Ensemble of convolutional neural networks to improve animal audio classification

Abstract

Recently, deep learning classifiers have proven even more robust in pattern recognition and classification than have texture analysis techniques. With the broad availability of relatively inexpensive Graphics Processing Units (GPUs), many researchers have begun applying deep learning techniques to visual representations of acoustic traces. Preselected or handcrafted descriptors, such as LBP, are not necessary for deep learners since they learn salient features during the training phase. Deep learners, moreover, are uniquely suited to handling visual representations of audio because many of the most famous deep classifiers, such as Convolutional Neural Networks (CNN), require matrices as their input. Humphrey and Bello [17, 18] were among the first to apply CNNs to audio images for music classification and, as a result, succeeded in redefining the state of the art in automatic chord detection and recognition. In the same year, Nakashika et al. [19] reported converting spectrograms to GCLM maps to train CNNs to performed music genre classification on the GTZAN dataset [20]. Later, Costa et al. [21] fused a CNN with the traditional pattern recognition framework of training SVMs on LBP features to classify the LMD dataset. These works exceeded traditional classification results on these genre datasets. Up to this point, most work in audio classification has applied the latest advances in machine learning to the problem of sound classification and recognition without modifying the classification process to make it singularly suitable for sound recognition. An early exception to the generic approach is found in the work of Sigtia and Dixon [22], who adjusted CNN parameters and structures in such a way as to reduce the time it took to train a set of audio images. Time reduction was accomplished by replacing

RETRACTED ARTICLE: Classification of audio signals using SVM-WOA in Hadoop map-reduce framework

“…Nanni et al [22] proposed a set of classifiers that work similarly using taxonomy and parameter settings on different animal audio datasets. To create this general-purpose ensemble, they experimented with a huge amount of finely tuned Convolutional Neural Networks (CNNs) already trained for a variety of audio classification tasks.…”

“…The presentation measures have been clarified as pursues. This setup is connected for RBFNN [22], PLMC [23], RARE [25], and the proposed MR-WOA-SVM calculations.…”

Towards Aircraft Maintenance Metaverse Using Speech Interactions with Virtual Objects in Mixed Reality

“…The common property they share is the architecture, which is based on convolutional neural networks (CNN) for features extraction and fully connected layers for classification [ 22 ]. The authors of [ 13 , 14 , 16 , 20 , 23 ] applied 1D convolutions with raw audio, whereas [ 13 , 15 , 17 , 18 , 24 , 25 ] used 2D convolutions with special audio features such as mel frequency cepstral coefficients (MFCCs) [ 26 ]. According to [ 13 ], both approaches are effective, but MFCCs are a great approximation to the human voice that help to discard background noise [ 26 ].…”

“…Since audio is a one-dimensional signal, their CNN architecture is also based on one-dimensional filters. Second, [ 13 , 15 , 17 , 18 , 24 , 25 , 31 ] applied various audio features such as mel frequency cepstral coefficients (MFCCs) [ 26 ] or Spectrograms with 2D convolutions. Since these features have a two-dimensional representation, CNN filters have the corresponding shape.…”

Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

“…This table is a representative reference for the animal speech classification accuracy of the existing deep learning technologies. Additionally, exhaustive tests have been performed on the fusion between an ensemble of handcrafted descriptors and an ensemble system based on a CNN [48], and the possibility was higher for the classification performance of the CNN-based ensemble system. Recent attempts have been made to analyze livestock voices in relation to animal welfare in livestock facilities.…”