515 research outputs found
TaylorAECNet: A Taylor Style Neural Network for Full-Band Echo Cancellation
This paper describes aecX team's entry to the ICASSP 2023 acoustic echo
cancellation (AEC) challenge. Our system consists of an adaptive filter and a
proposed full-band Taylor-style acoustic echo cancellation neural network
(TaylorAECNet) as a post-filter. Specifically, we leverage the recent advances
in Taylor expansion based decoupling-style interpretable speech enhancement and
explore its feasibility in the AEC task. Our TaylorAECNet based approach
achieves an overall mean opinion score (MOS) of 4.241, a word accuracy (WAcc)
ratio of 0.767, and ranks 5th in the non-personalized track (track 1)
Improved Bounds for Randomly Colouring Simple Hypergraphs
We study the problem of sampling almost uniform proper q-colourings in k-uniform simple hypergraphs with maximum degree ?. For any ? > 0, if k ? 20(1+?)/? and q ? 100?^({2+?}/{k-4/?-4}), the running time of our algorithm is O?(poly(? k)? n^1.01), where n is the number of vertices. Our result requires fewer colours than previous results for general hypergraphs (Jain, Pham, and Vuong, 2021; He, Sun, and Wu, 2021), and does not require ?(log n) colours unlike the work of Frieze and Anastos (2017)
Perfect sampling from spatial mixing
We introduce a new perfect sampling technique that can be applied to general Gibbs distributions and runs in linear time if the correlation decays faster than the neighborhood growth. In particular, in graphs with subexponential neighborhood growth like [Formula: see text] , our algorithm achieves linear running time as long as Gibbs sampling is rapidly mixing. As concrete applications, we obtain the currently best perfect samplers for colorings and for monomerâdimer models in such graphs
Swendsen-Wang dynamics for the ferromagnetic Ising model with external fields
We study the sampling problem for the ferromagnetic Ising model with
consistent external fields, and in particular, Swendsen-Wang dynamics on this
model. We introduce a new grand model unifying two closely related models: the
subgraph world and the random cluster model. Through this new viewpoint, we
show: (1) polynomial mixing time bounds for Swendsen-Wang dynamics and
(edge-flipping) Glauber dynamics of the random cluster model, generalising the
bounds and simplifying the proofs for the no-field case by Guo and Jerrum
(2018); (2) near linear mixing time for the two dynamics above if the maximum
degree is bounded and all fields are (consistent and) bounded away from .Comment: Major revision and new result
Rapid mixing from spectral independence beyond the Boolean domain
We extend the notion of spectral independence (introduced by Anari, Liu, and
Oveis Gharan [ALO20]) from the Boolean domain to general discrete domains. This
property characterises distributions with limited correlations, and implies
that the corresponding Glauber dynamics is rapidly mixing.
As a concrete application, we show that Glauber dynamics for sampling proper
-colourings mixes in polynomial-time for the family of triangle-free graphs
with maximum degree provided where
is the unique solution to
and is any constant. This is the first efficient algorithm for
sampling proper -colourings in this regime with possibly unbounded .
Our main tool of establishing spectral independence is the recursive coupling
by Goldberg, Martin, and Paterson [GMP05]
A simple polynomial-time approximation algorithm for the total variation distance between two product distributions
We give a simple polynomial-time approximation algorithm for the totalvariation distance between two product distributions
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