xiaohanghang / supervised_two_speaker

Supervised Two Speaker Separation

Supervised Two Speaker Separation

This program implements the cochannel speech separation algorithm described in K. Hu and D. L. Wang (2013), "An iterative model-based approach to cochannel speech separation," EURASIP Journal on Audio, Speech, and Music Processing, vol. 2013, Article ID 2013-14.

The MATLAB program run/twoSpeaker.m is a wrapper including several related model-based algorithms. The core separation algorithms are written in C++ under folder “c”.

Usage

rmask = twoSpeaker(sig, sid, type, nGau, bW, snr_criterion, nStep, workFolder)

Inputs

• sig: Input time-domain cochannel speech signal
• sid: Two speaker identities (sid(1) and sid(2))
• type:
  • 'acoustDym_iter': The iterative model-based algorithm
  • 'ReddyRaj': Reddy & Raj''07 (training and test energy levels must match)
  • 'MMSE': Minimum mean square estimation
  • 'MAP': Maximum a posteriori estimation
  • 'acoustDym': With temporal dynamics
  • 'MMSE_iter': MMSE + iterative estimation
  • 'MAP_iter': MAP + iterative estimation
• nGau: Number of Gaussians in GMM (use 256 in this code)
• bW: Beam width in a Viterbi search (use 16; only used in HMM-based algorithms)
• snr_criterion: A threshold (in dB) on the absolute SNR difference to stop iterative estimation (use 0.5)
• nStep: Maximum number of iterations (make sure iterative estimation will stop)
• workFolder: Folder storing temporary files

Outputs

• rmask: Estimated soft masks for two speaker (mask{1} and mask{2})

The two ratio masks will also be output as ascii files: ratio_mask_1.ascii and ratio_mask_2.ascii

Run an example in Linux

cd ./run; ./run_example.sh

or

cd run; matlab;
% Run followling commands in MATLAB
>> sig = load('sample/t11_lwby6p_m30_lrwp2a.-9dB.val2');
>> rmask = twoSpeaker(sig,  [11,30],  'acoustDym_iter',  256,  16,  .5,  3,  '.');

Run related model-based methods:

% First load a signal: 
>> sig = load('sample/t11_lwby6p_m30_lrwp2a.-9dB.val2');

Then choose one of the following:

• Reddy & Raj'07 (Training and test signal levels must match): rmask = twoSpeaker(sig, [11,30], 'ReddyRaj07', 256, -1, -1, -1, '.'); (-1 means no parameter needed)
• MMSE: rmask = twoSpeaker(sig, [11,30], 'MMSE', 256, -1, -1, -1, '.');
• MAP: rmask = twoSpeaker(sig, [11,30], 'MAP', 256, -1, -1, -1, '.');
• MAP + acoustic dynamics: rmask = twoSpeaker(sig, [11,30], ‘acoustDym’, 256, 16, -1, -1, '.');
• MMSE + iterative: rmask = twoSpeaker(sig, [11,30], 'MMSE_iter', 256, -1, .5, 3, '.');
• MAP + iterative: rmask = twoSpeaker(sig, [11,30], 'MAP_iter', 256, -1, .5, 3, '.');

Notes:

• Speakers in this program come from the Speech Separation Challenge (SSC) corpus. Speaker identities are numbered from 1-34 following the definition in the SSC corpus. For example, sid=[1,2] means the target is speaker 1 and interferer is speaker 2
• This implementation is in the log-cochleagram domain using 128-channel gammatone filterbank
• Sampling frequency is 16 kHz