36,140 research outputs found
Signal Clustering with Class-independent Segmentation
Radar signals have been dramatically increasing in complexity, limiting the
source separation ability of traditional approaches. In this paper we propose a
Deep Learning-based clustering method, which encodes concurrent signals into
images, and, for the first time, tackles clustering with image segmentation.
Novel loss functions are introduced to optimize a Neural Network to separate
the input pulses into pure and non-fragmented clusters. Outperforming a variety
of baselines, the proposed approach is capable of clustering inputs directly
with a Neural Network, in an end-to-end fashion.Comment: Under Review for IEEE ICASSP 202
Deep clustering: Discriminative embeddings for segmentation and separation
We address the problem of acoustic source separation in a deep learning
framework we call "deep clustering." Rather than directly estimating signals or
masking functions, we train a deep network to produce spectrogram embeddings
that are discriminative for partition labels given in training data. Previous
deep network approaches provide great advantages in terms of learning power and
speed, but previously it has been unclear how to use them to separate signals
in a class-independent way. In contrast, spectral clustering approaches are
flexible with respect to the classes and number of items to be segmented, but
it has been unclear how to leverage the learning power and speed of deep
networks. To obtain the best of both worlds, we use an objective function that
to train embeddings that yield a low-rank approximation to an ideal pairwise
affinity matrix, in a class-independent way. This avoids the high cost of
spectral factorization and instead produces compact clusters that are amenable
to simple clustering methods. The segmentations are therefore implicitly
encoded in the embeddings, and can be "decoded" by clustering. Preliminary
experiments show that the proposed method can separate speech: when trained on
spectrogram features containing mixtures of two speakers, and tested on
mixtures of a held-out set of speakers, it can infer masking functions that
improve signal quality by around 6dB. We show that the model can generalize to
three-speaker mixtures despite training only on two-speaker mixtures. The
framework can be used without class labels, and therefore has the potential to
be trained on a diverse set of sound types, and to generalize to novel sources.
We hope that future work will lead to segmentation of arbitrary sounds, with
extensions to microphone array methods as well as image segmentation and other
domains.Comment: Originally submitted on June 5, 201
Latent Class Model with Application to Speaker Diarization
In this paper, we apply a latent class model (LCM) to the task of speaker
diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in
that it uses soft information and avoids premature hard decisions in its
iterations. In contrast to the VB method, which is based on a generative model,
LCM provides a framework allowing both generative and discriminative models.
The discriminative property is realized through the use of i-vector (Ivec),
probabilistic linear discriminative analysis (PLDA), and a support vector
machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid are introduced. In addition, three further improvements are
applied to enhance its performance. 1) Adding neighbor windows to extract more
speaker information for each short segment. 2) Using a hidden Markov model to
avoid frequent speaker change points. 3) Using an agglomerative hierarchical
cluster to do initialization and present hard and soft priors, in order to
overcome the problem of initial sensitivity. Experiments on the National
Institute of Standards and Technology Rich Transcription 2009 speaker
diarization database, under the condition of a single distant microphone, show
that the diarization error rate (DER) of the proposed methods has substantial
relative improvements compared with mainstream systems. Compared to the VB
method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments
on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial
conditions also show that the proposed LCM-Ivec-Hybrid system has the best
overall performance
Sparse Subspace Clustering: Algorithm, Theory, and Applications
In many real-world problems, we are dealing with collections of
high-dimensional data, such as images, videos, text and web documents, DNA
microarray data, and more. Often, high-dimensional data lie close to
low-dimensional structures corresponding to several classes or categories the
data belongs to. In this paper, we propose and study an algorithm, called
Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of
low-dimensional subspaces. The key idea is that, among infinitely many possible
representations of a data point in terms of other points, a sparse
representation corresponds to selecting a few points from the same subspace.
This motivates solving a sparse optimization program whose solution is used in
a spectral clustering framework to infer the clustering of data into subspaces.
Since solving the sparse optimization program is in general NP-hard, we
consider a convex relaxation and show that, under appropriate conditions on the
arrangement of subspaces and the distribution of data, the proposed
minimization program succeeds in recovering the desired sparse representations.
