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Wake word (WW) spotting is challenging in far-field due to the complexities and variations in acoustic conditions and the environmental interference in signal transmission. A suite of carefully designed and optimized audio front-end (AFE) algorithms help mitigate these challenges and provide better quality audio signals to the downstream modules such as WW spotter. Since the WW model is trained with the AFE-processed audio data, its performance is sensitive to AFE variations, such as gain changes. In addition, when deploying to new devices, the WW performance is not guaranteed because the AFE is unknown to the WW model. To address these issues, we propose a novel approach to use a new feature called $\Delta$LFBE to decouple the AFE gain variations from the WW model. We modified the neural network architectures to accommodate the delta computation, with the feature extraction module unchanged. We evaluate our WW models using data collected from real household settings and showed the models with the $\Delta$LFBE is robust to AFE gain changes. Specifically, when AFE gain changes up to $\pm$12dB, the baseline CNN model lost up to relative 19.0% in false alarm rate or 34.3% in false reject rate, while the model with $\Delta$LFBE demonstrates no performance loss., Comment: In Proc. Interspeech 2020

We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data. Compared to other research areas like computer vision, few-shot learning for audio recognition has been under-studied. We formulate few-shot AED problem and explore different ways of utilizing traditional supervised methods for this setting as well as a variety of meta-learning approaches, which are conventionally used to solve few-shot classification problem. Compared to supervised baselines, meta-learning models achieve superior performance, thus showing its effectiveness on generalization to new audio events. Our analysis including impact of initialization and domain discrepancy further validate the advantage of meta-learning approaches in few-shot AED., Comment: ICASSP 2020

Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems. Recently deep neural network significantly advances this field and reduces detection errors to a large scale. However how to efficiently execute deep models in AED has received much less attention. Meanwhile state-of-the-art AED models are based on large deep models, which are computational demanding and challenging to deploy on devices with constrained computational resources. In this paper, we present a simple yet effective compression approach which jointly leverages knowledge distillation and quantization to compress larger network (teacher model) into compact network (student model). Experimental results show proposed technique not only lowers error rate of original compact network by 15% through distillation but also further reduces its model size to a large extent (2% of teacher, 12% of full-precision student) through quantization., Interspeech 2019

This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset. Recent acoustic event detectors are based on large-scale neural networks, which are typically trained with huge amounts of labeled data. Labels for acoustic events are expensive to obtain, and relevant acoustic event audios can be limited, especially for rare events. In this paper we leverage an Internet-scale unlabeled dataset with potential domain shift to improve the detection of acoustic events. Based on the classic tri-training approach, our proposed method shows accuracy improvement over both the supervised training baseline, and semisupervised self-training set-up, in all pre-defined acoustic event detection tasks. As our approach relies on ensemble models, we further show the improvements can be distilled to a single model via knowledge distillation, with the resulting single student model maintaining high accuracy of teacher ensemble models., Comment: 5 pages

This paper proposes a Sub-band Convolutional Neural Network for spoken term classification. Convolutional neural networks (CNNs) have proven to be very effective in acoustic applications such as spoken term classification, keyword spotting, speaker identification, acoustic event detection, etc. Unlike applications in computer vision, the spatial invariance property of 2D convolutional kernels does not fit acoustic applications well since the meaning of a specific 2D kernel varies a lot along the feature axis in an input feature map. We propose a sub-band CNN architecture to apply different convolutional kernels on each feature sub-band, which makes the overall computation more efficient. Experimental results show that the computational efficiency brought by sub-band CNN is more beneficial for small-footprint models. Compared to a baseline full band CNN for spoken term classification on a publicly available Speech Commands dataset, the proposed sub-band CNN architecture reduces the computation by 39.7% on commands classification, and 49.3% on digits classification with accuracy maintained., Comment: Accepted by Interspeech 2019

System and method for an active learning system including a sensor obtains data from a scene including a set of images having objects. A memory to store active learning data including an object detector trained for detecting objects in images. A processor in communication with the memory, is configured to detect a semantic class and a location of at least one object in an image selected from the set of images using the object detector to produce a detection metric as a combination of an uncertainty of the object detector about the semantic class of the object in the image (classification) and an uncertainty of the object detector about the location of the object in the image (localization). Using an output interface or a display type device, in communication with the processor, to display the image for human labeling when the detection metric is above a threshold.

