显式估计大小和相频谱并行用于高质量的语音增强
Ye-Xin Lu1, Yang Ai1, Zhen-Hua Ling1
1National Engineering Research Center of Speech and Language Information Processing, University of Science and Technology of China, Hefei, China.
概括
本研究介绍了MP-SENet,这是一种新的语音增强网络,可以明确改善大小和阶段光谱. MP-SENet在语音消音,脱声和带宽扩展方面取得了最先进的结果.
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:
- 阶段信息对于语音质量和可理解性至关重要.
- 现有的语音增强方法在明确的阶段估计方面扎,限制了性能.
- 阶段的非结构性和包装性对传统方法提出了挑战.
研究的目的:
- 提出一种新的语音增强网络 (MP-SENet),该网络可以同时显式增强大小和阶段光谱.
- 克服当前处理阶段信息方法的局限性,以改善语音增强.
- 在各种语音增强任务中实现最先进的表现.
主要方法:
- MP-SENet采用了转换器嵌入式编码器-解码器架构.
- 编码器处理大小和相位光谱,输入时间频变压器.
- 解码器采用并行大小掩盖和相位估计,训练有多级损失函数和度量区分器.
主要成果:
- MP-SENet在语音消音,脱声和带宽扩展方面实现了最先进的性能.
- 在MP-SENet中的明确相位估计减轻了大小相位补偿效应,提高了感知质量.
- 在VoiceBank+DEMAND上获得了3.60的PESQ评分,在语音拒绝方面获得了3.62的DNS.
结论:
- 通过有效处理阶段信息,MP-SENet在语音增强方面取得了重大进展.
- 大小和相谱的并行增强导致优越的语音质量和可理解性.
- 拟议的方法证明了在各种语言增强挑战中具有广泛的适用性.
相关概念视频
01:18Aliasing
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
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01:22Upsampling
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01:26Linear Approximation in Frequency Domain
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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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01:10Interference: Path Lengths
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Two special sources may be considered when they are in phase. This can be easily achieved by feeding the two sources from the same source. An example would be synchronizing the two speakers by feeding them with the same source, such as the sound waves produced by a tuning fork. This setup ensures that the two sources have the same frequency and are...
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01:15Sampling Theorem
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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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01:10Reconstruction of Signal using Interpolation
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