The proposed algorithm can be solved efficiently and can handle data points
near the intersections of subspaces. Another key advantage of the proposed
algorithm with respect to the state of the art is that it can deal with data
nuisances, such as noise, sparse outlying entries, and missing entries,
directly by incorporating the model of the data into the sparse optimization
program. We demonstrate the effectiveness of the proposed algorithm through
experiments on synthetic data as well as the two real-world problems of motion
segmentation and face clustering
Speaker segmentation and clustering
This survey focuses on two challenging speech processing topics, namely: speaker segmentation and speaker clustering. Speaker segmentation aims at finding speaker change points in an audio stream, whereas speaker clustering aims at grouping speech segments based on speaker characteristics. Model-based, metric-based, and hybrid speaker segmentation algorithms are reviewed. Concerning speaker clustering, deterministic and probabilistic algorithms are examined. A comparative assessment of the reviewed algorithms is undertaken, the algorithm advantages and disadvantages are indicated, insight to the algorithms is offered, and deductions as well as recommendations are given. Rich transcription and movie analysis are candidate applications that benefit from combined speaker segmentation and clustering. © 2007 Elsevier B.V. All rights reserved
Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification
There are a number of studies about extraction of bottleneck (BN) features
from deep neural networks (DNNs)trained to discriminate speakers, pass-phrases
and triphone states for improving the performance of text-dependent speaker
verification (TD-SV). However, a moderate success has been achieved. A recent
study [1] presented a time contrastive learning (TCL) concept to explore the
non-stationarity of brain signals for classification of brain states. Speech
signals have similar non-stationarity property, and TCL further has the
advantage of having no need for labeled data. We therefore present a TCL based
BN feature extraction method. The method uniformly partitions each speech
utterance in a training dataset into a predefined number of multi-frame
segments. Each segment in an utterance corresponds to one class, and class
labels are shared across utterances. DNNs are then trained to discriminate all
speech frames among the classes to exploit the temporal structure of speech. In
addition, we propose a segment-based unsupervised clustering algorithm to
re-assign class labels to the segments. TD-SV experiments were conducted on the
RedDots challenge database. The TCL-DNNs were trained using speech data of
fixed pass-phrases that were excluded from the TD-SV evaluation set, so the
learned features can be considered phrase-independent. We compare the
performance of the proposed TCL bottleneck (BN) feature with those of
short-time cepstral features and BN features extracted from DNNs discriminating
speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels
and boundaries are generated by three different automatic speech recognition
(ASR) systems. Experimental results show that the proposed TCL-BN outperforms
cepstral features and speaker+pass-phrase discriminant BN features, and its
performance is on par with those of ASR derived BN features. Moreover,....Comment: Copyright (c) 2019 IEEE. Personal use of this material is permitted.
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A novel neural network approach to cDNA microarray image segmentation
This is the post-print version of the Article. The official published version can be accessed from the link below. Copyright @ 2013 Elsevier.Microarray technology has become a great source of information for biologists to understand the workings of DNA which is one of the most complex codes in nature. Microarray images typically contain several thousands of small spots, each of which represents a different gene in the experiment. One of the key steps in extracting information from a microarray image is the segmentation whose aim is to identify which pixels within an image represent which gene. This task is greatly complicated by noise within the image and a wide degree of variation in the values of the pixels belonging to a typical spot. In the past there have been many methods proposed for the segmentation of microarray image. In this paper, a new method utilizing a series of artificial neural networks, which are based on multi-layer perceptron (MLP) and Kohonen networks, is proposed. The proposed method is applied to a set of real-world cDNA images. Quantitative comparisons between the proposed method and commercial software GenePix(®) are carried out in terms of the peak signal-to-noise ratio (PSNR). This method is shown to not only deliver results comparable and even superior to existing techniques but also have a faster run time.This work was funded in part by the National Natural Science Foundation of China under Grants 61174136 and 61104041, the Natural Science Foundation of Jiangsu Province of China under Grant BK2011598, the International Science and Technology Cooperation Project of China under Grant No. 2011DFA12910, the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. under Grant GR/S27658/01, the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany
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