# A Multi-Source Separation Approach Based on DOA Cue and DNN
## 4 Performance Evaluation
### Experiment Conditions  
Firstly, the two sound sources scene were taken as an example to elaborate on the preparation of the data set for training the DNN. From the Nippon Telegraph and Telephone (NTT) database, 200 speech segments sampled by 16 kHz, including 100 male speaker segments and 100 female speaker segments, were selected as source signals. We randomly selected two speech segments from these 200 speech segments as two sound source signals \(s_{1}\) and \(s_{2}\). Roomsim software was then used to record two source signals with reverberation, respectively [26]. The reverberant mixed signal \(s_{mix}\) was obtained by linear addition of two recorded signals in the time domain. Thereafter, the dereverberation model used the method described in Section 1. Hence, we obtained one group of training data (i.e., \(s_{1}\), \(s_{2}\) and \(s_{mix}\)). The same method was utilized to generate 400 groups of data to form the training set. Another 50 and 40 groups of data constituted the validation set and the test set, respectively. The codes of the proposed methods were written in MATLAB (R2019b version). The experiments were implemented on a desktop with an Intel i7 CPU with 2.9 GHz and 16 GB of memory without parallel processing. In the training and testing phases, a NVIDIA GeForce RTX 2060 GPU was used.  
Subsequently, the mixed speech signals for multiple-sound-source separation were generated as follows. Speech segments from NTT speech database were selected as sound source signals which were sampled at 16 kHz and included eight females and eight males. In this paper, Roomsim software was used to simulate acoustic rooms with the size of \(6.25\times 4.75\times 2.5\) m\({}^{3}\) where the sound speed was 340 m/s. The sound-field microphone was located at the center of the simulated rooms. The distance between the microphone and sound source was 1 m.  
Performance evaluation consisted of objective and subjective evaluations. In objective evaluation, the perceptual evaluation of speech quality (PESQ) [33] and the short-time objective intelligibility (STOI) [34] were utilized to measure the perceptual quality and intelligibility of separated signals, respectively. The PESQ score is between \(-0.5\) and \(4.5\). A higher PESQ score means a higher perceptual quality and a higher STOI score means higher intelligibility. The signal-to-distortion ratio (SDR) and signal-to-interference ratio (SIR) were used to measure the distortion and interference level of the separated signals, respectively. Higher SDR and SIR scores mean lower distortion and interference level, respectively. In subjective evaluation, the multiple stimuli with hidden reference and anchor (MUSRHA) measurement method was employed to further evaluate the separated speech signal quality [35]. A high MUSRHA score means better listening quality. The reference methods were the separation algorithm based on independent component analysis (ICA) [10], the separation algorithm based on expectation maximization (EM) [36], the joint sparse and non-sparse components separation method (JSNCS) [14], and the separation method based on linearly constrained minimum variance beamforming (LCMV) [9].