Search Results for author:
Found 12 papers, 2 papers with code
NIIRF: Neural IIR Filter Field for HRTF Upsampling and Personalization
Existing NF-based methods focused on estimating the magnitude of the HRTF from a given sound source direction, and the magnitude is converted to a finite impulse response (FIR) filter.
Scenario-Aware Audio-Visual TF-GridNet for Target Speech Extraction
Target speech extraction aims to extract, based on a given conditioning cue, a target speech signal that is corrupted by interfering sources, such as noise or competing speakers.
Exploring the Integration of Speech Separation and Recognition with Self-Supervised Learning Representation
In detail, we explore multi-channel separation methods, mask-based beamforming and complex spectral mapping, as well as the best features to use in the ASR back-end model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios
The CHiME challenges have played a significant role in the development and evaluation of robust automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source Separation
Our neural separation model introduced for AVI alternately performs neural network blocks and single steps of an efficient iterative algorithm called iterative source steering.
Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge
To address the challenges encountered in the CEC2 setting, we introduce four major novelties: (1) we extend the state-of-the-art TF-GridNet model, originally designed for monaural speaker separation, for multi-channel, causal speech enhancement, and large improvements are observed by replacing the TCNDenseNet used in iNeuBe with this new architecture; (2) we leverage a recent dual window size approach with future-frame prediction to ensure that iNueBe-X satisfies the 5 ms constraint on algorithmic latency required by CEC2; (3) we introduce a novel speaker-conditioning branch for TF-GridNet to achieve target speaker extraction; (4) we propose a fine-tuning step, where we compute an additional loss with respect to the target speaker signal compensated with the listener audiogram.
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
To showcase such integration, we performed experiments on carefully designed synthetic datasets for noisy-reverberant multi-channel ST and SLU tasks, which can be used as benchmark corpora for future research.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Joint Optimization of Sampling Rate Offsets Based on Entire Signal Relationship Among Distributed Microphones
To address this problem, the proposed method jointly optimizes all SROs based on a probabilistic model of a multichannel signal.
Self-supervised Neural Audio-Visual Sound Source Localization via Probabilistic Spatial Modeling
By incorporating with the spatial information in multichannel audio signals, our method trains deep neural networks (DNNs) to distinguish multiple sound source objects.
Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance.
Phase reconstruction based on recurrent phase unwrapping with deep neural networks
In the proposed method, DNNs estimate phase derivatives instead of phase itself, which allows us to avoid the sensitivity problem.
Deep Griffin-Lim Iteration
This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN).
