## 4 Results and Discussion  
The experiments in order to evaluate the performance of the proposed method were implemented on the real and simulated data. The TIMIT dataset was considered for the simulated data, where the data collection MDAB0 by four continuous sentences SX139, SX229, SX319, and SX409 were selected as a male speaker in the simulations [39]. This dataset includes short sentences for testing and training the algorithms. The tones and frequency components are two different parameters in the speech signal. There are pitch and speech spectrum components for the speakers. It is important to work with male or female signals for the algorithms, which works with the pitch parameter. Since this parameter changes highly based on the gender. Since we consider the speech spectrum, then the issue to use the male or female speakers does not change the results. Therefore, 12.5 s male-speech signal is used for implementations and experiments. A voice activity detector is implemented to detect the silence part of the speech signal [40], and the noise spectrum is estimated of these parts for the proposed SBAPA. Figure 7 shows the simulated room with the location of speakers and microphone array. The inter-microphone distances \(d_{sim}=2.2\) cm for the simulated data was selected based on the designed array. A speaker and a steered noise source were considered in the simulations. The room dimensions, speaker, and noise source locations were selected as 475,592,420 cm, 374,146,110 cm, and 362,412,120 cm, respectively. These dimensions and locations were considered the same as the real room recording conditions. In addition, the proposed algorithm was implemented on real data to evaluate the real effect of the noise and reverberation on the performance. For this purpose, the real speech signal was recorded in the speech processing laboratory at Fondazione Bruno Kessler (FBK), Trento, Italy. Figure 8 shows a view of the recording room at FBK. Two electronic speakers were used instead of the human and noise source in the process of data recording. In addition, Figure 8 shows the position of the circular NMA in the center of the room. We were able to consider the minimum inter-microphone distance in the real conditions with our setup (see Figure 8) as \(d_{real}=2.7\) cm because of the microphone dimension, electronic board, and the microphone shield. Additionally, each microphone had a cross section, where in the real conditions it was about 0.7 cm. It means it is hard to measure the exact distance between two microphones and it has some errors. Since all cross sections in a microphone are areas for a sound recording, then, based on all limitations, we were forced to have this inter-microphone distance for real data implementation even with a few millimeters difference with the mathematical calculations. Therefore, the differences in the results of our proposed method for the real and simulated data were for this an issue. In the real condition, there are always some inaccuracy factors for the measurements. We found the center of the room and the microphones were located on the table based on the primary measurements. All microphones were connected to the sound recording system, which uses parallel acquisition for all microphone channels. All channel acquisitions were synchronized and there was not any delay between recorded signals in different microphones or channels. The phase error based on the recording condition was very low and was even close to zero based on the audio recording system. In the real room, the table did not make any direct reflection. All the reflected waves from the table will cross to the walls and ceiling firstly, and since all of them were covered with curtains and sound absorption panels, the indirect reflections to the microphones were very few. Both speakers were connected to the two computers for playing the speech and noise with a sampling frequency of \(F_{s}=16,000\) Hz.