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Found 10 papers, 2 papers with code
Data-Driven Tree Transforms and Metrics
Often the data is such that the observations do not reside on a regular grid, and the given order of the features is arbitrary and does not convey a notion of locality.
PCMC-T1: Free-breathing myocardial T1 mapping with Physically-Constrained Motion Correction
We compared PCMC-T1 to baseline deep-learning-based image registration approaches using a 5-fold experimental setup on a publicly available dataset of 210 patients.
Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection
In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences.
Diffusion Nets
In this paper, we propose a manifold learning algorithm based on deep learning to create an encoder, which maps a high-dimensional dataset and its low-dimensional embedding, and a decoder, which takes the embedded data back to the high-dimensional space.
Evaluation of Deep-Learning-Based Voice Activity Detectors and Room Impulse Response Models in Reverberant Environments
To mitigate this mismatch between training data and real data, we simulate an augmented training set that contains nearly five million utterances.
Deep Residual Echo Suppression with A Tunable Tradeoff Between Signal Distortion and Echo Suppression
In this paper, we propose a residual echo suppression method using a UNet neural network that directly maps the outputs of a linear acoustic echo canceler to the desired signal in the spectral domain.
Nonlinear Acoustic Echo Cancellation with Deep Learning
Second, the network is succeeded by a standard adaptive linear filter that constantly tracks the echo path between the loudspeaker output and the microphone.
Voice Activity Detection for Transient Noisy Environment Based on Diffusion Nets
A deep neural network, which is trained to separate speech from non-speech frames, is obtained by concatenating the decoder to the encoder, resembling the known Diffusion nets architecture.
Convolutional Sparse Coding Fast Approximation with Application to Seismic Reflectivity Estimation
In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks.
Challenges and Opportunities in Multi-device Speech Processing
We review current solutions and technical challenges for automatic speech recognition, keyword spotting, device arbitration, speech enhancement, and source localization in multidevice home environments to provide context for the INTERSPEECH 2022 special session, "Challenges and opportunities for signal processing and machine learning for multiple smart devices".
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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