# Target Selection Strategies for Demucs-Based Speech Enhancement
## 4 Training Methodology  
The employed training methodology is based on the one employed to train the original Demucs-Denoiser model [12]. The clean and noise recordings from the Interspeech 2020 deep noise suppression challenge [39] were utilized to create 200 h of data to be used for training, validation, and evaluation. The original data creation scripts were heavily modified to include relevant information to train both of the previously described target selection strategies. To simulate the reverberation and microphone array, we made use of the Pyroomacoustics package [40]. Several data creation parameter combinations (later described) were used to create a series of input data instances: a set of artificial recordings employed for training, validating, and evaluating both target selection strategies, representing the given parameter combination. For each input data instance, a \(4\times 6\) m room was simulated with a two-microphone array positioned in the center of the room, and the following steps were carried out to create its artificial recordings:  
1. Randomly select an inter-microphone distance between a given inter-microphone distance range of 0.05 and 0.21 m.
2. Randomly select a user \(u\) from the set of clean recordings. 3. Randomly select a recording \(r_{u}\) from user \(u\). 4. Randomly select a 5 s segment from recording \(r_{u}\) with an energy level above a given energy threshold of 0.6. 5. Establish the location \(o_{u}\) of the source of \(r_{u}\) 1 m away from the microphone array in the simulated room. 6. Randomly select a signal-to-noise ratio (SNR) between a given SNR range of 0 and 20 dB. 7. Randomly select a noise recording \(r_{n}\). 8. Randomly select a 5 s segment from recording \(r_{n}\). 9. Apply the SNR to the noise segment. 10. Randomly select a location \(o_{n}\) of the source of \(r_{n}\) in the simulated room. 11. Randomly select a number of interferences \(E\), which are other users from the clean recordings, with a maximum of 2. 12. If \(E>0\): 1. Randomly select an input signal-to-interference ratio (SIR) between a given SIR range of 0 and 20 dB. * Randomly select the users that will act as interferences \(e\), which should be different from \(u\). * For each interference \(e\):
* Randomly select a recording \(r_{e}\) from user \(e\). * Randomly select a 5 s segment from recording \(r_{e}\) with an energy level above a given energy threshold of 0.6. * Randomly select a location \(o_{e}\) of the source of \(r_{e}\) in the simulated room. * Apply the SIR to the segment of interference \(e\). * Randomly select a reverberation time \(rt_{60}\) between a given reverberation time range of 0.1 and 3.0 s.
* Apply Pyroomacoustics to simulate the environment, with all the given audio segments (clean, noise, and interferences), the result of which is a two-channel recording (one channel for each simulated microphone). * The simulated microphones are fed to the PFM filter to obtain \(X_{PFM}\). For each input data instance the following is stored:
* The clean recording \(r_{u}\). * The output of the PFM filter \(X_{PFM}\). * The first channel of the Pyroomacoustics output \(I_{1}\). * A text file with the following information:
* The location of the noise recording \(r_{u}\). * The location of all interference recordings \(r_{e}\). * The location of a randomly selected recording \(r_{u_{2}}\) of user \(u\), such that \(r_{u_{2}}\
eq r_{u}\), to create the embedding of user \(u\). * All the relevant data creation parameters (SNR, \(E\), input SIR, and \(rt_{60}\)) are stored as part of the file name of the \(X_{PFM}\) recording. * \(X_{PFM}\) is used to train the location-based target selection strategy. \(I_{1}\) and \(r_{u_{2}}\) are used to train the embedding-based target selection strategy. It is important to note that since \(r_{u_{2}}\
eq r_{u}\), the embedding is not created from \(r_{u}\) (the clean recording of user \(u\)); this hopefully provides generality in its use.