## 4 Computer Simulation Results  
### Experimental Dataset  
#### 4.1.1 Speech Source Parameters  
In order to evaluate the effectiveness of the proposed methodology, in this work we have constructed a new dataset of simulated speech sources in motion under noisy, reverberant conditions. Speech signals corresponding to the sources have been selected from the WSJ0 mix [46] and LibriSpeech [47] speech corpuses, while the noise samples were selected from CHiME-3 [48], the diverse environments multichannel acoustic noise database (DEMAND) [49] and environment sound classification (ESC-50) [50] databases. The signals from WSJ0 mix and CHiME-3 are randomly selected and mixed for SNRs between 2 dB and 10 dB. The selected noise samples from the DEMAND database correspond to noise signals recorded in diverse acoustic environments such as a busy subway station, an office cafeteria, and a university restaurant. Fifteen noises were selected for training and validation sets, whereas three noises were selected for the testing set. The SNR selected for training and validation sets were randomly sampled between \(-5\) dB and 0 dB. However, for the testing set, different SNR values of \(-5\) dB, 0 dB and 5 dB were used. ESC-50 is a dataset for environment sound samples, one which contains a total of 2000 environmental sound recordings organized into 5 primary classes, namely animal, natural, human, interior/domestic and exterior. The dataset is labelled with 50 classes and is also available with noise added to the audio signals. Therefore, the ESC-50 dataset also provides a plethora of sound waveforms which can be added to speech waveforms in the indoor environment to illustrate the target speech waveform enhancement task. In this work, a training set comprising 7000 speech mixtures was created for training the proposed neural architecture, whereas a validation set comprising 1000 speech mixture signals for validation and a testing set comprising 1000 speech signal mixtures for testing the proposed neural architecture were also created. MATLAB 2024b is used to generate the reverberant speech signals, whereas Tensorflow 2.11 is used with Python version 3.9 to train and test the proposed neural approach. The signals were sampled at 16 KHz. In addition, a Hammingwindow of a duration of 20 ms was utilized to segment the waveforms into a set of time frames, whereby an overlap of 50% between adjacent frames is applied. The size of the DFT applied to each time frame of received speech signal mixture is therefore of a length of 320 points. The room impulse response (RIR) between the source speaker and the receiving microphone array is generated using the ISM [55] approach. The acoustic scene parameters used for this simulation are tabulated in Table 1. For each simulated RIR, the room geometry, array position and start and end coordinates of the source speakers are randomly selected. Similar parameters of acoustic scenes and microphone array configurations have also been considered in [6; 26; 31]. However, this work involves a DL-based combined approach of tracking and enhancement, and the DL parameters used in this work are mentioned in Section 4.3. The speech signals corresponding to the 3 speakers are simulated inside a room with the dimensions of 10 m by 18 m by 6 m, where the reverberation times denoted by RT60 are considered as 50 ms, 250 ms and 550 ms, respectively. During the training phase, RT60 values of 50 ms, 250 ms and 550 ms are chosen, whereas during the testing phase, RT60 values of 550 ms are chosen to test the performance of the DL architecture. A single microphone array with 6 sensors is simulated. The maximum number of speakers chosen is 3. The RIRs were generated using ISM [54] at the above-mentioned reverberation times. The sampling frequency is taken to be 16,000 Hz. In the simulation setting, a maximum number of 3 speakers is selected, and these are placed in continuous motion inside the room.