F0_extraction

所属分类:通讯编程
开发工具:matlab
文件大小:0KB
下载次数:0
上传日期:2021-02-05 20:26:42
上 传 者sh-1993
说明:  使用经验模式分解(EMD)从连续语音中提取基频(和高次谐波)波形。,
(Fundamental Frequency (and higher harmonics) waveform extraction from continuous speech using Empirical Mode Decomposition (EMD).,)

文件列表:
Autoemd.m (5330, 2021-02-05)
Data_Filtering_1500HzLowPass_8kHzFs.m (8588, 2021-02-05)
FWs/ (0, 2021-02-05)
FWs/odin_F0.mat (171223, 2021-02-05)
Filter_1500HzLowPass_8kHzFs.m (871, 2021-02-05)
Hilbert_Spectrum.m (819, 2021-02-05)
IMFcrossfader.m (9815, 2021-02-05)
IMFselector.m (3190, 2021-02-05)
LICENSE (1071, 2021-02-05)
Main.m (473, 2021-02-05)
SoundFiles/ (0, 2021-02-05)
SoundFiles/odin.wav (291102, 2021-02-05)
extractFW.m (5899, 2021-02-05)
spCorr.m (1459, 2021-02-05)
spPitchCorr.m (749, 2021-02-05)
spPitchTrackCorr.m (2658, 2021-02-05)

# Fundamental waveform extractor Fundamental Frequency (and higher harmonics) waveform extraction from continuous speech using Empirical Mode Decomposition (EMD). The code was used to obtained fundamental waveforms for modelling the auditory brainstem responses to continous speech in [1-4]. Method was originally developed in [1] and this repository serves for code maintenece, so please do feel free to report issues or suggest improvements. ### Notes: - For best results use mono float .wav files sampled at 44100 Hz. - If you come across a bug or any sort of unexpected behaviour, please open an issue on GitHub. ### References * [1] Forte, A. E., Etard, O., & Reichenbach, T. (2017). The human auditory brainstem response to running speech reveals a subcortical mechanism for selective attention. Elife, 6, e27203. https://doi.org/10.7554/eLife.27203 * [2] Etard, O.\*, Kegler, M.\*, Braiman, C., Forte, A. E., & Reichenbach, T. (2019). Decoding of selective attention to continuous speech from the human auditory brainstem response. *Neuroimage*, 200, 1-11. https://doi.org/10.1016/j.neuroimage.2019.06.029 * [3] Saiz-Alia, M., Forte, A. E., & Reichenbach, T. (2019). Individual differences in the attentional modulation of the human auditory brainstem response to speech inform on speech-in-noise deficits. *Scientific reports*, 9(1), 1-10. https://doi.org/10.1038/s41598-019-50773-1 * [4] Saiz-Alia, M., & Reichenbach, T. (2020). Computational modeling of the auditory brainstem response to continuous speech. *Journal of Neural Engineering*. https://doi.org/10.1088/1741-2552/ab970d

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