We propose a simple recurrent model for detecting rare sound events, when the time boundaries of events are available for training. Our model optimizes the combination of an utterance-level loss, which classifies whether an event occurs in an utterance, and a frame-level loss, which classifies whether each frame corresponds to the event when it does occur. The two losses make use of a shared vectorial representation the event, and are connected by an attention mechanism. We demonstrate our model on Task 2 of the DCASE 2017 challenge, and achieve competitive performance., Comment: Accepted by Interspeech 2018

Image hallucination and super-resolution have been studied for decades, and many approaches have been proposed to upsample low-resolution images using information from the images themselves, multiple example images, or large image databases. However, most of this work has focused exclusively on small magnification levels because the algorithms simply sharpen the blurry edges in the upsampled images - no actual new detail is typically reconstructed in the final result. In this paper, we present a patch-based algorithm for image hallucination which, for the first time, properly synthesizes novel high frequency detail. To do this, we pose the synthesis problem as a patch-based optimization which inserts coherent, high-frequency detail from contextually-similar images of the same physical scene/subject provided from either a personal image collection or a large online database. The resulting image is visually plausible and contains coherent high frequency information. We demonstrate the robustness of our algorithm by testing it on a large number of images and show that its performance is considerably superior to all state-of-the-art approaches, a result that is verified to be statistically significant through a randomized user study., 13 pages, 8 figures, submitted to IEEE Transactions on Multimedia, under revision

Filtering is widely used in image and video processing for various applications. Recently, the guided filter has been proposed and became one of the popular filtering methods. In this paper, to achieve the computation demand of guided filtering in full-HD video, a double integral image architecture for guided filter ASIC design is proposed. In addition, a reformation of the guided filter formula is proposed, which can prevent the error resulted from truncation in the fractional part and modify the regularization parameter e on user's demand. The hardware architecture of the guided image filter is then proposed and can be embedded in mobile devices to achieve real-time HD applications. To the best of our knowledge, this paper is also the first ASIC design for guided image filter. With a TSMC 90-nm cell library, the design can operate at 100 MHz and support for Full-HD (1920 × 1080) 30 frame/s with 92.9K gate counts and 3.2 KB on-chip memory. Moreover, for the hardware efficiency, our architecture is also the best compared to other previous works with bilateral filter.

Tennis Real Play (TRP) is an interactive tennis game system constructed with models extracted from videos of real matches. The key techniques proposed for TRP include player modeling and video-based player/court rendering. For player model creation, we propose the process for database normalization and the behavioral transition model of tennis players, which might be a good alternative for motion capture in the conventional video games. For player/court rendering, we propose the framework for rendering vivid game characters and providing the real-time ability. We can say that image-based rendering leads to a more interactive and realistic rendering. Experiments show that video games with vivid viewing effects and characteristic players can be generated from match videos without much user intervention. Because the player model can adequately record the ability and condition of a player in the real world, it can then be used to roughly predict the results of real tennis matches in the next days. The results of a user study reveal that subjects like the increased interaction, immersive experience, and enjoyment from playing TRP.

In this paper, the framework for a real-time view recommendation system is proposed. The proposed system comprises two parts: offline aesthetic modeling stage and efficient online aesthetic view finding process. A preference-aware aesthetic model is proposed to suggest views according to varied user-favorite photographic styles, where a bottom-up approach is developed to construct an aesthetic feature library with bag-of-aesthetics-preserving features instead of top-down methods that implement the heuristic guidelines (rule-specific features) listed in photography literatures, which is employed in previous works. A collection of scenic photos is used as the test set; however, the proposed method can be employed to other types of photo collection according to different application scenarios. The proposed model can cover both implicit and explicit aesthetic features and can adapt to users' preferences with a learning process. In the second part, the learned model is employed in a view finder to help the user to locate the most aesthetic view while taking a photograph. The experimental results show that the proposed features in the library (92.06% in accuracy) outperform the state-of-the-art rule-specific features (83.63% in accuracy) significantly in the photo aesthetic quality classification task, and the rule-specific features are also proved to be encompassed by the proposed features. Meanwhile, it is observed from experiments that the features extracted for contrast information are more effective than those for absolute information, which is consistent with the properties of human visual systems. Furthermore, the user studies for the view recommendation task confirm that the suggested views are consistent with users' preferences (81.25% agreements).

