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Found 16 papers, 8 papers with code
Signal Inpainting from Fourier Magnitudes
Signal inpainting is the task of restoring degraded or missing samples in a signal.
Mean absorption estimation from room impulse responses using virtually supervised learning
The critical choices of geometric, acoustic and simulation parameters used to train the models are extensively discussed and studied, while keeping in mind conditions that are representative of the field of building acoustics.
Blind Room Parameter Estimation Using Multiple-Multichannel Speech Recordings
Knowing the geometrical and acoustical parameters of a room may benefit applications such as audio augmented reality, speech dereverberation or audio forensics.
A Dataset of Dynamic Reverberant Sound Scenes with Directional Interferers for Sound Event Localization and Detection
This report presents the dataset and baseline of Task 3 of the DCASE2021 Challenge on Sound Event Localization and Detection (SELD).
dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing
This paper presents dEchorate: a new database of measured multichannel Room Impulse Responses (RIRs) including annotations of early echo timings and 3D positions of microphones, real sources and image sources under different wall configurations in a cuboid room.
LibriMix: An Open-Source Dataset for Generalizable Speech Separation
Most deep learning-based speech separation models today are benchmarked on it.
Audio and Speech Processing
Filterbank design for end-to-end speech separation
Also, we validate the use of parameterized filterbanks and show that complex-valued representations and masks are beneficial in all conditions.
A Statistically Principled and Computationally Efficient Approach to Speech Enhancement using Variational Autoencoders
Recent studies have explored the use of deep generative models of speech spectra based of variational autoencoders (VAEs), combined with unsupervised noise models, to perform speech enhancement.
MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval
The approach operates directly in the parameter-space of echo locations and weights, and enables near-exact blind and off-grid echo retrieval from discrete-time measurements.
Blind Source Separation Using Mixtures of Alpha-Stable Distributions
We propose a new blind source separation algorithm based on mixtures of alpha-stable distributions.
VAST : The Virtual Acoustic Space Traveler Dataset
This paper introduces a new paradigm for sound source lo-calization referred to as virtual acoustic space traveling (VAST) and presents a first dataset designed for this purpose.
Phase Unmixing : Multichannel Source Separation with Magnitude Constraints
We consider the problem of estimating the phases of K mixed complex signals from a multichannel observation, when the mixing matrix and signal magnitudes are known.
Robust Head-Pose Estimation Based on Partially-Latent Mixture of Linear Regressions
Head-pose estimation has many applications, such as social event analysis, human-robot and human-computer interaction, driving assistance, and so forth.
Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies
This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations.
Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression
Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces.
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables
We introduce a mixture of locally-linear probabilistic mapping model that starts with estimating the parameters of inverse regression, and follows with inferring closed-form solutions for the forward parameters of the high-dimensional regression problem of interest.
