Search Results for author:
Found 29 papers, 5 papers with code
Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam
First, we propose a time-domain implementation of SpeakerBeam similar to that proposed for a time-domain audio separation network (TasNet), which has achieved state-of-the-art performance for speech separation.
PILOT: Introducing Transformers for Probabilistic Sound Event Localization
Sound event localization aims at estimating the positions of sound sources in the environment with respect to an acoustic receiver (e. g. a microphone array).
Integration of variational autoencoder and spatial clustering for adaptive multi-channel neural speech separation
In this paper, we propose a method combining variational autoencoder model of speech with a spatial clustering approach for multi-channel speech separation.
Audio and Speech Processing
Exploiting Attention-based Sequence-to-Sequence Architectures for Sound Event Localization
Herein, attentions allow for capturing temporal dependencies in the audio signal by focusing on specific frames that are relevant for estimating the activity and direction-of-arrival of sound events at the current time-step.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
All-neural online source separation, counting, and diarization for meeting analysis
While significant progress has been made on individual tasks, this paper presents for the first time an all-neural approach to simultaneous speaker counting, diarization and source separation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Jointly optimal dereverberation and beamforming
We previously proposed an optimal (in the maximum likelihood sense) convolutional beamformer that can perform simultaneous denoising and dereverberation, and showed its superiority over the widely used cascade of a WPE dereverberation filter and a conventional MPDR beamformer.
End-to-end training of time domain audio separation and recognition
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multi-speaker speech recognition.
Tackling real noisy reverberant meetings with all-neural source separation, counting, and diarization system
Automatic meeting analysis is an essential fundamental technology required to let, e. g. smart devices follow and respond to our conversations.
Improving noise robust automatic speech recognition with single-channel time-domain enhancement network
With the advent of deep learning, research on noise-robust automatic speech recognition (ASR) has progressed rapidly.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Block Coordinate Descent Algorithms for Auxiliary-Function-Based Independent Vector Extraction
We also newly develop a BCD for a semiblind IVE in which the transfer functions for several super-Gaussian sources are given a priori.
Neural Network-based Virtual Microphone Estimator
Developing microphone array technologies for a small number of microphones is important due to the constraints of many devices.
Speaker activity driven neural speech extraction
Target speech extraction, which extracts the speech of a target speaker in a mixture given auxiliary speaker clues, has recently received increased interest.
A Joint Diagonalization Based Efficient Approach to Underdetermined Blind Audio Source Separation Using the Multichannel Wiener Filter
Based on this approach, we present FastFCA, a computationally efficient extension of FCA.
Audio Source Separation
Sound
Audio and Speech Processing
Multimodal Attention Fusion for Target Speaker Extraction
Recently an audio-visual target speaker extraction has been proposed that extracts target speech by using complementary audio and visual clues.
Data Fusion for Audiovisual Speaker Localization: Extending Dynamic Stream Weights to the Spatial Domain
Estimating the positions of multiple speakers can be helpful for tasks like automatic speech recognition or speaker diarization.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend
Recently, the end-to-end approach has been successfully applied to multi-speaker speech separation and recognition in both single-channel and multichannel conditions.
Independent Vector Extraction for Fast Joint Blind Source Separation and Dereverberation
We address a blind source separation (BSS) problem in a noisy reverberant environment in which the number of microphones $M$ is greater than the number of sources of interest, and the other noise components can be approximated as stationary and Gaussian distributed.
Comparison of remote experiments using crowdsourcing and laboratory experiments on speech intelligibility
Many subjective experiments have been performed to develop objective speech intelligibility measures, but the novel coronavirus outbreak has made it very difficult to conduct experiments in a laboratory.
Blind and neural network-guided convolutional beamformer for joint denoising, dereverberation, and source separation
This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Switching Independent Vector Analysis and Its Extension to Blind and Spatially Guided Convolutional Beamforming Algorithms
This paper develops a framework that can perform denoising, dereverberation, and source separation accurately by using a relatively small number of microphones.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ISS2: An Extension of Iterative Source Steering Algorithm for Majorization-Minimization-Based Independent Vector Analysis
Although the time complexity per iteration of ISS is $m$ times smaller than that of IP, the conventional ISS converges slower than the current fastest IP (called $\text{IP}_2$) that updates two rows of $W$ in each iteration.
Listen only to me! How well can target speech extraction handle false alarms?
Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance.
Mask-based Neural Beamforming for Moving Speakers with Self-Attention-based Tracking
We thus introduce a learning-based framework that computes optimal attention weights for beamforming.
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods.
Modified Parametric Multichannel Wiener Filter \\for Low-latency Enhancement of Speech Mixtures with Unknown Number of Speakers
This paper introduces a novel low-latency online beamforming (BF) algorithm, named Modified Parametric Multichannel Wiener Filter (Mod-PMWF), for enhancing speech mixtures with unknown and varying number of speakers.
Target Speech Extraction with Conditional Diffusion Model
TSE is realized by conditioning the extraction process on a clue identifying the target speaker.
Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss
However, this training objective may not be optimal for a specific array processing back-end, such as beamforming.
Array Geometry-Robust Attention-Based Neural Beamformer for Moving Speakers
Recently, a mask-based beamformer with attention-based spatial covariance matrix aggregator (ASA) was proposed, which was demonstrated to track moving sources accurately.