This paper proposes a new method for presenting sports videos. Tennis videos are used as an example for the implementation of a viewing program called as Tennis Video 2.0. For the methods in video analysis, background generation by considering the pixels in temporal and spatial distribution is proposed; foreground segmentation combining automatic trimap generation and matting model is proposed. To provide more functions in watching videos, the rendering flow of video contents and the semantic Scalability are proposed. With the new analysis and rendering tools, the presentation of sports videos has three properties-Structure, Interactivity, and Scalability. The experiments show that several broadcasting game videos are employed to evaluate the robustness and performance of the proposed system. For user study, 20 evaluators highly identify that Tennis Video 2.0 is a new presentation of sports videos and give people better viewing experience.

Spectral graph methods are widely employed in image segmentation, and they exhibit excellent performance. However, for high-resolution images, it is impractical to directly calculate the eigenvectors of the affinity matrix owing to the high computational requirements. The Nystrom method provides an efficient way to approximate the large-scale affinity matrix by low-rank approximation. In the machine learning field, previous studies have mainly focused on less data points with high dimensional features. To the best of our knowledge, this is the first study to discuss the performance of sampling methods for Nystrom approximation, in which we focus on the pixel-wise affinity matrix for a single image. In this paper, we propose a mean-shift segmentation-based Nystrom sampling technique for image analysis. The experimental results show that for images with simple compositions and backgrounds, k-means sampling performs better, whereas for images with more complicated compositions and backgrounds, the proposed method can perform better.

In this paper, an aesthetic modeling method for scenic photographs is proposed. A bottom-up approach is developed to construct an aesthetic library with bag-of-aesthetics preserving features instead of top-down methods that implement the heuristic guidelines (rule-specific features) listed in the photography literature, which is employed in previous works. The proposed method can cover both implicit and explicit aesthetic features with a learning process. The experimental results show that the proposed features in the library (92.06% in accuracy) outperform the state-of-the-art rule-specific features (83.63% in accuracy) significantly in the aesthetic quality assessment for scenic photos, and the rule-specific features are also proved to be encompassed by the proposed features. Meanwhile, it is observed from experiments that the features extracted for contrast information are more effective than those for absolute information, which is consistent with the properties of human visual systems.

Tennis Real Play (TRP) is an interactive tennis game system constructed with models extracted from videos of real matches. The key techniques proposed for TRP include player modeling and video-based player/court rendering. For player model creation, we propose a database normalization process and a behavioral transition model of tennis players, which might be a good alternative for motion capture in the conventional video games. For player/court rendering, we propose a framework for rendering vivid game characters and providing the real-time ability. We can say that image-based rendering leads to a more interactive and realistic rendering. Experiments show that video games with vivid viewing effects and characteristic players can be generated from match videos without much user intervention. Because the player model can adequately record the ability and condition of a player in the real world, it can then be used to roughly predict the results of real tennis matches in the next days. The results of a user study reveal that subjects like the increased interaction, immersive experience, and enjoyment from playing TRP.

Tennis Real Play (TRP) is an interactive tennis game system constructed with models extracted from real game videos. The key techniques proposed for TRP include player modeling and video-based player/court rendering. Experiments show that vivid rendering results can be generated.

Sprite is an image constructed from video clips and is also a medium for multimedia applications. An automatic sprite generation with foreground removal and super-resolution is proposed in this paper. To remove the foreground objects, each pixel-value on the sprite is iteratively updated by the value with maximum appearance probability on temporal and spatial distribution. By storing the half-pixel, superresolution sprite has less blurring-defect from source video. In the result, the generated sprite preserves the complete scenes of background and has higher image quality, and it can used to increase the visual quality in current sprite applications and also employed to facilitate video segmentation.