This paper considers the blind deconvolution of multiple modulated signals/filters, and an arbitrary filter/signal. Multiple inputs s 1 , s 2 , . . . , s N =: [s n ] are modulated (pointwise multiplied) with random sign sequences r 1 , r 2 , . . . , r N =: [r n ], respectively, and the resultant inputs (s n r n ) ∈ C Q , n = [N] are convolved against an arbitrary input h ∈ C M to yield the measurements y n = (s n r n ) h, n = [N] := 1, 2, . . . , N, where , and denote pointwise multiplication, and circular convolution. Given [y n ], we want to recover the unknowns [s n ], and h. We make a structural assumption that unknown [s n ] are members of a known K-dimensional (not necessarily random) subspace, and prove that the unknowns can be recovered from sufficiently many observations using an alternating gradient descent algorithm whenever the modulated inputs s n r n are long enough, i.e, Q K N+M (to within log factors and signal dispersion/coherence parameters).To the best of our knowledge, this is the first provable result of multichannel blind deconvolution using gradient descent, and importantly under comparatively lenient structural assumptions on the convolved inputs. A neat conclusion drawn from this result is that modulation of a bandlimited signal protects it against an unknown convolutive distortion. We discuss the applications of this result in passive imaging, wireless communication in unknown environment, and image deblurring. A thorough numerical investigation of the theoretical results is also presented using phase transitions, image deblurring experiments, and noise stability plots.