165 research outputs found
Learning with Out-of-Distribution Data for Audio Classification
In supervised machine learning, the assumption that training data is labelled
correctly is not always satisfied. In this paper, we investigate an instance of
labelling error for classification tasks in which the dataset is corrupted with
out-of-distribution (OOD) instances: data that does not belong to any of the
target classes, but is labelled as such. We show that detecting and relabelling
certain OOD instances, rather than discarding them, can have a positive effect
on learning. The proposed method uses an auxiliary classifier, trained on data
that is known to be in-distribution, for detection and relabelling. The amount
of data required for this is shown to be small. Experiments are carried out on
the FSDnoisy18k audio dataset, where OOD instances are very prevalent. The
proposed method is shown to improve the performance of convolutional neural
networks by a significant margin. Comparisons with other noise-robust
techniques are similarly encouraging.Comment: Paper accepted for 45th International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2020
THE CORRELATIONS BETWEEN DYNAMIC WALKING STABILITY AND PERCEPTION-MOTOR ABILITIES OF HUMANS
External perturbations can challenge a person’s walking stability, and people will autonomously make a series of responses to regain the balance of walking, which includes two periods: perturbation-perception (reaction time, RT) and posture-adjustment (motion time, MT). The purpose of this paper was to investigate the correlations between the dynamic walking stability and perception-motor abilities. During the 30 level walking trials preformed by sixteen healthy participants, perturbations were applied at random. The fall probability (FP) during the walking with perturbations was calculated to evaluate the dynamic walking stability of each participant. Furthermore, the ground reaction force (GRF) of each participant during walking with perturbations was recorded and analyzed. The experimental results show that the RT had a significant positive-correlation with FP, while MT had no correlation with FP
Polyphonic Sound Event Detection and Localization using a Two-Stage Strategy
Sound event detection (SED) and localization refer to recognizing sound events and estimating their spatial and temporal locations. Using neural networks has become the prevailing method for SED. In the area of sound localization, which is usually performed by estimating the direction of arrival (DOA), learning-based methods have recently been developed. In this paper, it is experimentally shown that the trained SED model is able to contribute to the direction of arrival estimation (DOAE). However, joint training of SED and DOAE degrades the performance of both. Based on these results, a two-stage polyphonic sound event detection and localization method is proposed. The method learns SED first, after which the learned feature layers are transferred for DOAE. It then uses the SED ground truth as a mask to train DOAE. The proposed method is evaluated on the DCASE 2019 Task 3 dataset, which contains different overlapping sound events in different environments. Experimental results show that the proposed method is able to improve the performance of both SED and DOAE, and also performs significantly better than the baseline method.303
Event-Independent Network for Polyphonic Sound Event Localization and Detection
Polyphonic sound event localization and detection is not only detecting what
sound events are happening but localizing corresponding sound sources. This
series of tasks was first introduced in DCASE 2019 Task 3. In 2020, the sound
event localization and detection task introduces additional challenges in
moving sound sources and overlapping-event cases, which include two events of
the same type with two different direction-of-arrival (DoA) angles. In this
paper, a novel event-independent network for polyphonic sound event
localization and detection is proposed. Unlike the two-stage method we proposed
in DCASE 2019 Task 3, this new network is fully end-to-end. Inputs to the
network are first-order Ambisonics (FOA) time-domain signals, which are then
fed into a 1-D convolutional layer to extract acoustic features. The network is
then split into two parallel branches. The first branch is for sound event
detection (SED), and the second branch is for DoA estimation. There are three
types of predictions from the network, SED predictions, DoA predictions, and
event activity detection (EAD) predictions that are used to combine the SED and
DoA features for on-set and off-set estimation. All of these predictions have
the format of two tracks indicating that there are at most two overlapping
events. Within each track, there could be at most one event happening. This
architecture introduces a problem of track permutation. To address this
problem, a frame-level permutation invariant training method is used.
Experimental results show that the proposed method can detect polyphonic sound
events and their corresponding DoAs. Its performance on the Task 3 dataset is
greatly increased as compared with that of the baseline method.Comment: conferenc
An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection
Polyphonic sound event localization and detection (SELD), which jointly
performs sound event detection (SED) and direction-of-arrival (DoA) estimation,
detects the type and occurrence time of sound events as well as their
corresponding DoA angles simultaneously. We study the SELD task from a
multi-task learning perspective. Two open problems are addressed in this paper.
