Environmental Sound Classification with Parallel Temporal-spectral Attention

Convolutional neural networks (CNN) are one of the best-performing neural network architectures for environmental sound classification (ESC). Recently, temporal attention mechanisms have been used in CNN to capture the useful information from the relevant time frames for audio classification, especially for weakly labelled data where the onset and offset times of the sound events are not applied. In these methods, however, the inherent spectral characteristics and variations are not explicitly exploited when obtaining the deep features. In this paper, we propose a novel parallel temporal-spectral attention mechanism for CNN to learn discriminative sound representations, which enhances the temporal and spectral features by capturing the importance of different time frames and frequency bands. Parallel branches are constructed to allow temporal attention and spectral attention to be applied respectively in order to mitigate interference from the segments without the presence of sound events. The experiments on three environmental sound classification (ESC) datasets and two acoustic scene classification (ASC) datasets show that our method improves the classification performance and also exhibits robustness to noise.Comment: submitted to INTERSPEECH202

Positive solutions for higher-order nonlinear fractional differential equation with integral boundary condition

In this paper, we study a kind of higher-order nonlinear fractional differential equation with integral boundary condition. The fractional differential operator here is the Caputo's fractional derivative. By means of fixed point theorems, the existence and multiplicity results of positive solutions are obtained. Furthermore, some examples given here illustrate that the results are almost sharp

Positive solutions of two-point boundary value problems of nonlinear fractional differential equation at resonance

This paper is concerned with a kind of nonlinear fractional differential boundary value problem at resonance with Caputo's fractional derivative. Our main approach is the recent Leggett-Williams norm-type theorem for coincidences due to O'Regan and Zima. The most interesting point is the acquisition of positive solutions for fractional differential boundary value problem at resonance. Moreover, an example is constructed to show that our result here is valid

SpecAugment++: A Hidden Space Data Augmentation Method for Acoustic Scene Classification

In this paper, we present SpecAugment++, a novel data augmentation method for deep neural networks based acoustic scene classification (ASC). Different from other popular data augmentation methods such as SpecAugment and mixup that only work on the input space, SpecAugment++ is applied to both the input space and the hidden space of the deep neural networks to enhance the input and the intermediate feature representations. For an intermediate hidden state, the augmentation techniques consist of masking blocks of frequency channels and masking blocks of time frames, which improve generalization by enabling a model to attend not only to the most discriminative parts of the feature, but also the entire parts. Apart from using zeros for masking, we also examine two approaches for masking based on the use of other samples within the minibatch, which helps introduce noises to the networks to make them more discriminative for classification. The experimental results on the DCASE 2018 Task1 dataset and DCASE 2019 Task1 dataset show that our proposed method can obtain 3.6% and 4.7% accuracy gains over a strong baseline without augmentation (i.e. CP-ResNet) respectively, and outperforms other previous data augmentation methods.Comment: Submitted to Interspeech 202

A Global-local Attention Framework for Weakly Labelled Audio Tagging

Weakly labelled audio tagging aims to predict the classes of sound events within an audio clip, where the onset and offset times of the sound events are not provided. Previous works have used the multiple instance learning (MIL) framework, and exploited the information of the whole audio clip by MIL pooling functions. However, the detailed information of sound events such as their durations may not be considered under this framework. To address this issue, we propose a novel two-stream framework for audio tagging by exploiting the global and local information of sound events. The global stream aims to analyze the whole audio clip in order to capture the local clips that need to be attended using a class-wise selection module. These clips are then fed to the local stream to exploit the detailed information for a better decision. Experimental results on the AudioSet show that our proposed method can significantly improve the performance of audio tagging under different baseline network architectures.Comment: Accepted to ICASSP202

A Two-student Learning Framework for Mixed Supervised Target Sound Detection

Target sound detection (TSD) aims to detect the target sound from mixture audio given the reference information. Previous work shows that a good detection performance relies on fully-annotated data. However, collecting fully-annotated data is labor-extensive. Therefore, we consider TSD with mixed supervision, which learns novel categories (target domain) using weak annotations with the help of full annotations of existing base categories (source domain). We propose a novel two-student learning framework, which contains two mutual helping student models ($\mathit{s\_student}$ and $\mathit{w\_student}$) that learn from fully- and weakly-annotated datasets, respectively. Specifically, we first propose a frame-level knowledge distillation strategy to transfer the class-agnostic knowledge from $\mathit{s\_student}$ to $\mathit{w\_student}$. After that, a pseudo supervised (PS) training is designed to transfer the knowledge from $\mathit{w\_student}$ to $\mathit{s\_student}$. Lastly, an adversarial training strategy is proposed, which aims to align the data distribution between source and target domains. To evaluate our method, we build three TSD datasets based on UrbanSound and Audioset. Experimental results show that our methods offer about 8\% improvement in event-based F score.Comment: submitted to interspeech202

Diagnostic accuracy of tumor necrosis factor-alpha, interferon-gamma, interlukine-10 and adenosine deaminase 2 in differential diagnosis between tuberculous pleural effusion and malignant pleural effusion

BACKGROUND: The current study was performed to investigate the potential biomarkers for the differential diagnosis of tuberculous pleural effusion (TPE) and malignant pleural effusions (MPE). METHODS: Among ninety patients (n = 90) involved in the study, 47 with tuberculous pleural effusion aged from 18 to 70 and 43 with secondary malignant pleural effusion aged from 34 to 78. We tested the pleural levels of TNF-α, IFN-γ and IL-10 as well as the enzyme activity of ADA(2), and then we compared the differential diagnostic efficiencies of those biochemical parameters with ADA between the two groups. RESULTS: Our results show that, the concentrations of pleural TNF-α (45.55 ± 15.85 ng/L), IFN-γ (114.97 ± 27.85 ng/L) as well as activities of ADA(2) (35.71 ± 10.00 U/L) and ADA (39.39 ± 10.60 U/L) in tuberculous group were significantly higher compared to malignant group. Furthermore, according to the ROC curve analysis the thresholds of TNF-α, IFN-γ, ADA(2) and ADA were found to be 30.3 ng/L, 103.65 ng/L, 29.45 U/L, and 39.00 U/L, respectively. TNF-α, IFN-γ and ADA(2) yielded better sensitivity, specificity, and accuracy of the diagnosis than ADA. Our investigation further revealed that the combinations of TNF-α and ADA(2) further increased the specificity and accuracy for the differential diagnosis. CONCLUSION: In conclusion, we found that TNF-α, IFN-γ, ADA and ADA(2) all increased in TPE. Combinations of the TNF-α and ADA(2) yielded the best specificity and accuracy for the differential diagnosis of TPE from MPE. Our investigation suggests that the applications of TNF-α together with ADA(2) may contribute to more efficient diagnosis strategies in the management of discrimination between tuberculous and malignant pleural effusions