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The availability of coarse-resolution cost-effective optical spectrum analyzers (OSAs) allows their widespread deployment in operators’ networks. In this paper, we explore several machine learning approaches for soft-failure detection, identification, and localization that take advantage of OSAs. In particular, we present three different solutions for the two most common filter-related soft-failures: filter shift and tight filtering, which noticeably deform the expected shape of the optical spectrum. However, filter cascading is a key challenge as it affects the shape of the optical spectrum similarly to the tight filtering; the approaches are specifically designed to avoid the misclassification of properly operating signals when normal filter cascading effects are present. The proposed solutions are as follows: First, the multi-classifier approach, which uses features extracted directly from the optical spectrum; second, the single …