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Found 20 papers, 2 papers with code
Exploiting the compressed spectral loss for the learning of the DEMUCS speech enhancement network
In this study, we present revising the STFT loss in DEMUCS by employing the compressed magnitude spectrogram.
A Preliminary Study of the Application of Discrete Wavelet Transform Features in Conv-TasNet Speech Enhancement Model
Nowadays, time-domain features have been widely used in speech enhancement (SE) networks like frequency-domain features to achieve excellent performance in eliminating noise from input utterances.
Employing low-pass filtered temporal speech features for the training of ideal ratio mask in speech enhancement
Preliminary experiments conducted on a subset of TIMIT corpus reveal that the proposed method can make the resulting IRM achieve higher speech quality and intelligibility for the babble noise-corrupted signals compared with the original IRM, indicating that the lowpass filtered temporal feature sequence can learn a superior IRM network for speech enhancement.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Cross-domain Single-channel Speech Enhancement Model with Bi-projection Fusion Module for Noise-robust ASR
In recent decades, many studies have suggested that phase information is crucial for speech enhancement (SE), and time-domain single-channel speech enhancement techniques have shown promise in noise suppression and robust automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
TENET: A Time-reversal Enhancement Network for Noise-robust ASR
Due to the unprecedented breakthroughs brought about by deep learning, speech enhancement (SE) techniques have been developed rapidly and play an important role prior to acoustic modeling to mitigate noise effects on speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Improving Speech Enhancement Performance by Leveraging Contextual Broad Phonetic Class Information
Experimental results from speech denoising, speech dereverberation, and impaired speech enhancement tasks confirmed that contextual BPC information improves SE performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Incorporating Broad Phonetic Information for Speech Enhancement
In noisy conditions, knowing speech contents facilitates listeners to more effectively suppress background noise components and to retrieve pure speech signals.
Time-Domain Multi-modal Bone/air Conducted Speech Enhancement
Previous studies have proven that integrating video signals, as a complementary modality, can facilitate improved performance for speech enhancement (SE).
Distributed Microphone Speech Enhancement based on Deep Learning
Speech-related applications deliver inferior performance in complex noise environments.
Speech Enhancement Based on Reducing the Detail Portion of Speech Spectrograms in Modulation Domain via Discrete Wavelet Transform
The presented DWT-based SE method with various scaling factors for the detail part is evaluated with a subset of Aurora-2 database, and the PESQ metric is used to indicate the quality of processed speech signals.
