A Hybrid Approach with Multi-channel I-Vectors and Convolutional Neural Networks for Acoustic Scene Classification

In Acoustic Scene Classification (ASC) two major approaches have been followed . While one utilizes engineered features such as mel-frequency-cepstral-coefficients (MFCCs), the other uses learned features that are the outcome of an optimization algorithm. I-vectors are the result of a modeling technique that usually takes engineered features as input. It has been shown that standard MFCCs extracted from monaural audio signals lead to i-vectors that exhibit poor performance, especially on indoor acoustic scenes. At the same time, Convolutional Neural Networks (CNNs) are well known for their ability to learn features by optimizing their filters. They have been applied on ASC and have shown promising results. In this paper, we first propose a novel multi-channel i-vector extraction and scoring scheme for ASC, improving their performance on indoor and outdoor scenes. Second, we propose a CNN architecture that achieves promising ASC results. Further, we show that i-vectors and CNNs capture complementary information from acoustic scenes. Finally, we propose a hybrid system for ASC using multi-channel i-vectors and CNNs by utilizing a score fusion technique. Using our method, we participated in the ASC task of the DCASE-2016 challenge. Our hybrid approach achieved 1 st rank among 49 submissions, substantially improving the previous state of the art

Label Mask for Multi-Label Text Classification

One of the key problems in multi-label text classification is how to take advantage of the correlation among labels. However, it is very challenging to directly model the correlations among labels in a complex and unknown label space. In this paper, we propose a Label Mask multi-label text classification model (LM-MTC), which is inspired by the idea of cloze questions of language model. LM-MTC is able to capture implicit relationships among labels through the powerful ability of pre-train language models. On the basis, we assign a different token to each potential label, and randomly mask the token with a certain probability to build a label based Masked Language Model (MLM). We train the MTC and MLM together, further improving the generalization ability of the model. A large number of experiments on multiple datasets demonstrate the effectiveness of our method

Bi-factor Multidimensional Item Response Theory Modeling for Subscores Estimation, Reliability, and Classification

In recent years, there has been increasing interest in estimating and improving subscore reliability. In this study, the multidimensional item response theory (MIRT) and the bi-factor model were combined to estimate subscores, to obtain subscores reliability, and subscores classification. Both the compensatory and partially compensatory MIRT models are defined with bi-factor structure. A Monte Carlo study with 1,500 examinees was carried out for each model to examine two different test lengths (30 and 60 items) and five levels of item discrimination between primary and specific abilities (.50, .75, 1.0, 1.25, 1.50). The Markov Chain Monte Carlo (MCMC) with the Gibbs sampling method was applied to simultaneously estimate the expected a posteriori (EAP) subscores for primary and specific ability dimensions. Results were evaluated in light of estimation accuracy and fit, subscore reliability based on the Bayesian marginal reliability, and subscore classification based on subscore separation index. Despite a very minimum computing intensity for the MCMC simulation, both bi-factor compensatory and bi-factor partially compensatory models produced higher subscores reliability resulted from lower bias and reduction in the error variance of EAP subscores in all ability dimensions. These improved subscores reliability that also arrived at a higher discrimination level and for a longer test. This study found the bi-factor compensatory model to show better potential in classifying the magnitude of distinction between specific abilities and primary ability. Whereas, the bi-factor partially compensatory minimized the classification of subscores between the specific and primary abilities

Grounding deep models of visual data

Deep models are state-of-the-art for many computer vision tasks including object classification, action recognition, and captioning. As Artificial Intelligence systems that utilize deep models are becoming ubiquitous, it is also becoming crucial to explain why they make certain decisions: Grounding model decisions. In this thesis, we study: 1) Improving Model Classification. We show that by utilizing web action images along with videos in training for action recognition, significant performance boosts of convolutional models can be achieved. Without explicit grounding, labeled web action images tend to contain discriminative action poses, which highlight discriminative portions of a video’s temporal progression. 2) Spatial Grounding. We visualize spatial evidence of deep model predictions using a discriminative top-down attention mechanism, called Excitation Backprop. We show how such visualizations are equally informative for correct and incorrect model predictions, and highlight the shift of focus when different training strategies are adopted. 3) Spatial Grounding for Improving Model Classification at Training Time. We propose a guided dropout regularizer for deep networks based on the evidence of a network prediction. This approach penalizes neurons that are most relevant for model prediction. By dropping such high-saliency neurons, the network is forced to learn alternative paths in order to maintain loss minimization. We demonstrate better generalization ability, an increased utilization of network neurons, and a higher resilience to network compression. 4) Spatial Grounding for Improving Model Classification at Test Time. We propose Guided Zoom, an approach that utilizes spatial grounding to make more informed predictions at test time. Guided Zoom compares the evidence used to make a preliminary decision with the evidence of correctly classified training examples to ensure evidenceprediction consistency, otherwise refines the prediction. We demonstrate accuracy gains for fine-grained classification. 5) Spatiotemporal Grounding. We devise a formulation that simultaneously grounds evidence in space and time, in a single pass, using top-down saliency. We visualize the spatiotemporal cues that contribute to a deep recurrent neural network’s classification/captioning output. Based on these spatiotemporal cues, we are able to localize segments within a video that correspond with a specific action, or phrase from a caption, without explicitly optimizing/training for these tasks