If a signal-processing engineer applies singular value decomposition (SVD) to a matrix representing an audio signal, which consequence follows regarding noise reduction?

💡 Explanation

SVD decomposes the matrix into singular values representing signal strength; smaller singular values, often associated with noise, can be zeroed or reduced. Therefore, noise is suppressed selectively rather than amplified, because SVD allows targeted filtering.

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