70 research outputs found
Land subsidence over oilfields in the Yellow River Delta
Subsidence in river deltas is a complex process that has both natural and human causes. Increasing human activities like aquaculture and petroleum extraction are affecting the Yellow River delta, and one consequence is subsidence. The purpose of this study is to measure the surface displacements in the Yellow River delta region and to investigate the corresponding subsidence source. In this paper, the Stanford Method for Persistent Scatterers (StaMPS) package was employed to process Envisat ASAR images collected between 2007 and 2010. Consistent results between two descending tracks show subsidence with a mean rate up to 30 mm/yr in the radar line of sight direction in Gudao Town (oilfield), Gudong oilfield and Xianhe Town of the delta, each of which is within the delta, and also show that subsidence is not uniform across the delta. Field investigation shows a connection between areas of non-uniform subsidence and of petroleum extraction. In a 9 km2 area of the Gudao Oilfield, a poroelastic disk reservoir model is used to model the InSAR derived displacements. In general, good fits between InSAR observations and modeled displacements are seen. The subsidence observed in the vicinity of the oilfield is thus suggested to be caused by fluid extraction
A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data for Cancer Classification
Microarrays allow researchers to monitor the gene expression patterns for tens of thousands of genes across a wide range of cellular responses, phenotype and conditions. Selecting a small subset of discriminate genes from thousands of genes is important for accurate classification of diseases and phenotypes. Many methods have been proposed to find subsets of genes with maximum relevance and minimum redundancy, which can distinguish accurately between samples with different labels. To find the minimum subset of relevant genes is often referred as biomarker discovery. Two main approaches, filter and wrapper techniques, have been applied to biomarker discovery. In this paper, we conducted a comparative study of different biomarker discovery methods, including six filter methods and three wrapper methods. We then proposed a hybrid approach, FR-Wrapper, for biomarker discovery. The aim of this approach is to find an optimum balance between the precision of the biomarker discovery and the computation cost, by taking advantages of both filter methodās efficiency and wrapper methodās high accuracy. Our hybrid approach applies Fisherās ratio, a simple method easy to understand and implement, to filter out most of the irrelevant genes, then a wrapper method is employed to reduce the redundancy. The performance of FR-Wrapper approach is evaluated over four widely used microarray datasets. Analysis of experimental results reveals that the hybrid approach can achieve the goal of maximum relevance with minimum redundancy
Few-shot Class-incremental Audio Classification Using Stochastic Classifier
It is generally assumed that number of classes is fixed in current audio
classification methods, and the model can recognize pregiven classes only. When
new classes emerge, the model needs to be retrained with adequate samples of
all classes. If new classes continually emerge, these methods will not work
well and even infeasible. In this study, we propose a method for fewshot
class-incremental audio classification, which continually recognizes new
classes and remember old ones. The proposed model consists of an embedding
extractor and a stochastic classifier. The former is trained in base session
and frozen in incremental sessions, while the latter is incrementally expanded
in all sessions. Two datasets (NS-100 and LS-100) are built by choosing samples
from audio corpora of NSynth and LibriSpeech, respectively. Results show that
our method exceeds four baseline ones in average accuracy and performance
dropping rate. Code is at https://github.com/vinceasvp/meta-sc.Comment: 5 pages, 3 figures, 4 tables. Accepted for publication in INTERSPEECH
202
Speaker verification using attentive multi-scale convolutional recurrent network
In this paper, we propose a speaker verification method by an Attentive
Multi-scale Convolutional Recurrent Network (AMCRN). The proposed AMCRN can
acquire both local spatial information and global sequential information from
the input speech recordings. In the proposed method, logarithm Mel spectrum is
extracted from each speech recording and then fed to the proposed AMCRN for
learning speaker embedding. Afterwards, the learned speaker embedding is fed to
the back-end classifier (such as cosine similarity metric) for scoring in the
testing stage. The proposed method is compared with state-of-the-art methods
for speaker verification. Experimental data are three public datasets that are
selected from two large-scale speech corpora (VoxCeleb1 and VoxCeleb2).
Experimental results show that our method exceeds baseline methods in terms of
equal error rate and minimal detection cost function, and has advantages over
most of baseline methods in terms of computational complexity and memory
requirement. In addition, our method generalizes well across truncated speech
segments with different durations, and the speaker embedding learned by the
proposed AMCRN has stronger generalization ability across two back-end
classifiers.Comment: 21 pages, 6 figures, 8 tables. Accepted for publication in Applied
Soft Computin
Domestic Activity Clustering from Audio via Depthwise Separable Convolutional Autoencoder Network
Automatic estimation of domestic activities from audio can be used to solve
many problems, such as reducing the labor cost for nursing the elderly people.
