6 accepted papers
Existing audio search engines use one of two approaches: matching text-text or audio-audio pairs. In the former, text queries are matched to semantically similar words in an index of audio metadata to retrieve corresponding audio clips or segments, while in the latter, audio signals are directly use…
Conventional speech-enhancement techniques employ statistical signal-processing algorithms. They are computationally efficient and improve speech quality even under unknown noise conditions. For these reasons, they are preferred for deployment in unpredictable environments. One limitation of these a…
For a speech-enhancement algorithm, it is highly desirable to simultaneously improve perceptual quality and recognition rate. Thanks to computational costs and model complexities, it is challenging to train a model that effectively optimizes both metrics at the same time. In this paper, we propose a…
We propose an end-to-end model based on convolutional and recurrent neural networks for speech enhancement. Our model is purely data-driven and does not make any assumptions about the type or the stationarity of the noise. In contrast to existing methods that use multilayer perceptrons (MLPs), we em…
Fast and robust voice-activity detection is critical to efficiently process speech. While deep-learning based methods to detect voice have shown competitive accuracies, the best models in the literature incur over a 100 ms latency on commodity processors. Such delays are unacceptable for real-time s…
Although existing inertial motion-capture systems work reasonably well (≤10° error in Euler angles), their accuracy suffers when sensor positions change relative to the associated body segments (±60° mean error and 120° standard deviation). We attribute this performance degradation to undermined cal…