Why Automatic Filter Optimization Often Fails in Real DSP Systems
Introduction Modern DSP tools increasingly rely on automated optimization to design filters. By minimizing spectral error or maximizing attenuation, algorithms attempt to generate “optimal” responses. In practice, these filters frequently fail after deployment. Common symptoms include instability, excessive distortion, numerical fragility, and unpredictable behavior across operating conditions. This article explains why blind optimization fails in real DSP systems and why engineering constraints are essential for deployable design. Optimization Ignores Physical and Numerical Limits Most optimization algorithms treat filter coefficients as continuous variables. ...