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.
New ML approach predicts glare in open-plan offices with high accuracy.
problem Challenging to predict discomfort glare in open-plan offices.
method Used Machine Learning algorithms to predict glare.
result ML model achieved 83.8% accuracy in predicting glare.
The study uses machine learning to analyze office floor plans and predict function based on geometry.
problem Lack of formalisms to describe spatial affordance in automated floor-plan generation tools.
method Supervised and unsupervised data mining techniques, including J48 algorithm, were used to analyze office floor plans.
result J48 algorithm can predict class performance on unseen examples up to 79.5% for office dataset.
Machine learning predicts TV show success based on factors like characters and direction.
problem Predicting the success of TV shows in a competitive market.
method Descriptive and predictive modeling using machine learning.
result Shows with strong characters and good direction tend to be more successful.
On June 26th, 2004, Central bank governors and the heads of bank supervisory authorities in the Group of Ten (G10) countries issued a press release and endorsed the publication of "International Convergence of Capital Measurement and Capital Standards: a Revised Framework", the new capital adequacy framework commonly k…
Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exp…
Predicting movie box office success using historical data and modern computing.
problem Manual prediction of movie revenue is difficult due to many exogenous variables.
method Use modern computing power and historical data to model movie revenue.
result Predicted movie revenues can be used for planning production and distribution stages.
Post-pandemic, work patterns shifted with fewer days in offices and a new midweek mountain.
problem Shift in work patterns and integration of personal and professional life.
method Behavioral analysis using mobile geolocation records.
result Significant decline in office-based workdays and emergence of a new midweek mountain.
New technique reduces FRTB IMA capital calculation burden by over 90%.
problem Reduction of computational burden in FRTB IMA capital calculation.
method Orthogonal Chebyshev Sliding Technique based on high-dimensional Chebyshev Tensors.
result Reduction of computational burden by more than 90%.
System tracks regulatory changes for compliance officers.
problem Struggling to keep up with regulatory changes.
method Fetch announcements, classify importance and applicability.
result Simple hierarchical classification works best.
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.
Deep learning model predicts window openings for commercial buildings.
problem Window openings are often biased and require tuning for each occupant.
method Deep learning model trained on occupant data from multiple buildings.
result Model achieves high evaluation accuracy and F1 scores.
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.
Mathematical framework for transfer learning feasibility and transfer risk.
problem Theoretical analysis of transfer learning.
method Reformulated transfer learning as an optimization problem, introduced transfer risk concept.
result Demonstrated the potential and benefits of incorporating transfer risk in transfer learning evaluation.
Spacematch matches office workers with suitable workspaces based on their preferences.
problem Matching workers with suitable workspaces in ABW environments.
method Developed a web-based mobile app to collect occupant preferences and IoT sensors for real-time environmental feedback.
result Occupant preferences can be segmented and matched to build a recommendation platform.
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.
Study finds strict collection policies improve portfolio quality of microfinance banks.
problem Improving portfolio quality of microfinance banks through better credit collection policies.
method Multi-stage sampling, regression analysis, descriptive statistics.
result Collection policy has a higher effect on portfolio quality.
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.
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 …
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.
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.
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.
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.
Heart diseases constitute a global health burden, and the problem is exacerbated by the error-prone nature of listening to and interpreting heart sounds. This motivates the development of automated classification to screen for abnormal heart sounds. Existing machine learning-based systems achieve accurate classificatio…
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.
Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of …
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.
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.
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.
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.