms Using Deep Convolutional Neural Networks

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