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Found 26 papers, 5 papers with code
Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge
In this paper, we present the objective and subjective evaluations of the systems that were submitted to the CHiME-7 UDASE task, and we provide an analysis of the results.
TokenSplit: Using Discrete Speech Representations for Direct, Refined, and Transcript-Conditioned Speech Separation and Recognition
The model operates on transcripts and audio token sequences and achieves multiple tasks through masking of inputs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
AudioSlots: A slot-centric generative model for audio separation
We discuss the results and limitations of our approach in detail, and further outline potential ways to overcome the limitations and directions for future work.
AudioScopeV2: Audio-Visual Attention Architectures for Calibrated Open-Domain On-Screen Sound Separation
We identify several limitations of previous work on audio-visual on-screen sound separation, including the coarse resolution of spatio-temporal attention, poor convergence of the audio separation model, limited variety in training and evaluation data, and failure to account for the trade off between preservation of on-screen sounds and suppression of off-screen sounds.
Text-Driven Separation of Arbitrary Sounds
The second model, SoundFilter, takes a mixed source audio clip as an input and separates it based on a conditioning vector from the shared text-audio representation defined by SoundWords, making the model agnostic to the conditioning modality.
CycleGAN-Based Unpaired Speech Dereverberation
Typically, neural network-based speech dereverberation models are trained on paired data, composed of a dry utterance and its corresponding reverberant utterance.
Improving Bird Classification with Unsupervised Sound Separation
This paper addresses the problem of species classification in bird song recordings.
DF-Conformer: Integrated architecture of Conv-TasNet and Conformer using linear complexity self-attention for speech enhancement
To make the model computationally feasible, we extend the Conformer using linear complexity attention and stacked 1-D dilated depthwise convolution layers.
Improving On-Screen Sound Separation for Open-Domain Videos with Audio-Visual Self-Attention
We introduce a state-of-the-art audio-visual on-screen sound separation system which is capable of learning to separate sounds and associate them with on-screen objects by looking at in-the-wild videos.
Sparse, Efficient, and Semantic Mixture Invariant Training: Taming In-the-Wild Unsupervised Sound Separation
The best performance is achieved using larger numbers of output sources, enabled by our efficient MixIT loss, combined with sparsity losses to prevent over-separation.
Self-Supervised Learning from Automatically Separated Sound Scenes
Real-world sound scenes consist of time-varying collections of sound sources, each generating characteristic sound events that are mixed together in audio recordings.
End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings
We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings.
Integration of speech separation, diarization, and recognition for multi-speaker meetings: System description, comparison, and analysis
Multi-speaker speech recognition of unsegmented recordings has diverse applications such as meeting transcription and automatic subtitle generation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
What's All the FUSS About Free Universal Sound Separation Data?
We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus for experiments in separating mixtures of an unknown number of sounds from an open domain of sound types.
Into the Wild with AudioScope: Unsupervised Audio-Visual Separation of On-Screen Sounds
For evaluation and semi-supervised experiments, we collected human labels for presence of on-screen and off-screen sounds on a small subset of clips.
Unsupervised Sound Separation Using Mixture Invariant Training
In such supervised approaches, a model is trained to predict the component sources from synthetic mixtures created by adding up isolated ground-truth sources.
Sequential Multi-Frame Neural Beamforming for Speech Separation and Enhancement
This work introduces sequential neural beamforming, which alternates between neural network based spectral separation and beamforming based spatial separation.
Improving Universal Sound Separation Using Sound Classification
Deep learning approaches have recently achieved impressive performance on both audio source separation and sound classification.
Transfer Learning From Sound Representations For Anger Detection in Speech
In this work, we train fully convolutional networks to detect anger in speech.
Differentiable Consistency Constraints for Improved Deep Speech Enhancement
Furthermore, the only previous approaches that apply mixture consistency use real-valued masks; mixture consistency has been ignored for complex-valued masks.
Sound Audio and Speech Processing
SDR - half-baked or well done?
In speech enhancement and source separation, signal-to-noise ratio is a ubiquitous objective measure of denoising/separation quality.
Sound Audio and Speech Processing
Deep Recurrent NMF for Speech Separation by Unfolding Iterative Thresholding
This interpretability also provides principled initializations that enable faster training and convergence to better solutions compared to conventional random initialization.
Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery
Recurrent neural networks (RNNs) are powerful and effective for processing sequential data.
Full-Capacity Unitary Recurrent Neural Networks
To address this question, we propose full-capacity uRNNs that optimize their recurrence matrix over all unitary matrices, leading to significantly improved performance over uRNNs that use a restricted-capacity recurrence matrix.
Ranked #25 on
Sequential Image Classification
on Sequential MNIST
Open-Ended Question Answering
Sequential Image Classification
Enhancement and Recognition of Reverberant and Noisy Speech by Extending Its Coherence
Our approach centers around using a single-channel minimum mean-square error log-spectral amplitude (MMSE-LSA) estimator proposed by Habets, which scales coefficients in a time-frequency domain to suppress noise and reverberation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
