Conv-TasNet is often presented as a neural time-domain separator, but its design is deeply rooted in acoustic signal processing: a learned analysis-synthesis filter bank replaces the STFT, masking is retained as the separation mechanism, and temporal convolutions implement structured finite-impulse-response filtering. This paper studies Conv-TasNet through singular value decomposition (SVD). For depthwise temporal convolutions, we view each filter as a Toeplitz operator, linking its singular spectrum to the filter frequency response through Szeg˝o-type theory. For pointwise 1 × 1 convolutions, we analyze the evolution of singular values and singular subspaces during training, showing rapid early spectral concentration and progressive stabilization. We then summarize a low-rank factorization scheme for the pointwise layers. On Libri2Mix, truncation near the effective rank gives the strongest compression-performance trade-off, while more aggressive truncation degrades gracefully. These results position Conv-TasNet as both an interpretable signal-processing architecture and a compressible low-rank model.