This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlati...
Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde...
An important application for microphone arrays is to extract highquality output from a single wideband source in multi-source and adverse environments. Methods based on blind-sour...
It is widely known that the quality of confidence measure is critical for speech applications. In this paper, we present our recent work on improving word confidence scores by cal...
In this paper, we explore the use of a Gaussian posteriorgram based representation for unsupervised discovery of speech patterns. Compared with our previous work, the new approach...
Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transform...
We propose a new type of audio feature (HFCC-ENS) as well as an unsupervised method for detecting short sequences of spoken words (key-phrases) within long speech recordings. Our ...
In this paper, we propose a novel method to localize (or track) a foreground object and segment the foreground object from the surrounding background with occlusions for a moving ...
The development of real-time image quality assessment algorithms is an important direction on which little research has focused. This paper presents a design of real-time implemen...
Signal Sequence Labeling consists in predicting a sequence of labels given an observed sequence of samples. A naive way is to filter the signal in order to reduce the noise and t...