We introduce PHAST-Net, an attention-guided, physics-informed network for unified estimation of Ideal Time-Frequency Representations (ITFRs), spanning spectral, tempo-based, metrical, and harmonic representations such as Spectrograms, Tempograms, and Metrograms. PHAST-Net learns a mapping from a constellation of wavelet transforms, specifically the proposed Continuous Log-frequency Adaptive Wavelet Transform (CLAWT), to high-resolution, cross-term-suppressed time-frequency representations. The proposed constellation of CLAWTs is strategically selected through Wigner-Ville kernel analysis to provide orientation-diverse coverage in a log-frequency time-frequency plane tailored to harmonic signal structure. PHAST-Net further incorporates a proposed physics-informed auxiliary reprojection loss designed to match the CLAWT-domain reprojections of the predicted and target ITFRs using the corresponding CLAWT kernels during training. This auxiliary objective promotes transform and energy-distribution consistency, mitigates target sparsity, and regularises optimisation. Attention layers further promote effective cross-term suppression across the input constellation. The log-frequency formulation also enables Harmonic PHAST-Net, which estimates a Harmonic ITFR that isolates fundamental structure, supporting robust fundamental-only representations for speech and music, such as derived fundamental Tempograms and Metrograms. We further introduce Spline-PHAST-Net, which parameterises detected and associated time-frequency ridges as continuous spline trajectories, enabling arbitrary-grid re-rendering and signal reconstruction. Trained on a procedurally generated dataset, PHAST-Net demonstrates improved accuracy over representative time-frequency analysis methods, providing a unified framework for high-resolution, cross-term-robust analysis of speech, music, and broader nonstationary signals.

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