Using spectrogram to detect North Atlantic right whale calls from audio recordings

Freitas, Guilherme Kamizake de; Costa, Yandre M. G.; Aguiar, Rafael de Lima

doi:10.1109/sccc.2016.7836034

“…Among the different works described in the literature, carried out on diverse application domains, using texture based features extraction approaches, we can mention: music genre classification [21], bird species classification [22], north atlantic right whale identification [23], identification of infants' cry motivation [24], speech recognition [25], acoustic scene classification [26], and COVID-19 identification using chest X-ray images [27], among others.…”

Section: Texture Descriptorsmentioning

confidence: 99%

Automatic Chronic Degenerative Diseases Identification Using Enteric Nervous System Images

Felipe¹,

Zanoni²,

Sehaber-Sierakowski³

et al. 2020

Preprint

0

View full text Add to dashboard Cite

Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcrafted features and deep learning based techniques, also known as non-handcrafted features. The handcrafted features were obtained from the textural content of the ECG images using texture descriptors, such as the Local Binary Pattern (LBP). Moreover, the representation learning techniques employed in the approach are based on different Convolutional Neural Network (CNN) architectures,

show abstract

“…This kind of recognition can help ships change their route to avoid a possible collision. Beyond this challenge, there are other research works on North Atlantic right whales' identification in the scientific literature [12][13][14]. In fact, the situation of this species is so critical, that another challenge was proposed in 2015 [15].…”

Section: Introductionmentioning

confidence: 99%

On the Importance of Passive Acoustic Monitoring Filters

Aguiar

¹

,

Maguolo

²

,

Nanni

³

et al. 2021

JMSE

View full text Add to dashboard Cite

Passive acoustic monitoring (PAM) is a noninvasive technique to supervise wildlife. Acoustic surveillance is preferable in some situations such as in the case of marine mammals, when the animals spend most of their time underwater, making it hard to obtain their images. Machine learning is very useful for PAM, for example to identify species based on audio recordings. However, some care should be taken to evaluate the capability of a system. We defined PAM filters as the creation of the experimental protocols according to the dates and locations of the recordings, aiming to avoid the use of the same individuals, noise patterns, and recording devices in both the training and test sets. It is important to remark that the filters proposed here were not intended to improve the accuracy rates. Indeed, these filters tended to make it harder to obtain better rates, but at the same time, they tended to provide more reliable results. In our experiments, a random division of a database presented accuracies much higher than accuracies obtained with protocols generated with PAM filters, which indicates that the classification system learned other components presented in the audio. Although we used the animal vocalizations, in our method, we converted the audio into spectrogram images, and after that, we described the images using the texture. These are well-known techniques for audio classification, and they have already been used for species classification. Furthermore, we performed statistical tests to demonstrate the significant difference between the accuracies generated with and without PAM filters with several well-known classifiers. The configuration of our experimental protocols and the database were made available online.

show abstract

Automatic chronic degenerative diseases identification using enteric nervous system images

Felipe

¹

,

Zanoni

²

,

Sehaber-Sierakowski

³

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcrafted features and deep learning-based techniques, also known as non-handcrafted features. The handcrafted features were obtained from the textural content of the ECG images using texture descriptors, such as the Local Binary Pattern (LBP). Moreover, the representation learning techniques employed in the approach are based on different Convolutional Neural Network (CNN) architectures, such as AlexNet and VGG16, with and without transfer learning. The complementarity between the handcrafted and non-handcrafted features was also evaluated with late fusion techniques. The datasets of EGC images used in the experiments, which are also contributions of this paper, are composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. The experimental results, supported by statistical analysis, show that the proposed approach can distinguish healthy cells from the sick ones with a recognition rate of 89.30% (Rheumatoid Arthritis), 98.45% (Cancer), and 95.13% (Diabetes Mellitus), being achieved by combining classifiers obtained on both feature scenarios.

show abstract

Using spectrogram to detect North Atlantic right whale calls from audio recordings

Cited by 8 publications

References 14 publications

Automatic Chronic Degenerative Diseases Identification Using Enteric Nervous System Images

Automatic Chronic Degenerative Diseases Identification Using Enteric Nervous System Images

On the Importance of Passive Acoustic Monitoring Filters

Automatic chronic degenerative diseases identification using enteric nervous system images

Contact Info

Product

Resources

About