6 citations · 7 across the 5 of their papers we have counts for
5 papers
Robust Audio Anomaly Detection
Wo Jae Lee, Karim Helwani, Arvindh Krishnaswamy +1
We propose an outlier robust multivariate time series model which can be used for detecting previously unseen anomalous sounds based on noisy training data. The presented approach…
Low-Complexity, Real-Time Joint Neural Echo Control and Speech Enhancement Based On PercepNet
Jean-Marc Valin, Srikanth Tenneti, Karim Helwani +2
Speech enhancement algorithms based on deep learning have greatly surpassed their traditional counterparts and are now being considered for the task of removing acoustic echo from…
Enhancing Audio Augmentation Methods with Consistency Learning
Turab Iqbal, Karim Helwani, Arvindh Krishnaswamy +1
Data augmentation is an inexpensive way to increase training data diversity and is commonly achieved via transformations of existing data. For tasks such as classification, there i…
A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech
Jean-Marc Valin, Umut Isik, Neerad Phansalkar +3
Over the past few years, speech enhancement methods based on deep learning have greatly surpassed traditional methods based on spectral subtraction and spectral estimation. Many of…
PoCoNet: Better Speech Enhancement with Frequency-Positional Embeddings, Semi-Supervised Conversational Data, and Biased Loss
Umut Isik, Ritwik Giri, Neerad Phansalkar +3
Neural network applications generally benefit from larger-sized models, but for current speech enhancement models, larger scale networks often suffer from decreased robustness to t…