Proposes Textual Echo Cancellation to improve speech recognition.
problem Improving speech recognition performance and user experience for smart devices.
method A novel sequence-to-sequence model with multi-source attention that processes both the microphone mixture signal and source text of TTS playback.
result Demonstrates enhanced speech recognition performance and reduced latency.
Jointly reduces echo, reverberation, and noise using neural networks.
problem Simultaneous reduction of acoustic echo, reverberation, and noise.
method Multichannel Gaussian modeling and neural network for joint optimization of filters.
result Outperforms individual and joint non-spectral models.
A new method suppresses echo more effectively with lower latency.
problem Echo cannot be fully removed by linear filters due to nonlinear relationships.
method Uses a modified Conv-TasNet with multiple streams of input signals.
result Efficacy validated in both single-talk and double-talk situations.
Data augmentation improves keyword spotting accuracy in noisy conditions.
problem Maintaining low false reject rates in far-field KWS with playback interference.
method Artificially corrupted training data with mixed music and TV audio.
result 30-45% reduction in false reject rates under audio playback.
In this article, we derive a new stepsize adaptation for the normalized least mean square algorithm (NLMS) by describing the task of linear acoustic echo cancellation from a Bayesian network perspective. Similar to the well-known Kalman filter equations, we model the acoustic wave propagation from the loudspeaker to th…
Classifies spherical objects filled with water or air using acoustic echoes and Form Function.
problem Automatic recognition of spherical objects based on acoustic echoes.
method Form Function analysis of wideband sonar data, fed into a Multilayer Perceptron (MLP) classifier.
result Performance of the MLP classifier compared to SVM, highlighting the effectiveness of Form Function.
In this article, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach for nonlinear system identification. The EPFES is derived from the frequently-employed state-space model, where the relevant information of the nonlinear system is captured by an unknown state vecto…
Mod-DeepESN improves echo state networks for complex, multi-scale tasks.
problem Efficiency in solving complex, multi-scale temporal tasks.
method Incorporates intrinsic plasticity into a modular deep echo state network architecture.
result Significantly outperforms state-of-the-art for time series prediction tasks.
The paper proposes count echo state networks for forecasting graduate student enrollments.
problem Forecasting graduate student enrollments from historical data.
method Developed hierarchical count echo state networks and compared them to Poisson autoregressions and negative binomial models.
result Hierarchical negative binomial based echo state network is the superior model.
Echo state networks with random weights can approximate any continuous system.
problem Approximating continuous dynamical systems using echo state networks.
method Randomly generated internal weights and a sampling procedure for activation functions.
result Echo state networks with random weights can approximate any continuous casual time-invariant operators with high probability.
New adaptive filters reduce energy consumption in electronic devices.
problem Reducing energy consumption in adaptive filtering algorithms.
method Data-selective adaptive filters, set-membership (SM) filters, trinion and quaternion systems, partial-updating, LMS and recursive LMS algorithms.
result Improved adaptive filtering algorithms with reduced computational complexity and enhanced stability.
GANs improve echo frame generation with labeled patches.
problem Automated echocardiography with limited labels.
method Conditional GAN with patch-based discriminator.
result GAN-enhanced echo frames match given segmentation masks.
Study converts echocardiography views using adversarial models.
problem Limited information sharing among echocardiography views.
method Adversarial generative models trained with LSGAN, PatchGAN, and cGAN.
result Generated views correlate with real views in LV area and length.
New technique halves scan time for multi-echo MR images.
problem Slow acquisition of multi-echo magnetic resonance images.
method Structured deep dictionary learning for adaptive reconstruction.
result Scan time reduced by half compared to state-of-the-art.
Echo noise improves compression in autoencoders with exact rate-distortion.
problem Limitations of Gaussian noise in lossy compression and VAEs.
method Introduces Echo noise, a data-driven noise channel with exact mutual information.
result Echo noise leads to improved bounds on log-likelihood and dominates VAEs.
Study analyzes Echo State Network parameters for Rossler attractor dynamics.
problem Understanding the influence of network type on Echo State Network performance.
method Experimental analysis of Echo State Network parameters using Rossler attractor.
result Exploration of how network type affects Echo State Network performance.
