Open Set Bioacoustic Signal Classification based on Class Anchor Clustering with Closed Set Unknown Bioacoustic Signals.

Recently, deep learning-driven studies have been introduced for bioacoustic signal classification. Most of them, however, have the limitation that the input of the classifier needs to match with a trained label which is known as closed set recognition (CSR). To this end, the classifier trained by CSR would not cover a real stream task since the input of the classifier has so many variations. To combat real-world tasks, open set recognition (OSR) has been developed. In OSR, randomly collected inputs are fed to the classifier and the classifier predicts target classes and Unknown class. However, this OSR has been spotlighted in the studies of computer vision and speech domains while the domain of bioacoustic signal is less developed. Especially, to our best knowledge, OSR for animal sound classification has not been studied. This paper proposes a novel method for open set bioacoustic signal classification based on Class Anchored Clustering (CAC) loss with closed set unknown bioacoustic signals. To use the closed set unknown signals for training, a total of n +1 classes are used by adding one additional Unknown class to n target classes, and n +1 cross-entropy loss is added to the CAC loss. To evaluate the proposed method, we build an animal sound dataset that includes 101 species of sounds and compare its performance with baseline methods. In the experiments, our proposed method shows higher performance than other baseline methods in the area under the receiver operating curve for detecting target class and unknown class, the classification accuracy of open set signals, and classification accuracy for target classes. As a result, the closed set class samples are well classified while the open set unknown class can be also recognized with high accuracy at the same time.

MRIFlow: Magnetic resonance image super-resolution based on normalizing flow and frequency prior.

Super-resolution (SR) is a computer vision task that involves recovering high-resolution (HR) images from low-resolution (LR) ones. While SR is applied to various disciplines, it is particularly important in the medical field which requires accurate diagnosis. L1 and L2 loss-based SR methods produce high values for the peak signal-to-noise ratio and structural similarity index measure but do not have high perceptual quality because SR methods are trained with the average of plausible HR predictions. In addition, SR is an ill-posed problem because only one LR image can be mapped to various HR images. This is crucial because poorly generated HR images can lead to misdiagnosis. In this paper, we propose MRIFlow, a novel method based on normalizing flow that transforms LR magnetic resonance (MR) images into HR MR images. MRIFlow contains frequency affine injectors to reflect frequency information. The frequency affine injector receives the output of a pre-trained LR encoder as the input and obtains frequency information from a wavelet transform based on ScatterNet. Using this method, its inverse operation is possible. MRIFlow has two versions based on the type of ScatterNet employed. In this paper, MRIFlow is compared with normalizing flow-based SR methods by using various MR image datasets such as IXI dataset, NYU fastMRI dataset, and LGG dataset and is demonstrated to produce better quantitative and qualitative results.