## 4 Performance Evaluation and Result Analysis
### Speech Quality Analysis  
The proposed DCGOSM-based speech enhancement technique was compared with four baseline methods: OMP [18], CoSaMP [19], StOMP [38], and the K-SVD-based CS technique (K-SVDCS) [63] (note: this technique was only compared for babble noise, as the paper only provided the results for this noise). Additionally, it was compared with some other methods based on human perception-related objective functions, such as DNN-MSE [64], DNN-PMSEQ [65], BLSTM-MSE [64], BLSTM-PMSQE [65], and DNN-Quality-Net [64]. To evaluate the performance of the proposed algorithm, the NOISEX-92 [66] and NOIZEUS [67] datasets were used. The clean speech samples were taken from the NOIZEUS dataset, which contains 30 clean speech files (sp01.wav to sp30.wav) with a single channel (mono), 16-bit PCM, sampled at 8 kHz, and encoded in the Windows \"wav\" format. The noise samples were taken from the NOISEX-92, which contains white noise, babble, and  
Figure 8: Column contributions for speech components (blue bars) and noise components (red bars) in the non-optimized sensing matrix. Figure 10: Sensing matrix optimization convergence plot showing global best (red) and average (blue) fitness value for each iteration. Figure 9: Column contributions for speech components (blue bars) and noise components (red bars) in the optimized sensing matrix. f-16 noise files with a single channel sampled using a 16-bit analog-to-digital converter (A/D) at a sampling rate of 19.98 kHz. These samples were encoded in the MATLAB \"mat\" format. However, for the experimental analysis, we downsampled all signals to 8 kHz. The clean speech signal was mixed with one of the noise signals at a time to evaluate the performance of the proposed technique. The amplitude of the noise was modified before mixing according to the required SNR. The noisy speech was then processed through the enhancement algorithm to obtain the enhanced speech. This process is depicted in Figure 11, which shows the time domain plots of the \"sp05.wav\" clean speech sample, the \"babble\" noise, and the noisy speech signal generated by corrupting the clean speech signal with babble noise, and their respective spectrogram plots (Figure 11b,d,f). The blue bars in the spectrogram of the enhanced speech (Figure 11f) depict the events where VAD detected non-speech components. These bars were mostly synchronized with the silence events in the clean speech (Figure 11a), which demonstrate that the VAD is capable of detecting non-speech signals even in the presence of noise (Figure 11c). Figure 11: Plots for (**a**) clean speech, (**c**) noisy speech with 5 dB SNR corrupted by babble noise, (**e**) speech samples enhanced by the proposed algorithm with their respective spectrogram plots in (**b**,d,f). Finally, to compare the performance of each algorithm, four objective measures, the signal-to-noise ratio (SNR), segmental SNR (SSNR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ), were utilized. The performance comparison of the proposed technique with different variants of the OMP algorithm for white, babble, and f-16 noises is presented in Tables 1-3, respectively. These results demonstrate the superior performance of the proposed technique over competitors for SNR, SSNR, and PESQ, irrespective of the noise type and magnitude. However, for STOI, the proposed algorithm performed better under low SNR conditions. A further in-depth analysis of these tables showed that the proposed technique achieved the highest SNR improvement of 42.54% for f-16 noise, the second-highest improvement of 21.56% for babble noise, and the third-highest improvement of 11.03% for white noise at 0 dB of SNR. The minimum improvement of 0.41% was achieved for babble noise at 20 dB. Similar behavior was observed for SSNR; however, at 0 dB of SNR, the higher improvements were 62.97%, 28.80%, and 26.84% for f-16, babble, and white noise, respectively. White noise achieved the lowest improvement of 2.77% at 20 dB.