Then, the Swin-Transformer is employed to capture hierarchical multi-scale features, where a window attention is designed to grasp dynamic time鈥揻requency features. Furthermore, to enhance the extraction of contextual information from the spectrogram, a frame-level shifted w...
3D Swin Transformer ECoG解码器:基于Swin Transformer,这是一种有效的注意力机制模型,它通过在小窗口内进行自注意力计算来降低计算复杂性。研究者将Swin Transformer扩展到3D,以在3D空间中进行局部自注意力计算。 LSTM ECoG解码器:使用了长短期记忆网络(LSTM)来处理ECoG信号。LSTM特别适合处理和记忆时间序列数据,这对于...
Speech emotion recognition (SER) has gained an increased interest during the last decades as part of enriched affective computing. As a consequence, a variety of engineering approaches have been developed addressing the challenge of the SER problem, exploiting different features, learning algorithms, an...
Swin Transformer Large FP16 - Supported INT8 - - VGG16 FP16 Supported Supported INT8 Supported - Wide ResNet50 FP16 Supported Supported INT8 Supported Supported Detection ModelsPrecisionIGIEIxRT ATSS FP16 Supported - INT8 - - CenterNet FP16 Supported Supported INT8 - - DETR FP16 - Supporte...
HuBERT 是一个自监督模型,它通过对模型的中间表示应用 k-means 聚类来为掩蔽的音频片段预测离散标签进行学习。它结合了 1-D 卷积层和一个 Transformer 编码器,将语音编码为连续的中间表示,然后使用 k-means 模型将这些表示转换为一系列聚类索引的序列。随后,相邻的重复索引被移除,得到表示为 ...
Built for enterprise-grade performance, Primus uses state-of-the-art pretrained transformer models like CLIP, WavLM, GPT-2, and Swin Transformer V2, enabling seamless fusion of multimodal inputs and robust action generation. Key Features Multimodal Inputs: Integrates text, speech, vision, and vide...
提出HIFI-gan方法来提高采样和高保真度的语音合成。语音信号由很多不同周期的正弦信号组成,对于音频周期模式进行建模对于提高音频质量至关重要。其次生成样本的速度是其他同类算法的13.4倍,并且质量还很高。 前言 主流的语音合成大部分分为两个阶段:1)预测低分辨率的中间表示,例如梅尔声谱图或语言特征,从中间表示合成原始...
Additionally, we harness the power of the Swin transformer for visual classification, trained on a dataset of 14 million annotated images from ImageNet. The pre-trained scores from the Swin transformer are utilized as input for the deep bidirectional long short-term memory with gated recurrent ...
(2) A multilevel Swin-transformer is introduced to improve image representation. (3) A hierarchical iterative generator is introduced to improve speech generation. (4) A flash attention mechanism is introduced to improve computational efficiency. Many experiments have indicated...
Speech enhancementU-NetSwin-transformerDeep learningenhancement performance has improved significantly with the introduction of deep learning models, especially methods based on the Long–Short-Term Memory architecture. However, these ...doi:10.1007/s00034-024-02736-9ZhangZipeng...