In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
--------
Question: Distortion of the underlying speech is a common problem for single-channel speech enhancement algorithms, and hinders such methods from being used more extensively. A dictionary based speech enhancement method that emphasizes preserving the underlying speech is proposed. Spectral patches of clean speech are sampled and clustered to train a dictionary. Given a noisy speech spectral patch, the best matching dictionary entry is selected and used to estimate the noise power at each time-frequency bin. The noise estimation step is formulated as an outlier detection problem, where the noise at each bin is assumed present only if it is an outlier to the corresponding bin of the best matching dictionary entry. This framework assigns higher priority in removing spectral elements that strongly deviate from a typical spoken unit stored in the trained dictionary. Even without the aid of a separate noise model, this method can achieve significant noise reduction for various non-stationary noises, while effectively preserving the underlying speech in more challenging noisy environments.

Answer: SINGLE CHANNEL SPEECH ENHANCEMENT USING OUTLIER DETECTION


Question: Extracting per-frame features using convolutional neural networks for real-time processing of video data is currently mainly performed on powerful GPU-accelerated workstations and compute clusters. However, there are many applications such as smart surveillance cameras that require or would benefit from on-site processing. To this end, we propose and evaluate a novel algorithm for changebased evaluation of CNNs for video data recorded with a static camera setting, exploiting the spatio-temporal sparsity of pixel changes. We achieve an average speed-up of 8.6× over a cuDNN baseline on a realistic benchmark with a negligible accuracy loss of less than 0.1% and no retraining of the network. The resulting energy efficiency is 10× higher than per-frame evaluation and reaches an equivalent of 328GOp/s/W on the Tegra X1 platform.

Answer: CBinfer: Change-Based Inference for Convolutional Neural Networks on Video Data


Question: Deep Neural Network architectures with external memory components allow the model to perform inference and capture long term dependencies, by storing information explicitly. In this paper, we generalize Key-Value Memory Networks to a multimodal setting, introducing a novel keyaddressing mechanism to deal with sequence-to-sequence models. The advantages of the framework are demonstrated on the task of video captioning, i.e generating natural language descriptions for videos. Conditioning on the previous time-step attention distributions for the key-value memory slots, we introduce a temporal structure in the memory addressing schema. The proposed model naturally decomposes the problem of video captioning into vision and language segments, dealing with them as key-value pairs. More specifically, we learn a semantic embedding (v) corresponding to each frame (k) in the video, thereby creating (k, v) memory slots. This allows us to exploit the temporal dependencies at multiple hierarchies (in the recurrent keyaddressing; and in the language decoder). Exploiting this flexibility of the framework, we additionally capture spatial dependencies while mapping from the visual to semantic embedding. Extensive experiments on the Youtube2Text dataset demonstrate usefulness of recurrent key-addressing, while achieving competitive scores on BLEU@4, METEOR metrics against state-of-the-art models.

Answer:
Recurrent Memory Addressing for describing videos