Firstly, to detect overlapping sound events of the same type but with different
DoAs, we propose to use a trackwise output format and solve the accompanying
track permutation problem with permutation-invariant training. Multi-head
self-attention is further used to separate tracks. Secondly, a previous finding
is that, by using hard parameter-sharing, SELD suffers from a performance loss
compared with learning the subtasks separately. This is solved by a soft
parameter-sharing scheme. We term the proposed method as Event Independent
Network V2 (EINV2), which is an improved version of our previously-proposed
method and an end-to-end network for SELD. We show that our proposed EINV2 for
joint SED and DoA estimation outperforms previous methods by a large margin,
and has comparable performance to state-of-the-art ensemble models.Comment: 5 pages, 2021 IEEE International Conference on Acoustics, Speech and
Signal Processin
Incident fluence dependent morphologies, photoluminescence and optical oxygen sensing properties of ZnO nanorods grown by pulsed laser deposition
The photoluminescence-based O2 sensing properties of PLD ZnO nanorods at elevated temperatures depend sensitively on their (incident fluence determined) morphology and defect density.</p
META-SELD: Meta-Learning for Fast Adaptation to the new environment in Sound Event Localization and Detection
For learning-based sound event localization and detection (SELD) methods,
different acoustic environments in the training and test sets may result in
large performance differences in the validation and evaluation stages.
Different environments, such as different sizes of rooms, different
reverberation times, and different background noise, may be reasons for a
learning-based system to fail. On the other hand, acquiring annotated spatial
sound event samples, which include onset and offset time stamps, class types of
sound events, and direction-of-arrival (DOA) of sound sources is very
expensive. In addition, deploying a SELD system in a new environment often
poses challenges due to time-consuming training and fine-tuning processes. To
address these issues, we propose Meta-SELD, which applies meta-learning methods
to achieve fast adaptation to new environments. More specifically, based on
Model Agnostic Meta-Learning (MAML), the proposed Meta-SELD aims to find good
meta-initialized parameters to adapt to new environments with only a small
number of samples and parameter updating iterations. We can then quickly adapt
the meta-trained SELD model to unseen environments. Our experiments compare
fine-tuning methods from pre-trained SELD models with our Meta-SELD on the
Sony-TAU Realistic Spatial Soundscapes 2023 (STARSSS23) dataset. The evaluation
results demonstrate the effectiveness of Meta-SELD when adapting to new
environments.Comment: Submitted to DCASE 2023 Worksho
Epidemic characteristics, high-risk townships and space-time clusters of human brucellosis in Shanxi Province of China, 2005–2014
BACKGROUND: Brucellosis, one of the world's most important zoonosis, has been re-emerging in China. Shanxi Province, located in northern China, where husbandry development has been accelerated in recent years, has a rather high incidence of human brucellosis but drew little attention from the researchers. This study aimed to describe the changing epidemiology of human brucellosis in Shanxi Province from 2005 to 2014 and explore high-risk towns and space-time clusters for elucidating the necessity of decentralizing disease control resource to township level in epidemic regions, particularly in hotspot areas.METHODS: We extracted data from the Chinese National Notifiable Infectious Disease Reporting System to describe the incidence and spatiotemporal distribution of human brucellosis in Shanxi Province. Geographic information system was used to identify townships at high risk for the disease. Space-Time Scan Statistic was applied to detect the space-time clusters of human brucellosis during the past decade.RESULTS: From 2005 to 2014, a total of 50,002 cases of human brucellosis were recorded in Shanxi, with a male-to-female ratio of 3.9:1. The reported incidence rate increased dramatically from 7.0/100,000 in 2005 to 23.5/100,000 in 2014, with an average annual increase of 14.5%. There were still 33.8% cases delaying diagnosis in 2014. The proportion of the affected towns increased from 31.5% in 2005 to 82.5% in 2014. High-risk towns spread from the north to the center and then south of Shanxi Province, which were basins and adjacent highlands suitable for livestock cultivation. During the past decade, there were 55 space-time clusters of human brucellosis detected in high risk towns; the clusters could happen in any season. Some clusters' location maintained stable over time.CONCLUSIONS: During the last decade, Shanxi province's human brucellosis epidemic had been aggravated and high-risk areas concentrated in some towns located in basins and adjacent highlands. Space-time clusters existed and some located steadily over time. Quite a few cases still missed timely diagnosis. Greater resources should be allocated and decentralized to mitigate the momentum of rise and improve the accessibility of prompt diagnosis treatment in the high-risk townships
Evaluation of the performance of a dengue outbreak detection tool for China
An outbreak detection and response system, using time series moving percentile method based on historical data, in China has been used for identifying dengue fever outbreaks since 2008. For dengue fever outbreaks reported from 2009 to 2012, this system achieved a sensitivity of 100%, a specificity of 99.8% and a median time to detection of 3 days, which indicated that the system was a useful decision tool for dengue fever control and risk-management programs in China.This work was supported by the grants from Research and Promotion of Key Technology on Health Emergency Preparation and Dispositions (201202006), the National Key Science and Technology Project on Infectious Disease Surveillance Technique Platform of China (2012ZX10004-201) and Development of Early Warning Systems for Dengue Fever Based on Socio-ecological Factors (NHMRC APP1002608)
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