Binaural signal synthesis is typically formulated as forward modelling using head-related transfer functions (HRTFs). We explore an inverse auditory modelling perspective in which binaural ear signals are estimated directly from a source signal and its azimuth. We present a lightweight complex-valued neural network that predicts frequency-domain binaural filters from the input source spectrum and azimuthal direction, which are then applied to synthesize binaural signals. Controlled experiments evaluate how excitation bandwidth and angular sampling density affect reconstruction and generalization. Results show accurate spectral reconstruction and interpolation to unseen source directions even when training uses sparse angular grids, while bandwidth strongly influences problem conditioning and error behaviour. This work focuses on characterizing compact signal-conditioned inverse models as efficient components for binaural signal generation.
