Paper introduces ToyADMOS dataset for detecting anomalous machine sounds.
problem Lack of large-scale datasets for ADMOS anomaly detection.
method Collected anomalous sounds of miniature machines by deliberate damage.
result Released dataset includes over 180 hours of normal and 4,000 anomalous sounds.
This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS is to detect unknown anomalous sound without training data of anomalous sound. Use of an AE as a normal model is a state-of-the-art techniqu…
Paper discusses ASD challenge for machine condition monitoring.
problem Detecting unknown anomalous sounds without labeled data.
method Design and evaluation of a large-scale ASD dataset, novel approaches.
result Several novel approaches developed, evaluation results analyzed.
DCASE 2021 ASD task tackles domain-shifted anomalous sound detection.
problem Detecting unknown anomalous sounds under domain-shifted conditions.
method Ensemble of outlier exposure and inlier modeling detectors, feature learning from machine identification.
result Two types of remarkable approaches were adopted by top teams.
A new method detects anomalous sounds using self-supervised learning.
problem Detecting anomalous sounds in noisy data.
method Flow-based self-supervised density estimation using Normalizing Flows.
result The method improves anomaly detection performance significantly.
New dataset for industrial machine sounds to aid maintenance.
problem Lack of public datasets for industrial machine sounds.
method Recorded normal and anomalous sounds of industrial machines.
result Assists in automated facility maintenance development.
DCASE 2022 Task 2 tackles domain shifts in ASD for machine condition monitoring.
problem Domain shifts change acoustic characteristics, affecting ASD performance.
method Domain generalization techniques to detect anomalies across unknown domains.
result Two types of domain generalization techniques were identified and analyzed.
New dataset for industrial machine malfunction detection with domain shifts.
problem Challenges in detecting anomalies due to domain shifts in industrial sounds.
method Created a dataset with domain shifts for five types of industrial machines.
result Significant performance differences between source and target domains.
Special issue on understanding physical processes from unusual diffusion patterns.
problem Understanding physical processes from anomalous diffusion data.
method Not explicitly described in the abstract, but likely involves analysis of data from the Anomalous Diffusion Challenge.
result Not explicitly stated, but likely includes analysis of physical processes from anomalous diffusion data.
New rules control false discoveries in online anomaly detection for time series data.
problem Controlling false discoveries in anomaly detection for time series data.
method Novel online false discovery rate control (FDRC) rules for time series anomaly detection.
result Ensures high power in detecting anomalies even when the alternative is rare and test statistics are serially dependent.
Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Additionally, recent dete…
Paper investigates preserving anomalous subgroups in anonymized datasets.
problem Preserving anomalous subgroups in machine learning transformed data.
method Trained a binary classifier to discover anomalous subgroups, then used variational autoencoder (VAE) to anonymize data.
result Synthesized datasets preserved high subgroup differentiation as in original data.
This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing an anomalous input. Detecting anomalies is a critical component for multiple machine learning problems including detecting adversarial noise. More br…
Paper proposes a new dataset for group anomaly detection in physics.
problem Group anomaly detection in physics.
method Development of a synthetic benchmark dataset and comparison of anomaly detection techniques.
result Demonstrated performance of anomaly detection techniques on the new dataset.
Deep learning methods have achieved high performance in sound recognition tasks. Deciding how to feed the training data is important for further performance improvement. We propose a novel learning method for deep sound recognition: Between-Class learning (BC learning). Our strategy is to learn a discriminative feature…
Paper presents an audiovisual model to recognize sounds from weakly labeled video data.
problem Sound recognition from weakly labeled video data.
method Audiovisual fusion model with attention mechanism.
result The model achieves a mean Average Precision (mAP) of 46.16 on AudioSet, outperforming state-of-the-art models.
RCGAN improves anomaly detection by better recognizing anomalous samples.
problem Previous methods fail to correctly detect anomalous data.
method RCGAN uses adversarial training with a new loss function and penalty distribution.
result RCGAN outperforms state-of-the-art methods on various datasets.
Bi-Mamba model predicts diffusion coefficients and exponents from short data.
problem Characterizing anomalous diffusion in complex systems.
method Bidirectional state-space deep learning architecture.
result Efficient inference of diffusion coefficient and exponent from short trajectories.
Paper proposes active learning for sound event detection with reduced annotation effort.
problem Reducing annotation effort for sound event detection.
method Change point detection for candidate selection, mismatch-first farthest-traversal for selection, training with context recordings.
result The proposed system achieves similar performance to full annotation with only 2% of data, reducing annotation effort.
Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound ha…
We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups (clusters) of anomalies; i.e. sets of points which collectively exhibit abnormal patterns. I…
Paper introduces IAD for detecting anomalous VMMs in cloud without VMM access.
problem Anomalous VMMs in cloud-based environments without direct access.
method IAD: Indirect Anomalous VMMs Detection algorithm using VM resource utilization data.
result IAD algorithm outperforms other methods with an average F1-score of 83.7%.
New method explains anomalies in multivariate time series data.
problem Understanding and explaining anomalies in multivariate time series data.
method Counterfactual reasoning applied to MDI-detected anomalous intervals.
result Our method accurately identifies and explains anomalies in various extreme events.
Paper introduces MoM-KDE for robust density estimation robust to anomalous data.
problem Density estimation robustness to anomalous data.
method Combines Kernel Density Estimation and Median-of-Means principle.
result Achieves competitive results with lower computational complexity compared to other robust estimators.
ELM combines machine learning and feature engineering for anomalous diffusion detection.
problem Quantitative characterization of anomalous diffusion from single trajectories.
method Extreme Learning Machine (ELM) combined with feature engineering.
result ELM achieves satisfactory performance in AnDi challenge tasks.
Proposes a novel model-agnostic training procedure for anomaly detection incorporating known anomalies.
problem Challenges of anomaly detection, especially when only a few anomalous samples are available.
method Reformulates one-class classification as a binary classification problem, using pseudo-anomalous samples drawn from a normalizing flow model.
result Demonstrates comparable or superior performance on tasks with variable amounts of known anomalies.
Paper improves sound event detection using semi-supervised learning.
problem Weakly labeled sound event detection in polyphonic audio clips.
method Combines tri-training and adversarial learning for semi-supervised learning.
result Significant performance improvement over baseline model.
An adversarial detector identifies anomalous sequences in sequential data.
problem Detecting anomalous sequences in one-class settings with limited data.
method Solves a minimax problem to find an optimal detector against the worst-case sequences from a generator, using marked point process model.
result Demonstrated good performance on simulations and real credit card fraud datasets.
A wide variety of application domains are concerned with data consisting of entities and their relationships or connections, formally represented as graphs. Within these diverse application areas, a common problem of interest is the detection of a subset of entities whose connectivity is anomalous with respect to the r…
The state of a stochastic process evolving over a time t is typically assumed to lie on a normal distribution whose width scales like t1/2. However, processes where the probability distribution is not normal and the scaling exponent differs from 21 are known. The search for possible origins of such "a…
Paper uses DBSCAN variation to detect ship anomalies.
problem Detecting anomalous ship behavior.
method Variation of DBSCAN algorithm applied to AIS data.
result Alternative anomaly metric is more statistically informative.
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but inferring labels from this metadata introduces errors due to unreliable inputs, and limitations in the mapping. There is, however, little r…
Paper detects anomalous edges in social networks using edge exchangeability.
problem Detecting anomalous edges in directed social networks.
method Exploits edge exchangeability and uses conformal prediction theory.
result Proposed anomaly detector has a guaranteed upper bound for false positives.
