基于双路长短记忆网络的语音感知改进算法
Hyeong Il Koh1, Sungdae Na2, Myoung Nam Kim3
1Department of Medical & Biological Engineering, Graduate School, Kyungpook National University, Daegu 41944, Republic of Korea.
Bioengineering (Basel, Switzerland)
|November 25, 2023
概括
这项研究引入了一种新的双路径深度学习模型,用于语音增强. 该模型通过同时处理时间和时间频率域来提高语音质量和可理解性.
科学领域:
- 信号处理 信号处理
- 人工智能的人工智能
- 声学 声学 在声学上
背景情况:
- 深度学习语音增强通常侧重于时间频率表示.
- 传统的方法可能会导致由于特定领域的处理限制而导致语音扭曲.
研究的目的:
- 开发一种新的语音增强模型,解决单域方法的局限性.
- 通过整合时间和光谱特征提取来提高语音质量和可理解性.
主要方法:
- 设计了一个双路径深度学习网络,包括时间和时间频率领域.
- 时间路径模拟波形的语义特征,而时间频率路径细化了光谱细节.
- 长短期记忆 (LSTM) 网络被用来建模模具功能,以增强时间和光谱特征.
主要成果:
- 拟议的双路径LSTM网络与单域方法相比,表现优越.
- 在对语音质量的客观和主观评估中都观察到一致的改善.
- 增强可理解性是双路径方法的一个关键结果.
结论:
- 双路径架构有效地捕获来自不同领域的互补语音信息.
- 这个模型为语音增强挑战提供了更全面,更强大的解决方案.
- 这些发现表明了未来基于深度学习的音频处理研究有前途的方向.
关键词:
这是LSTM的LSTM.在STFT,STFT和STFT中.双通道网络的双通道网络.编码器解码器结构梅尔-过器银行合并算法 合并算法 合并算法频谱扩展区块是一个频谱扩展区块.语音增强器 语音增强器更多相关视频
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