使用各种波形系数提高语音分离性能.
Rawad Melhem1, Oumayma Al Dakkak1, Assef Jafar1
1Communication Department, Higher Institute for Applied Sciences and Technology, Damascus, Syria.
The Journal of the Acoustical Society of America
|July 8, 2025
概括
波形系数在杂的环境中显著增强了语音分离模型. 整合波纹散射系数提高了精度,为具有挑战性的声条件提供了强大的解决方案.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 由于特征扭曲,语音分离性能在现实世界的噪音条件下下降.
- 传统的语音分离功能在实际声学环境中缺乏稳定性.
- 波形变换 (WT) 显示了语音分离的超越分类的潜力.
研究的目的:
- 探索波形系数在改进语音分离模型中的有效性.
- 评估离散波量和波量数据包对语音分离的影响.
- 为了将波纹散射 (WS) 系数整合到语音分离中,尽管缺乏确切的反向变换.
主要方法:
- 将离散波段和波段包集成到模型训练中.
- 将波纹散射系数纳入损失函数以解决逆变换限制.
- 使用尺度不变信号与扭曲比率,平均意见得分和短期客观可理解性等指标评估模型性能.
主要成果:
- 基于波纹的模型在噪音条件下表现出卓越的性能和弹性.
- 整合WS系数显著提高了语音分离的准确性.
- 基于波形的方法在关键客观和主观语音质量指标上优于其他方法.
结论:
- 波形系数在具有挑战性的声学环境中有效提高语音分离.
- 波纹散射集成提供了一种新的方法来提高分离精度.
- 波形系数代表了强大的语音分离的最先进解决方案.
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