使用代码书进行扬声器适应,集成深度神经网络以增强语音
B Chidambar1, D Hanumanth Rao Naidu1
1Department of Mathematical and Computational Sciences, Sri Sathya Sai University for Human Excellence, Karnataka, Indiachidambar.b@sssuhe.ac.in, hanumanth@sssuhe.ac.in.
JASA express letters
|November 5, 2024
概括
一个新的编码集成深度神经网络 (CI-DNN) 通过减少训练和测试条件之间的不匹配来改善语音增强. 这种方法比扬声器独立的深度神经网络提供了更高的性能,以获得更清晰的音频.
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 在声学方面
背景情况:
- 深度神经网络 (DNN) 在语音增强方面表现出色,特别是在非静止噪声方面.
- DNN的一个主要限制是由于训练测试条件不匹配而导致的性能下降.
- 传统的方法与复杂的,非静止的噪声环境作斗争.
研究的目的:
- 为了引入一个新的编码集成深度神经网络 (CI-DNN) 强大的语音增强.
- 为了解决基于DNN的语音增强中条件不匹配引起的性能妥协.
- 提高DNN在各种声学场景中的适用性和性能.
主要方法:
- 通过将适应扬声器的代码库与DNN架构集成,开发了一个CI-DNN.
- CI-DNN作为后处理步骤,与各种DNN模型兼容.
- 在语音增强任务中对语音独立的DNN进行评估.
主要成果:
- 该CI-DNN方法显示显著改善了语音增强性能.
- 提出的方法有效地减轻了培训和测试条件之间的不匹配.
- 与扬声器独立的DNN相比,CI-DNN在降低噪音和语音清晰度方面取得了更好的结果.
结论:
- CI-DNN是提高语音增强性能的一种有效技术.
- 将适合扬声器的代码库与DNN集成成功地解决了条件不匹配问题.
- CI-DNN提供了一种适用于各种语音增强挑战的多功能和高性能解决方案.
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