Research maps body movements to sound using advanced algorithms.
problem Detect and classify user commands with minimal latency.
method Vector Autoregressive Hierarchical Hidden Markov Models (VAR-HHMM) with wireless sensor nodes.
result VAR-HHMM algorithm outperforms K-nearest neighbors in detection and classification.
Paper introduces probabilistic module interface for complex models and inference.
problem Handling complex probabilistic models with latent variables and custom inference methods.
method Develops a platform-agnostic interface for encapsulating models and inference programs, allowing sound approximate inference algorithms for networks of modules.
result Sound approximate inference algorithms can be constructed for networks of probabilistic modules.
From a machine learning perspective, the human ability localize sounds can be modeled as a non-parametric and non-linear regression problem between binaural spectral features of sound received at the ears (input) and their sound-source directions (output). The input features can be summarized in terms of the individual…
Deep learning maps tongue movements to speech sounds for voiceless individuals.
problem Developing silent speech interfaces for individuals without a larynx.
method Hybrid spatio-temporal 3D convolutions and feature shuffling for formant estimation and tracking from ultrasound tongue images.
result Best model achieves R-squared of 99.96% for vowel formant regression.
BC learning improves deep sound recognition performance.
problem Improving deep sound recognition using novel training data.
method BC learning: mixing sounds from different classes to generate between-class sounds and train models to recognize these.
result BC learning improves performance on various sound recognition networks, surpassing human level.
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.
Insurance contracts for autonomous AI agents must be actuarially sound and resistant to gaming.
problem Designing insurance contracts for autonomous AI agents that are actuarially sound and resistant to gaming.
method Characterizing a five-attack space and proving the actuarial runtime is gaming-resistant.
result An incentive-compatible layer for actuarial control of autonomous-agent side effects.
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.
Paper proposes a method to detect unknown anomalous sounds without training data using deep learning and Neyman-Pearson lemma.
problem Unsupervised detection of unknown anomalous sounds in audio data.
method Uses an autoencoder to minimize reconstruction error of normal sounds and Neyman-Pearson lemma to maximize true positive rate under low false positive rate conditions.
result The proposed method improves performance measures of unsupervised anomaly detection in audio data under low false positive rate conditions.
Batch uniformization improves anomaly detection in sound data.
problem Anomaly scores for rare and frequent normal sounds are not uniform.
method Propose batch uniformization to minimize anomaly scores by weighting samples based on their density.
result Improves performance of unsupervised anomaly detection in sound data.
Breath sounds can identify speakers with high accuracy.
problem Identifying speakers from breath sounds.
method Examined breath sounds during continuous speech, focusing on inhalation phase.
result Breath sounds carry unique speaker-specific information, enabling accurate speaker identification.
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.
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.
CNN improves whale sound detection in noisy environments.
problem Automatically detecting humpback whale sounds in complex background noises.
method Used Convolution Neural Network (CNN) for bi-class classification.
result CNN features outperformed traditional spectrogram methods in detecting whale sounds.
MCLNN improves sound classification with fewer parameters.
problem Improving sound classification accuracy with fewer parameters.
method MCLNN uses a binary mask to induce sparseness in frequency bands, automating feature exploration.
result MCLNN achieves competitive results on Urbansound8k with 12% fewer parameters.
Deep learning models improve sound separation across various types of sounds.
problem Developing a universal method to separate arbitrary sounds of different types.
method Created a dataset of mixtures containing arbitrary sounds, investigated mask-based separation architectures, and tested different framewise analysis-synthesis bases.
result STFT outperformed learnable bases in universal sound separation tasks.
Smart city surveillance benefits from sound event recognition.
problem Improving monitoring capabilities in smart cities.
method Exploration of several classifiers on the SESA dataset.
result SGD achieved 72.13% accuracy in sound event recognition.
Study shows diffused interface flows to single diffused balls over time.
problem Volume-preserving mean curvature flow in Euclidean space.
method Diffused interface version, exponential convergence proof.
result Exponential convergence to single diffused balls.
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.
In this study we present a kernel based convolution model to characterize neural responses to natural sounds by decoding their time-varying acoustic features. The model allows to decode natural sounds from high-dimensional neural recordings, such as magnetoencephalography (MEG), that track timing and location of human …
Improved neural network detects heart sounds with 87.5% accuracy from noisy recordings.
problem Detecting cardiac abnormalities from noisy heart sound recordings.
method Segmental Convolutional Neural Network (CNN) architecture trained on noisy recordings.
result Best model achieved 87.5% accuracy on PhysioNet/CinC Challenge dataset.
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.
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.
MCLNN improves sound event recognition with fewer parameters.
problem Improving sound event recognition with deep neural networks.
method Developed MCLNN to enforce sparseness and frequency shift invariance.
result MCLNN achieved competitive performance with 12% fewer parameters.
This paper analyzes sound event detection in synthetic office audio, comparing different systems.
problem Comparing sound event detection systems in synthetic office audio.
method Analysis of systems submitted to DCASE 2016 task, using synthetic office sounds.
result Statistical analysis of results, highlighting system performance under controlled conditions.
