python version：https://github.com/IMLHF/PHASEN-PyTorch/blob/master/phasen_torch/sepm.py
This folder contains implementations of objective quality measures (Chapter 11):
MATLAB file Description Reference
comp_snr.m Overall and segmental SNR [1]
comp_wss.m Weighted-spectral slope metric [2]
comp_llr.m Likelihood-ratio measure [3]
comp_is.m Itakura-Saito measure [3]
comp_cep.m Cepstral distance measure [4]
comp_fwseg Freq. weighted segm. SNR (fwSNRseg) [5],Chap 11
comp_fwseg_variant Frequency-variant fwSNRseg measure Chap 11
comp_fwseg_mars Frequency variant fwSNRseg measure Chap 11
based on MARS analysis
comp_pesq.m PESQ measure (narrowband) ITU-T P.862 [6]
PESQ measure (wideband) ITU-T P.862.2 [7]
composite.m A composite measure [8]
addnoise_asl.m Adds noise to the clean signal at specified SNR
based on active speech level. [9]
[snr_mean, segsnr_mean]= compSNR(cleanFile.wav, enhdFile.wav);
% where 'snr_mean' is the global overall SNR and 'segsnr_mean' is the segmental SNR.
wss_mean = comp_wss(cleanFile.wav, enhancedFile.wav);
llr_mean= comp_llr(cleanFile.wav, enhancedFile.wav);
is_mean = comp_is(cleanFile.wav, enhancedFile.wav);
cep_mean = comp_cep(cleanFile.wav, enhancedFile.wav);
fwSNRseg = comp_fwseg(cleanFile.wav, enhancedFile.wav);
[SIG,BAK,OVL] = comp_fwseg_variant(cleanFile.wav, enhancedFile.wav);
% where 'SIG' is the predicted rating of speech distortion,
% 'BAK' is the predicted rating of background noise distortion,
% 'OVL' is the predicted rating of overall quality.
[SIG,BAK,OVL] = comp_fwseg_mars(cleanFile.wav, enhancedFile.wav);
pesq_val = comp_pesq(cleanFile.wav, enhancedFile.wav);
% Only sampling frequencies of 8000 Hz or 16000 Hz are supported.
[Csig,Cbak,Covl] = composite(cleanFile.wav, enhancedFile.wav);
% where 'Csig' is the predicted rating of speech distortion,
% 'Cbak' is the predicted rating of background noise distortion,
% 'Covl' is the predicted rating of overall quality.
addnoise_asl(cleanfile.wav, noisefile.wav, outfile.wav, SNRlevel)
[1] Hansen, J. and Pellom, B. (1998). An effective quality evaluation protocol for speech enhancement algorithms. Inter. Conf. on Spoken Language Processing, 7(2819), 2822
[2] Klatt, D. (1982). Prediction of perceived phonetic distance from critical band spectra. Proc. IEEE Int. Conf. Acoust. , Speech, Signal Processing, 7, 1278-1281.
[3] Quackenbush, S., Barnwell, T., and Clements, M. (1988). Objective measures of speech quality. NJ: Prentice-Hall, Eaglewood Cliffs.
[4] Kitawaki, N., Nagabuchi, H., and Itoh, K. (1988). Objective quality evaluation for low bit-rate speech coding systems. IEEE J. Select. Areas in Comm., 6(2), 262-273.
[5] Tribolet, J., Noll, P., McDermott, B., and Crochiere, R. E. (1978). A study of complexity and quality of speech waveform coders. Proc. IEEE Int. Conf. Acoust. , Speech, Signal Processing, 586-590.
[6] ITU (2000). Perceptual evaluation of speech quality (PESQ), and objective method for end-to-end speech quality assessment of narrowband telephone networks and speech codecs. ITU-T Recommendation P.862
[7] ITU (2007). Wideband extension to Recommendation P.862 for the assessment of wideband telephone networks and speech codecs. ITU-T Recommendation P.862.2
[8] Hu, Y. and Loizou, P. (2006). Evaluation of objective measures for speech enhancement. Proc. Interspeech
[9] ITU-T (1993). Objective measurement of active speech level. ITU-T Recommendation P. 56
Copyright (c) 2012 by Philipos C. Loizou
Revision: 1.0, Date: 05/14/2012