Anomaly detection is not an easy problem since distribution of anomalous samples is unknown a priori. We explore a novel method that gives a trade-off possibility between one-class and two-class approaches, and leads to a better performance on anomaly detection problems with small or non-representative anomalous sample…
Paper detects adversarial attacks in sound classification models.
problem Adversarial attacks threaten data-driven models, especially in sound classification.
method Detects adversarial subspaces in unitary vector domain using chordal distance and generalized Schur decomposition.
result Regularized logistic regression detector outperforms other approaches on benchmark datasets.
This paper improves universal sound separation using sound classification.
problem Separating acoustic sources from an open domain, regardless of their class.
method Utilizing semantic embeddings from a sound classifier to condition a separation network.
result Classifier embeddings provide nearly one dB of SNR gain, and iterative models achieve significant performance.
Continual learning consists in incrementally training a model on a sequence of datasets and testing on the union of all datasets. In this paper, we examine continual learning for the problem of sound classification, in which we wish to refine already trained models to learn new sound classes. In practice one does not w…
Paper introduces kernel methods for detecting anomalous changes in remote sensing imagery.
problem Detecting anomalous changes in remote sensing imagery.
method Nonlinear extension of Gaussian and elliptically contoured distribution algorithms using reproducing kernel Hilbert space.
result Improved detection accuracy and reduced false-alarm rates compared to linear formulations.
A nonparametric anomalous hypothesis testing problem is investigated, in which there are totally n sequences with s anomalous sequences to be detected. Each typical sequence contains m independent and identically distributed (i.i.d.) samples drawn from a distribution p, whereas each anomalous sequence contains m i.i.d.…
The paper explores anomalous subvarieties in hyperbolic 3-manifolds and their geometric implications.
problem Understanding anomalous subvarieties in holonomy varieties of hyperbolic 3-manifolds.
method Analyzing the structure of anomalous subvarieties and their relation to geometric properties of hyperbolic 3-manifolds.
result Maximal anomalous subvarieties of holonomy varieties correspond to specific geometric configurations of cusps in hyperbolic 3-manifolds.
Deep learning models generate music with arbitrary control strategies.
problem Lack of efficient methods for generating music with arbitrary control.
method Deep generative models learn to navigate arbitrary sound spaces.
result Deep learning enables high-quality, arbitrary sound synthesis.
FSD50K provides an open dataset of over 51k audio clips for sound event recognition.
problem Small and domain-specific sound event recognition datasets.
method Creation of an open dataset with over 51k audio clips manually labeled using 200 classes.
result FSD50K is a new open benchmark for sound event recognition research.
The nonparametric problem of detecting existence of an anomalous interval over a one dimensional line network is studied. Nodes corresponding to an anomalous interval (if exists) receive samples generated by a distribution q, which is different from the distribution p that generates samples for other nodes. If anomalou…
Model classifies environment sounds using multiple feature channels and attention mechanisms.
problem Environment sound classification task.
method Multiple feature channels (MFCC, GFCC, CQT, Chromagram) and attention mechanism in a deep CNN.
result Achieves state-of-the-art performance on three benchmark datasets.
Nonparametric detection of existence of an anomalous structure over a network is investigated. Nodes corresponding to the anomalous structure (if one exists) receive samples generated by a distribution q, which is different from a distribution p generating samples for other nodes. If an anomalous structure does not exi…
Neural network synthesizes percussive sounds with adjustable timbral features.
problem Control over high-level timbral characteristics of percussive sounds.
method Feedforward convolutional neural network mapping input parameters to waveform.
result Changing input parameters produces a waveform congruent with desired characteristics.
Simple methods boost sound event classifier accuracy by 2.5%.
problem Label noise in sound event classification datasets.
method Label smoothing regularization, mixup, and noise-robust loss functions.
result Simple methods can boost accuracy by up to 2.5%.
Deep CNN-RNN model classifies breathing sounds for respiratory disease diagnosis.
problem Automated diagnosis of respiratory diseases using wearable devices.
method Patient-specific model tuning and local log quantization of weights.
result 71.81% accuracy on leave-one-out validation with patient-specific data.