  The prevailing noise-resistant and reverberation-resistant localization
algorithms primarily emphasize separating and providing directional output for
each speaker in multi-speaker scenarios, without association with the identity
of speakers. In this paper, we present a target speaker localization algorithm
with a selective hearing mechanism. Given a reference speech of the target
speaker, we first produce a speaker-dependent spectrogram mask to eliminate
interfering speakers' speech. Subsequently, a Long short-term memory (LSTM)
network is employed to extract the target speaker's location from the filtered
spectrogram. Experiments validate the superiority of our proposed method over
the existing algorithms for different scale invariant signal-to-noise ratios
(SNR) conditions. Specifically, at SNR = -10 dB, our proposed network LocSelect
achieves a mean absolute error (MAE) of 3.55 and an accuracy (ACC) of 87.40%.