Efficient echo state network with explicit memory performs well on benchmark tasks.
problem Training differentiable neural computers is difficult and time-consuming.
method Echo state network with an explicit memory.
result Echo state network can recognize all regular languages, including those contractive networks cannot.
Study on deep and wide echo state networks for forecasting complex time series.
problem Performance analysis of deep reservoir computing models.
method Investigates the impact of partitioning neurons and parallel pathways on forecasting accuracy.
result Wide and deep networks outperform shallow models in forecasting multiscale spatiotemporal data.
Tuna-AI uses ML to predict tuna biomass from oceanography and echo-sounder data.
problem Estimating tuna biomass from limited data sources.
method Developed a Machine Learning model using 3-day echo-sounder data and oceanographic data.
result Tuna-AI predicts tuna biomass with high accuracy using a 3-day window of data.
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.
Paper predicts EEG features from acoustic features using RNN and GAN.
problem Predicting EEG features from acoustic features.
method Recurrent Neural Network (RNN) and Generative Adversarial Network (GAN).
result Lower RMSE and normalized RMSE values compared to generating acoustic features from EEG features.
New approach speeds up optimization with repeated gradient steps on same batch.
problem Performance bottlenecks in massive parallel pipelines with large batch sizes.
method Data echoing, taking repeated gradient steps on the same batch.
result Data echoing affords speedups on curvature-dominated part of convergence rate.
The paper analyzes feedback loops in recommender systems causing echo chambers and filter bubbles.
problem Feedback loops in recommender systems leading to echo chambers and filter bubbles.
method Theoretical analysis of user dynamics and recommender system behavior.
result Solutions to slow down system degeneracy and understanding echo chambers and filter bubbles.
Study on state dynamics in Deep Echo State Networks, revealing the importance of inter-reservoir connections.
problem Understanding state dynamics in multi-layered RNNs.
method Tools from information theory and numerical analysis.
result Inter-reservoir connections enrich representations in higher layers of DeepESNs.
This research unifies concepts of fading memory in RNNs.
problem Unclear relationships between fading memory concepts in RNNs.
method Unified language and new proofs for fading memory concepts.
result Clarified relationships between fading memory concepts.
Paper aims to find joint representation between vocal tract geometry and speech sound acoustics.
problem Finding a joint latent representation between articulatory and acoustic domains for vowel sounds.
method Invertible neural network models, convolutional autoencoder, normalizing flows, semi-supervised learning.
result Satisfactory performance in articulatory-to-acoustic and acoustic-to-articulatory mapping.
New empirical index reveals wider validity of Echo State Property in input-driven reservoirs.
problem Lack of proper input consideration in Echo State Property conditions.
method Introduced an empirical Echo State Property index to analyze stability of reservoirs with input signals.
result The actual domain of Echo State Property validity is wider than literature conditions suggest.
Note on new cancellation formulas for manifolds.
problem Generalizing anomaly cancellation formulas to manifolds.
method Proving new (a, b) type cancellation formulas and using transgression.
result Obtained characteristic forms with modularity properties.
Study shows integrating acoustic features in financial forecasting models can degrade performance.
problem Predicting stock market volatility from corporate earnings calls using speech features.
method Empirical investigation of acoustic feature extraction in teleconference environments using a two-stream late-fusion architecture.
result Integrating acoustic features via late fusion significantly degraded performance, reducing recall to 47.08%.
Bayesian SHMM discovers acoustic units from unlabeled speech.
problem Discovering language-specific acoustic units from unlabeled speech.
method Bayesian Subspace Hidden Markov Model (SHMM) trained on labeled data to find new acoustic units on target language.
result Significantly outperforms previous HMM-based systems and compares favorably with Variational Auto Encoder-HMM.
Ensemble of MFESNs improves macroeconomic forecasting.
problem Improving accuracy in macroeconomic forecasting.
method Hedge and Follow-the-Leader schemes applied to MFESNs.
result Ensemble models outperform individual models in forecasting.
