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Found 6 papers, 1 papers with code
Unrestricted Global Phase Bias-Aware Single-channel Speech Enhancement with Conformer-based Metric GAN
With the rapid development of neural networks in recent years, the ability of various networks to enhance the magnitude spectrum of noisy speech in the single-channel speech enhancement domain has become exceptionally outstanding.
A computationally efficient semi-blind source separation based approach for nonlinear echo cancellation based on an element-wise iterative source steering
While the semi-blind source separation-based acoustic echo cancellation (SBSS-AEC) has received much research attention due to its promising performance during double-talk compared to the traditional adaptive algorithms, it suffers from system latency and nonlinear distortions.
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.
Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features
Although there is a method to improve ASR performance in the presence of emotional speech, it requires the fine-tuning of ASR, which has a high computational cost and leads to the loss of cues important for determining the presence of emotion in speech segments, which can be helpful in SER.
Ranked #4 on
Multimodal Emotion Recognition
on IEMOCAP
Multimodal Emotion Recognition
Speech Emotion Recognition
+2
Fast MVAE: Joint separation and classification of mixed sources based on multichannel variational autoencoder with auxiliary classifier
While MVAE is notable in its impressive source separation performance, the convergence-guaranteed optimization algorithm and that it allows us to estimate source-class labels simultaneously with source separation, there are still two major drawbacks, i. e., the high computational complexity and unsatisfactory source classification accuracy.
Semi-blind source separation with multichannel variational autoencoder
This paper proposes a multichannel source separation technique called the multichannel variational autoencoder (MVAE) method, which uses a conditional VAE (CVAE) to model and estimate the power spectrograms of the sources in a mixture.
