2 citations · 4 across the 6 of their papers we have counts for
6 papers
Dynamic Processing Neural Network Architecture For Hearing Loss Compensation
Szymon Drgas, Lars Bramsløw, Archontis Politis +2
This paper proposes neural networks for compensating sensorineural hearing loss. The aim of the hearing loss compensation task is to transform a speech signal to increase speech in…
Subjective Evaluation of Deep Neural Network Based Speech Enhancement Systems in Real-World Conditions
Gaurav Naithani, Kirsi Pietilä, Riitta Niemistö +3
Subjective evaluation results for two low-latency deep neural networks (DNN) are compared to a matured version of a traditional Wiener-filter based noise suppressor. The target use…
Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair
Shanshan Wang, Gaurav Naithani, Archontis Politis +1
Time-frequency masking or spectrum prediction computed via short symmetric windows are commonly used in low-latency deep neural network (DNN) based source separation. In this paper…
Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of Parameters
Niccoló Nicodemo, Gaurav Naithani, Konstantinos Drossos +2
Effective employment of deep neural networks (DNNs) in mobile devices and embedded systems is hampered by requirements for memory and computational power. This paper presents a non…
Low-Latency Deep Clustering For Speech Separation
Shanshan Wang, Gaurav Naithani, Tuomas Virtanen
This paper proposes a low algorithmic latency adaptation of the deep clustering approach to speaker-independent speech separation. It consists of three parts: a) the usage of long-…
Deep neural network based speech separation optimizing an objective estimator of intelligibility for low latency applications
Gaurav Naithani, Joonas Nikunen, Lars Bramsløw +1
Mean square error (MSE) has been the preferred choice as loss function in the current deep neural network (DNN) based speech separation techniques. In this paper, we propose a new…