This study focuses on solving the problem of domestic activity clustering from
audio. The target of domestic activity clustering is to cluster audio clips
which belong to the same category of domestic activity into one cluster in an
unsupervised way. In this paper, we propose a method of domestic activity
clustering using a depthwise separable convolutional autoencoder network. In
the proposed method, initial embeddings are learned by the depthwise separable
convolutional autoencoder, and a clustering-oriented loss is designed to
jointly optimize embedding refinement and cluster assignment. Different methods
are evaluated on a public dataset (a derivative of the SINS dataset) used in
the challenge on Detection and Classification of Acoustic Scenes and Events
(DCASE) in 2018. Our method obtains the normalized mutual information (NMI)
score of 54.46%, and the clustering accuracy (CA) score of 63.64%, and
outperforms state-of-the-art methods in terms of NMI and CA. In addition, both
computational complexity and memory requirement of our method is lower than
that of previous deep-model-based methods. Codes:
https://github.com/vinceasvp/domestic-activity-clustering-from-audioComment: 6 pages, 5 figures, 4 tables. Accepted by IEEE MMSP 202
Acoustic Scene Clustering Using Joint Optimization of Deep Embedding Learning and Clustering Iteration
Recent efforts have been made on acoustic scene classification in the audio
signal processing community. In contrast, few studies have been conducted on
acoustic scene clustering, which is a newly emerging problem. Acoustic scene
clustering aims at merging the audio recordings of the same class of acoustic
scene into a single cluster without using prior information and training
classifiers. In this study, we propose a method for acoustic scene clustering
that jointly optimizes the procedures of feature learning and clustering
iteration. In the proposed method, the learned feature is a deep embedding that
is extracted from a deep convolutional neural network (CNN), while the
clustering algorithm is the agglomerative hierarchical clustering (AHC). We
formulate a unified loss function for integrating and optimizing these two
procedures. Various features and methods are compared. The experimental results
demonstrate that the proposed method outperforms other unsupervised methods in
terms of the normalized mutual information and the clustering accuracy. In
addition, the deep embedding outperforms many state-of-the-art features.Comment: 9 pages, 6 figures, 11 tables. Accepted for publication in IEEE TM
Few-shot Class-incremental Audio Classification Using Adaptively-refined Prototypes
New classes of sounds constantly emerge with a few samples, making it
challenging for models to adapt to dynamic acoustic environments. This
challenge motivates us to address the new problem of few-shot class-incremental
audio classification. This study aims to enable a model to continuously
recognize new classes of sounds with a few training samples of new classes
while remembering the learned ones. To this end, we propose a method to
generate discriminative prototypes and use them to expand the model's
classifier for recognizing sounds of new and learned classes. The model is
first trained with a random episodic training strategy, and then its backbone
is used to generate the prototypes. A dynamic relation projection module
refines the prototypes to enhance their discriminability. Results on two
datasets (derived from the corpora of Nsynth and FSD-MIX-CLIPS) show that the
proposed method exceeds three state-of-the-art methods in average accuracy and
performance dropping rate.Comment: 5 pages,2 figures, Accepted by Interspeech 202
Domestic Activities Classification from Audio Recordings Using Multi-scale Dilated Depthwise Separable Convolutional Network
Domestic activities classification (DAC) from audio recordings aims at
classifying audio recordings into pre-defined categories of domestic
activities, which is an effective way for estimation of daily activities
performed in home environment. In this paper, we propose a method for DAC from
audio recordings using a multi-scale dilated depthwise separable convolutional
network (DSCN). The DSCN is a lightweight neural network with small size of
parameters and thus suitable to be deployed in portable terminals with limited
computing resources. To expand the receptive field with the same size of DSCN's
parameters, dilated convolution, instead of normal convolution, is used in the
DSCN for further improving the DSCN's performance. In addition, the embeddings
of various scales learned by the dilated DSCN are concatenated as a multi-scale
embedding for representing property differences among various classes of
domestic activities. Evaluated on a public dataset of the Task 5 of the 2018
challenge on Detection and Classification of Acoustic Scenes and Events
(DCASE-2018), the results show that: both dilated convolution and multi-scale
embedding contribute to the performance improvement of the proposed method; and
the proposed method outperforms the methods based on state-of-the-art
lightweight network in terms of classification accuracy.Comment: 5 pages, 2 figures, 4 tables. Accepted for publication in IEEE
MMSP202
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet
We present a work on low-complexity acoustic scene classification (ASC) with
multiple devices, namely the subtask A of Task 1 of the DCASE2021 challenge.
This subtask focuses on classifying audio samples of multiple devices with a
low-complexity model, where two main difficulties need to be overcome. First,
the audio samples are recorded by different devices, and there is mismatch of
recording devices in audio samples. We reduce the negative impact of the
mismatch of recording devices by using some effective strategies, including
data augmentation (e.g., mix-up, spectrum correction, pitch shift), usages of
multi-patch network structure and channel attention. Second, the model size
should be smaller than a threshold (e.g., 128 KB required by the DCASE2021
challenge). To meet this condition, we adopt a ResNet with both depthwise
separable convolution and channel attention as the backbone network, and
perform model compression. In summary, we propose a low-complexity ASC method
using data augmentation and a lightweight ResNet. Evaluated on the official
development and evaluation datasets, our method obtains classification accuracy
scores of 71.6% and 66.7%, respectively; and obtains Log-loss scores of 1.038
and 1.136, respectively. Our final model size is 110.3 KB which is smaller than
the maximum of 128 KB.Comment: 5 pages, 5 figures, 4 tables. Accepted for publication in the 16th
IEEE International Conference on Signal Processing (IEEE ICSP
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