Echo state networks are powerful recurrent neural networks. However, they are often unstable and shaky, making the process of finding an good ESN for a specific dataset quite hard. Obtaining a superb accuracy by using the Echo State Network is a challenging task. We create, develop and implement a family of predictably…
Unified treatment of RC in stochastic and deterministic settings.
problem Understanding and generalizing reservoir computing in both deterministic and stochastic contexts.
method Investigation of state-space systems, analysis of fading memory and solution stability, introduction of stochastic echo states.
result Generality of fading memory and solution stability in state-space systems, even without the echo state property.
This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network, the entire collection of hidden layer node states from the training dataset is …
The order submission and cancelation processes are two crucial aspects in the price formation of stocks traded in order-driven markets. We investigate the dynamics of order cancelation by studying the statistical properties of inter-cancelation durations defined as the waiting times between consecutive order cancelatio…
CESAR improves wind speed and power forecasting for high-resolution simulations.
problem Accurate high-resolution wind forecasting for efficient power grid management.
method A spatio-temporal neural network model using deep convolutional autoencoder and echo state network.
result CESAR provides up to 17% improvement in wind speed and power forecasting compared to best alternatives.
Many neural networks use the tanh activation function, however when given a probability distribution as input, the problem of computing the output distribution in neural networks with tanh activation has not yet been addressed. One important example is the initialization of the echo state network in reservoir computing…
Study modular forms over Γ^0(2) and anomaly cancellation formulas.
problem Anomaly cancellation formulas for modular forms over Γ^0(2).
method Study and analysis of modular forms over Γ^0(2).
result Anomaly cancellation formulas derived for modular forms over Γ^0(2).
Recent advances in speech synthesis suggest that limitations such as the lossy nature of the amplitude spectrum with minimum phase approximation and the over-smoothing effect in acoustic modeling can be overcome by using advanced machine learning approaches. In this paper, we build a framework in which we can fairly co…
Efficient cross-validation method for Echo State Networks reduces validation time complexity.
problem Hyper-parameter tuning and validation for Echo State Networks (ESNs).
method Proposes several k-fold cross-validation schemes for ESNs with an efficient algorithm. result Cross-validation of ESNs can be done for the same time complexity as a single split validation.
We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language model. The acoustic model is a neural network used for estimating the probabilities of pitches in …
Study shows structured reservoirs improve deep ESN performance.
problem Improving performance of deep reservoir computing networks.
method Investigated structured reservoir topologies in deep ESNs.
result Structured reservoirs significantly enhance predictive performance.
Discrete return (DR) Laser Detection and Ranging (Ladar) systems provide a series of echoes that reflect from objects in a scene. These can be first, last or multi-echo returns. In contrast, Full-Waveform (FW)-Ladar systems measure the intensity of light reflected from objects continuously over a period of time. In a c…
Local well-posedness proved for 3D compressible Euler equations with rough vorticity.
problem Proving local well-posedness for compressible Euler equations with rough vorticity.
method Decomposing velocity into irrotational and wave components, using cancellations, trilinear estimates, and Strichartz estimates.
result Local well-posedness achieved for compressible Euler equations in Hs with s>2 for rough vorticity. Order cancellation process plays a crucial role in the dynamics of price formation in order-driven stock markets and is important in the construction and validation of computational finance models. Based on the order flow data of 18 liquid stocks traded on the Shenzhen Stock Exchange in 2003, we investigate the empiric…
ConvNet classifies whale vocalizations and ambient noise in acoustic recordings.
problem Automated detection and classification of marine mammal vocalizations in acoustic recordings.
method Convolutional Neural Network with a novel acoustic representation.
result Classifier accurately detects and classifies whale vocalizations and ambient noise.
Improved multi-speaker TTS using GANs and waveform loss.
problem Training acoustic models for neural vocoders in multi-speaker TTS systems.
method Proposed frameworks incorporating Wasserstein GAN with gradient penalty (WGAN-GP) and discretized mixture logistic loss (DML) into acoustic models trained with WaveNet.
result Acoustic models trained with WGAN-GP and DML loss achieve highest subjective evaluation scores in multi-speaker TTS.
Improved acoustic word embeddings using shared decoder in multi-view encoders.
problem Learning discriminative acoustic word embeddings from text labels.
method Combining Siamese multi-view encoders with a shared decoder network to maximize the relationship between acoustic and text embeddings.
result 11.1% relative improvement in average precision on acoustic word discrimination task with WSJ dataset.