ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The ICASSP 2022 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit the website.
- Read more about SPARSE MODELING OF THE EARLY PART OF NOISY ROOM IMPULSE RESPONSES WITH SPARSE BAYESIAN LEARNING
- Log in to post comments
A model of a room impulse response (RIR) is useful for a wide range of applications. Typically, the early part of an RIR is sparse, and its sparse structure allows for accurate and simple modeling of the RIR. The existing L-p (0 < p ≤ 1)-norm-based methods suffer from the sensitivity to the user-selected regularization parameters or a high computational burden. In this work, we propose to reconstruct the sparse model for the early part of RIRs with sparse Bayesian learning (SBL).
slides.pdf
- Categories:
- Read more about Unrolling Particles: Unsupervised Learning of Sampling Distributions
- Log in to post comments
Particle filtering is used to compute nonlinear estimates of complex systems. It samples trajectories from a chosen distribution and computes the estimate as a weighted average of them. Easy-to-sample distributions often lead to degenerate samples where only one trajectory carries all the weight, negatively affecting the resulting performance of the estimate. While much research has been done on the design of appropriate sampling distributions that would lead to controlled degeneracy, in this paper our objective is to learn sampling distributions.
- Categories:
- Read more about CROSS-EPOCH LEARNING FOR WEAKLY SUPERVISED ANOMALY DETECTION IN SURVEILLANCE VIDEOS
- Log in to post comments
Weakly Supervised Anomaly Detection (WSAD) in surveillance videos is a complex task since usually only video-level annotations are available. Previous work treated it as a regression problem by giving different scores on normal and anomaly events. However, the widely used mini-batch training strategy may suffer from the data imbalance between these two types of events, which limits the model’s performance. In this work, a cross-epoch learning (XEL) strategy associated with a hard instance bank (HIB) is proposed to introduce additional information from previous training epochs.
- Categories:
- Read more about TRAINING STRATEGIES FOR AUTOMATIC SONG WRITING: A UNIFIED FRAMEWORK PERSPECTIVE
- Log in to post comments
Automatic song writing (ASW) typically involves four tasks: lyric-to-lyric generation, melody-to-melody generation, lyric-to-melody generation, and melody-to-lyric generation.
Previous works have mainly focused on individual tasks without considering the correlation between them, and thus a unified framework to solve all four tasks has not yet been explored.
- Categories:
- Read more about LERPS: LIGHTING ESTIMATION AND RELIGHTING FOR PHOTOMETRIC STEREO
- Log in to post comments
- Categories:
- Read more about EXPLORING DEEPER GRAPH CONVOLUTIONS FOR SEMI-SUPERVISED NODE CLASSIFICATION
- Log in to post comments
- Categories:
- Read more about RTSNET: DEEP LEARNING AIDED KALMAN SMOOTHING
- Log in to post comments
The smoothing task is the core of many signal processing applications. It deals with the recovery of a sequence of hidden state variables from a sequence of noisy observations in a one-shot manner. In this work we propose RTSNet, a highly efficient model-based and data-driven smoothing algorithm. RTSNet integrates dedicated trainable models into the flow of the classical Rauch-Tung-Striebel (RTS) smoother, and is able to outperform it when operating under model mismatch and non-linearities while retaining its efficiency and interpretability.
- Categories:
- Read more about Poster for ICASSP 2022 IVMSP-15.1
- Log in to post comments
Poster.pdf
- Categories:
- Read more about Presentation Slides for ICASSP 2022 IVMSP-15.1
- Log in to post comments
- Categories:
- Categories: