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.
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.
As part of the 2016 public evaluation challenge on Detection and Classification of Acoustic Scenes and Events (DCASE 2016), the second task focused on evaluating sound event detection systems using synthetic mixtures of office sounds. This task, which follows the `Event Detection - Office Synthetic' task of DCASE 2013,…
To address Task 5 in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2018 challenge, in this paper, we propose an ensemble learning system. The proposed system consists of three different models, based on convolutional neural network and long short memory recurrent neural network. With extracted …
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.
Acoustic scene classification and related tasks have been dominated by Convolutional Neural Networks (CNNs). Top-performing CNNs use mainly audio spectograms as input and borrow their architectural design primarily from computer vision. A recent study has shown that restricting the receptive field (RF) of CNNs in appro…
This paper describes Task 2 of the DCASE 2018 Challenge, titled "General-purpose audio tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle platform as "Freesound General-Purpose Audio Tagging Challenge". The goal of the task is to build an audio tagging system that can recognize the c…
Distribution mismatches between the data seen at training and at application time remain a major challenge in all application areas of machine learning. We study this problem in the context of machine listening (Task 1b of the DCASE 2019 Challenge). We propose a novel approach to learn domain-invariant classifiers in a…
Improved acoustic scene classification with factorized CNN.
problem Acoustic scene classification in varying environments.
method Large-margin factorized CNN with triplet loss.
result Improved performance and better generalization on unseen data.
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.
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.
System tackles indeterminacies in automated audio captioning.
problem Word selection and sentence length indeterminacies in automated audio captioning.
method Solves caption generation and sub-indeterminacy problems through multi-task learning to estimate keywords and sentence length.
result Model achieved 20.7 SPIDEr score, significantly outperforming baseline.
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.
Enhances ASC using time- and frequency-liked CNNs and bilinear pooling.
problem Improving acoustic scene classification accuracy.
method Harmonic and percussive source separation, two-stream CNN architecture, bilinear pooling.
result Improved accuracy on DCASE 2019 sub task 1a dataset.
CNNs improve generalization to unseen audio devices with increased width, not depth.
problem CNNs are sensitive to specific audio recording devices in acoustic scene classification.
method Investigated the relationship between over-parameterization and generalization in CNNs for audio classification.
result Increasing width improves generalization to unseen devices without increasing the number of parameters.
This paper introduces a model of environmental acoustic scenes which adopts a morphological approach by ab-stracting temporal structures of acoustic scenes. To demonstrate its potential, this model is employed to evaluate the performance of a large set of acoustic events detection systems. This model allows us to expli…
Acoustic scene classification is the task of identifying the scene from which the audio signal is recorded. Convolutional neural network (CNN) models are widely adopted with proven successes in acoustic scene classification. However, there is little insight on how an audio scene is perceived in CNN, as what have been d…
Paper proposes MTL for weakly labelled SED, improving performance with 2-step attention.
problem Weakly labelled sound event detection.
method Multi-Task Learning framework with 2-step Attention Pooling.
result Improved SED performance with 22.3%, 12.8%, 5.9% gains at 0, 10, 20 dB SNR.
A multi-head attention network improves ASC by recognizing overlapping sound patterns.
problem Challenging ASC due to overlapping sound patterns and complex event mixtures.
method Proposes a multi-head attention network to model complex temporal input structures.
result Achieved competitive performance on DCASE 2018 Task 5 dataset.
We describe a novel weakly labeled Audio Event Classification approach based on a self-supervised attention model. The weakly labeled framework is used to eliminate the need for expensive data labeling procedure and self-supervised attention is deployed to help a model distinguish between relevant and irrelevant parts …
In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical K-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform …
AdamP optimizes momentum-based optimizers for scale-invariant weights, improving model performance.
problem Premature decay of effective step sizes in momentum-based optimizers for scale-invariant weights.
method Proposes SGDP and AdamP to eliminate the radial component at each optimizer step, preserving convergence properties.
result Uniform gains across multiple benchmarks, improving model performance.
The ICML 2013 Workshop on Challenges in Representation Learning focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provi…
NIST's CTS Challenge evaluates speaker recognition using telephony data.
problem Improving speaker recognition accuracy using telephony data.
method Large-scale neural network architectures, extensive data augmentation, proprietary data, and fine-tuning.
result Remarkable improvements in speaker recognition performance.
This paper automates tagging programming challenge descriptions.
problem Tagging programming challenge descriptions is tedious for creators.
method Used machine and deep learning methods for automation.
result Deep learning methods outperform traditional IR approaches.
Deep learning models are growing, posing new mathematical challenges.
problem Mathematical challenges in training, inference, generalization, and optimization of deep models.
method Formal mathematical analysis and communication with mathematicians, statisticians, and computer scientists.
result A set of new mathematical challenges in deep learning.
Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is beginning to show some successes in real-world scenarios. However, much of the research advances in RL are often hard to leverage in real-world systems due to a series of assumptions that are rarely satisfied in practice. We pres…
VoxCeleb 2019 challenge assesses speaker recognition in uncontrolled settings.
problem Evaluate speaker recognition technology in unconstrained data.
method Public dataset, challenge, and workshop at Interspeech 2019.
result Baseline results and discussions provided.
Sparse DNNs face scalability issues; MIT/IEEE/Amazon challenge analyzes best solutions.
problem Scalability issues in Sparse Deep Neural Networks (DNNs).
method Mathematically defined DNN inference computation, community submissions from various fields.
result Sparse DNN execution time, TmDNN, is strongly dependent on the number of operations, Nmop. ICLR 2021 challenge in computational geometry and topology attracted 16 teams.
problem Designing and evaluating computational methods in differential geometry and topology.
method Designing and hosting an open-source competition with repositories Geomstats and Giotto-TDA.
result 16 teams participated in the challenge, showcasing innovative contributions to computational geometry and topology.
XPDNet wins MRI reconstruction challenge with neural network.
problem MRI reconstruction from under-sampled data.
method Inspired by MRI and computer vision best practices, XPDNet uses a neural network.
result XPDNet achieved state-of-the-art results in the 2020 fastMRI challenge.
Study functionals on almost complex structures for Yau's Challenge.
problem Yau's Challenge on compact C-manifolds. method Variational properties of functionals on almost complex structures.
result Variational properties could be used to tackle Yau's Challenge.
Improved disentanglement through learned feature aggregation.
problem Disentangling latent factors in images.
method Variational autoencoder trained on regionally aggregated feature maps from ImageNet.
result 2nd place in NeurIPS 2019 disentanglement challenge.
Challenge aims to develop automated meningioma MRI segmentation models.
problem Lack of automated, objective tools for meningioma assessment.
method Develop and evaluate models on largest annotated dataset.
result Improved care of patients with meningioma through automated segmentation.
Improved disentanglement in VAEs using aggregated feature maps.
problem Improving disentanglement in Variational Autoencoders (VAEs).
method Regionally aggregated feature maps extracted from pre-trained CNNs on ImageNet.
result 2nd place in NeurIPS 2019 disentanglement challenge.
We organized a competition on Autonomous Lifelong Machine Learning with Drift that was part of the competition program of NeurIPS 2018. This data driven competition asked participants to develop computer programs capable of solving supervised learning problems where the i.i.d. assumption did not hold. Large data sets w…
S2M optimizes mining for diverse data subpopulations.
problem Scalability and uniformity in training sets with many labels and diverse data.
method Doubly-stochastic mining (S2M) computes per-example and minibatch losses on hardest labels/examples.
result S2M ensures good performance across all data subpopulations.
Differentiable ABMs face challenges in inference and optimisation.
problem Challenges in parameter inference and optimisation for differentiable ABMs.
method Discussion and experiments highlighting challenges.
result Challenges remain in constructing differentiable ABMs.
Current state-of-the-art discrete optimization methods struggle behind when it comes to challenging contrast-enhancing discrete energies (i.e., favoring different labels for neighboring variables). This work suggests a multiscale approach for these challenging problems. Deriving an algebraic representation allows us to…
Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the …
Bayesian optimization outperformed random search in machine learning hyperparameter tuning challenge.
problem Optimizing hyperparameters of machine learning models using derivative-free methods.
method Bayesian optimization vs. random search on real datasets.
result Bayesian optimization significantly outperformed random search in held-out objective functions.
This paper describes the Speech Technology Center (STC) antispoofing systems submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous challenges and includes 2 evaluation conditions: logical access use-case scenario with speech synthesis and voice conversion attack types and ph…
Improved contact tracing models outperform NIST challenge results.
problem Contact tracing using phone data and machine learning.
method Developed two machine learning models (GBM and MLP) from phone instrumental data features.
result Outperformed the leading NIST challenge result by HKUST.
Machine learning simplifies finance, but faces challenges.
problem Adopting machine learning in finance.
method Analyzing challenges in financial services adoption.
result Challenges in finance adoption of ML.
Quantum ML promises faster data analysis but faces trainability challenges.
problem Challenges in training quantum machine learning models.
method Review of current methods and applications of quantum neural networks and quantum deep learning.
result Opportunities for quantum advantage in quantum machine learning.
Challenge evaluates semantic code search using annotated corpus.
problem Evaluating relevant code from natural language queries.
method Release of CodeSearchNet Corpus and expert annotations.
result 99 queries with 4k relevance annotations for evaluation.
NetML provides datasets and challenges for network traffic analysis.
problem Lack of representative datasets and reproducibility issues in network traffic analysis.
method Released three open datasets with flow features and raw packets, implemented machine learning methods.
result NetML datasets will serve as a common platform for AI-driven research.
Brief history and challenges of interpretable machine learning.
problem Challenges in interpreting machine learning models, especially in scientific applications.
method Overview of state-of-the-art methods and discussion of challenges.
result Interpretable machine learning has a rich history but faces significant challenges.