12 accepted papers
We introduce a real-time, multichannel speech enhancement algorithm which maintains the spatial cues of stereo recordings including two speech sources. Recognizing that each source has unique spatial information, our method utilizes a dual-path structure, ensuring the spatial cues remain unaffected…
In this study, we present an approach to train a single speech enhancement network that can perform both personalized and non-personalized speech enhancement. This is achieved by incorporating a frame-wise conditioning input that specifies the type of enhancement output. To improve the quality of th…
Singing voice separation aims to separate music into vocals and accompaniment components. One of the major constraints for the task is the limited amount of training data with separated vocals. Data augmentation techniques such as random source mixing have been shown to make better use of existing d…
Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech out-put. However, these models are tightly coupled with speech content, and produce unintended outputs in noisy conditions. Ba…
Recent progress in singing voice separation has primarily focused on supervised deep learning methods. However, the scarcity of ground-truth data with clean musical sources has been a problem for long. Given a limited set of labeled data, we present a method to leverage a large volume of unlabeled d…
Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary mask that is applied to the mixture in the time-frequency domain to produce the clean speech. Despite the great performanc…
Relative Impulse Responses (ReIRs) have several applications in speech enhancement, noise suppression and source localization for multi-channel speech processing in reverberant environments. Noise is usually assumed to be white Gaussian during the estimation of the ReIR between two microphones. We s…
Human listeners often have difficulties understanding speech in the presence of background noise in the real world. Recently, supervised learning based speech enhancement approaches have achieved substantial success, and show significant improvements over the conventional approaches. However, existi…
Acoustic Feedback Path in a digital hearing aid is not only affected by the user's head and ear, but also by different acoustic environments. But some of these effects are common for a specific style of hearing aid and individual ear, i.e., this part will be invariant to the different acoustic envir…
In this paper we address the problem of learning shared sparse representation across several tasks. Assuming that the tasks share a common set of relevant features across all tasks is highly restrictive. This acts as a motivation to look for a generalized model which will be able to learn any correl…
In this paper we present a novel Hierarchical Bayesian approach to estimate Relative Impulse Response (ReIR) using short, noisy and reverberant microphone recordings. The information contained in ReIRs between two microphones is useful for a wide range of multichannel speech processing applications…
In this paper, we propose two approaches to improve deep neural network (DNN) acoustic models for speech recognition in reverberant environments. Both methods utilize auxiliary information in training the DNN but differ in the type of information and the manner in which it is used. The first method…