MCLNN improves sound recognition by learning frequency bands.
problem Sound recognition from neural networks often misses environmental sound specifics.
method MCLNN incorporates filterbank behavior and automates feature combination exploration.
result MCLNN outperforms state-of-the-art methods on ESC-10 dataset.
Deep learning speeds sound speed inversion in ultrasound.
problem Limited high-end ultrasound hardware for shear wave imaging.
method Fully convolutional deep neural network using simulated data.
result Inversion of longitudinal sound speed at high frame rates.
The paper presents a method for sound event localization and detection using CRNN models.
problem Sound event localization and detection in complex environments.
method Consecutive ensemble of CRNN models for estimating event onset, offset, direction of arrival, and classification.
result The proposed method outperforms other participants in the DCASE2019 task3.
Unsupervised learning identifies sound categories from unlabeled audio.
problem Learning sound categories from unlabeled audio recordings.
method Class-agnostic semantic constraints and triplet loss-based training.
result Low-dimensional embeddings recover 41% and 84% of supervised performance.
Extends diffuse interface methods to graphs and hypergraphs with non-smooth potentials.
problem Semi-supervised learning on graphs and hypergraphs.
method Generalizes diffuse interface methods using non-smooth potential functions and hypergraph Laplacians.
result The diffuse interface method can be applied to both graph and hypergraph data.
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.
Novel BCI system classifies imagined speech with high accuracy.
problem Classifying imagined speech from brain signals.
method Hierarchical deep learning with CNN and autoencoder.
result Achieved 83.42% average accuracy across six phonological tasks.
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.
A new method classifies heart sounds using i-vectors and machine learning.
problem Heart sound classification for disease diagnostics.
method Extract i-vectors from MFCC features, apply PCA and VAE for dimensionality reduction, then use GMMs and SVM for classification.
result The method improves heart sound classification by 16% on the Physionet dataset.
New interface explains contextual bandits to non-experts.
problem Interpreting and managing contextual bandits for non-expert operators.
method Developed a metric 'value gain' for off-policy evaluation and designed an interface to explain bandit behavior.
result Empowered non-experts to manage complex machine learning systems through accessible presentation.
Study presents a dataset and methods to handle noisy labels in sound event classification.
problem Label noise in sound event classification datasets.
method Developed a dataset with noisy labels and evaluated CNN baseline systems.
result Training with large amounts of noisy data can outperform training with carefully-labeled data.
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.
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.
Adaptive pooling operators improve sound event detection with weak labels.
problem Efficiently label audio recordings with weakly annotated sound sources.
method Developed adaptive pooling operators for multiple instance learning.
result Adaptive pooling operators outperform non-adaptive methods on static predictions and nearly match strong annotations.
Generative model improves sound synthesis realism.
problem Synthesize natural sounds with realistic perception.
method Latent force modelling with physical knowledge incorporated.
result Generated sounds perceived as more realistic.
Develops a 2D Allen-Cahn theory for interfaces with boundary.
problem Analyzing interfaces with boundary in the line-bundle framework.
method Constructs a model solution on the punctured plane and develops its stability and invertibility theory.
result Constructs Allen-Cahn sections concentrating on prescribed line segments.
Reduces sound event localization error by 2.6x with hybrid parametric-deep learning.
problem Sound event localization and detection accuracy.
method Hybrid approach combining parametric spatial audio analysis and deep learning.
result Reduction of localization error by 2.6x compared to baseline.
Generative replay extends sound classification models to new classes without old data.
problem Incrementally refining a sound classifier with new data causes previously learned tasks to degrade.
method Developed a generative replay procedure to generate training data in place of older datasets.
result Generative replay with 4% of old data performs as well as keeping 20% of old data.
Deep model learns protein interfaces from high-order interactions.
problem Predicting protein interfaces from amino acid pairs.
method Graph neural networks and convolutional neural networks for 2D dense predictions.
result Our method consistently improves interface prediction performance.
InverSynth automatically tunes synthesizer parameters from audio input.
problem Manual tuning of synthesizer parameters is time-consuming and requires expertise.
method Strided convolutional neural networks for inferring synthesizer parameters.
result InverSynth outperforms baselines in synthesizer parameter tuning.
We consider the regularity of an interface between two incompressible and inviscid fluids flows in the presence of surface tension. We obtain local in time estimates on the interface in H23k+1 and the velocity fields in H23k. These estimates are obtained using geometric considerations which show th…
Study boundary behavior of limit interfaces in Riemannian manifolds without convexity assumptions.
problem Boundary behavior of limit interfaces in Riemannian manifolds.
method Proves limit-interface is a free boundary varifold, integer rectifiable up to boundary.
result No convexity assumption required; valid even when limit-interface clusters near boundary.
New task AQA tackles acoustic reasoning from sound scenes.
problem Promote research in acoustic reasoning.
method Generate acoustic scenes from elementary sounds and formulate questions.
result Preliminary results with models FiLM and MAC show promise.