Many speech and music analysis and processing schemes rely on an estimate of the fundamental frequency f0 of periodic signal components. Most established schemes apply rather unspecific signal models such as sinusoidal models to the estimation problem, which may limit time resolution and estimation accuracy. This study proposes a novel time-domain locked-loop algorithm with low computational effort and low memory footprint for f0 estimation. The loop control signal is directly derived from the input time signal, using a harmonic signal model. Theoretically, this allows for a noise-robust and rapid f0 estimation for periodic signals of arbitrary waveform, and without the requirement of a prior frequency analysis. Several simulations with short signals employing different types of periodicity and with added wide-band noise were performed to demonstrate and evaluate the basic properties of the proposed algorithm. Depending on the Signal-to-Noise Ratio (SNR), the estimator was found to converge within 3-4 signal repetitions, even at SNR close to or below 0dB. Furthermore, it was found to follow fundamental frequency sweeps with a delay of less than one period and to track all tones of a three-tone musical chord signal simultaneously. Quasi-periodic sounds with shifted harmonics as well as signals with stochastic periodicity were robustly tracked. Mean and standard deviation of the estimation error, i.e., the difference between true and estimated f0, were at or below 1 Hz in most cases. The results suggest that the proposed algorithm may be applicable to low-delay speech and music analysis and processing.


翻译:许多言语和音乐分析及处理计划依赖于对定期信号组件基本频率f0的估算。大多数既定计划对估算问题采用相当不具体的信号模型,如类流模型等,这可能会限制时间分辨率和估计准确性。本研究提出了一个新的时间-域锁定环流算法,其计算努力低,为F0估计的记忆足迹低。循环控制信号直接来自输入时间信号,使用一个调音信号模型。理论上,这允许对任意波形的定期信号进行噪音-气流和快速F0估计,而不需要事先的频率分析。进行了一些短信号模拟,使用不同周期和增加宽频波噪音模型,以显示和评估拟议算法的基本特性。根据信号-噪音比率(SNR),估计值直接来自3-4个信号重复,甚至近于或低于0dB。此外,还发现可以跟踪基本频率扫描,延迟不到一个时期,并跟踪三度短信号的频率,使用不同周期的频率分析,同时进行宽波波波波噪音分析,以1级的频率和最精确的周期性信号在1级之间,在精确的频率和最精确的轨道上,在精确的轨道上进行。

0
下载
关闭预览

相关内容

专知会员服务
31+阅读 · 2021年6月12日
专知会员服务
25+阅读 · 2021年4月2日
专知会员服务
60+阅读 · 2020年3月19日
【Uber AI新论文】持续元学习,Learning to Continually Learn
专知会员服务
36+阅读 · 2020年2月27日
计算机视觉最佳实践、代码示例和相关文档
专知会员服务
17+阅读 · 2019年10月9日
Ray RLlib: Scalable 降龙十八掌
CreateAMind
9+阅读 · 2018年12月28日
已删除
将门创投
4+阅读 · 2018年1月19日
Arxiv
0+阅读 · 2021年9月13日
VIP会员
相关资讯
Ray RLlib: Scalable 降龙十八掌
CreateAMind
9+阅读 · 2018年12月28日
已删除
将门创投
4+阅读 · 2018年1月19日
Top
微信扫码咨询专知VIP会员