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We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information. We use a deep clustering approach which trains on multi-channel mixtures and learns to project spectrogram bins to source clusters that correlate with various spatial features. We show that using such a training process we can obtain separation performance that is as good as making use of ground truth separation information.

Confidences are integral to ASR systems, and applied to data selection, adaptation, ranking hypotheses, arbitration etc.Hybrid ASR system is inherently a match between pronunciations and AM+LM evidence but current confidence features lack pronunciation information. We develop pronunciation embeddings to represent and factorize acoustic score in relevant bases, and demonstrate 8-10% relative reduction in false alarm (FA) on large scale tasks. We generalize to standard NLP embeddings like Glove, and show 16% relative reduction in FA in combination with Glove.

Video transrating has become an essential task in streaming service providers that need to transmit and deliver different versions of the same content for a multitude of users that operate under different network conditions. As the transrating operation is comprised of a decoding and an encoding step in sequence, a huge computational cost is required in such large-scale services, especially when considering the use of complex state-of-the-art codecs, such as the High Efficiency Video Coding (HEVC).

Virtual Reality (VR) audio scenes may be composed of a very large number of audio elements, including dynamic audio objects, fixed audio channels and scene-based audio elements such as Higher Order Ambisonics (HOA).

The paper introduces a novel family of deterministic signals, the orthogonal periodic sequences (OPSs), for the identification of functional link polynomial (FLiP) filters. The novel sequences share many of the characteristics of the perfect periodic sequences (PPSs). As the PPSs, they allow the perfect identification of a FLiP filter on a finite time interval with the cross-correlation method. In contrast to the PPSs, OPSs can identify also non-orthogonal FLiP filters, as the Volterra filters.

Electrocardiogram (ECG) signal is a common and powerful tool to study heart function and diagnose several abnormal arrhythmias. While there have been remarkable improvements in cardiac arrhythmia classification methods, they still cannot offer acceptable performance in detecting different heart conditions, especially when dealing with imbalanced datasets. In this paper, we propose a solution to address this limitation of current classification approaches by developing an automatic heartbeat classification method using deep convolutional neural networks and sequence to sequence models.

Voice-controlled house-hold devices, like Amazon Echo or Google Home, face the problem of performing speech recognition of device- directed speech in the presence of interfering background speech, i.e., background noise and interfering speech from another person or media device in proximity need to be ignored. We propose two end-to-end models to tackle this problem with information extracted from the “anchored segment”.