A Novel Trajectory-based Spatial-Temporal Spectral Features for Speech Emotion Recognition
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionSpeech Emotion RecognitionSimilar Papers 제목 키워드 기반
Leveraging Joint Spectral and Spatial Learning with MAMBA for Multichannel Speech Enhancement
In multichannel speech enhancement, effectively capturing spatial and spectral information across different microphones is crucial for noise reduction. Traditional methods, such as CNN or LSTM, attempt to model the tempo…
MambaSpeech EnhancementOn the Role of Spatial, Spectral, and Temporal Processing for DNN-based Non-linear Multi-channel Speech Enhancement
Employing deep neural networks (DNNs) to directly learn filters for multi-channel speech enhancement has potentially two key advantages over a traditional approach combining a linear spatial filter with an independent te…
Speech EnhancementSpeech ExtractionTemporal-Spatial Neural Filter: Direction Informed End-to-End Multi-channel Target Speech Separation
Target speech separation refers to extracting the target speaker's speech from mixed signals. Despite the recent advances in deep learning based close-talk speech separation, the applications to real-world are still an o…
Speech SeparationMcNet: Fuse Multiple Cues for Multichannel Speech Enhancement
In multichannel speech enhancement, both spectral and spatial information are vital for discriminating between speech and noise. How to fully exploit these two types of information and their temporal dynamics remains an …
Speech EnhancementInsights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement
The key advantage of using multiple microphones for speech enhancement is that spatial filtering can be used to complement the tempo-spectral processing. In a traditional setting, linear spatial filtering (beamforming) a…
Speech Enhancement