Compressive sensing in footstep sounds, hand tremors and speech using K-SVD dictionaries

Abstract

The application of Compressive Sensing is explored in three signal categories; footstep sounds, hand tremors and speech. An investigation of the reconstruction performance of various dictionaries is undertaken. It is demonstrated that these signal categories are reconstructed with higher SNR performance using K-SVD dictionaries than other fixed dictionaries. In particular, for footstep sounds and hand tremors, the K-SVD dictionaries outperform the fixed dictionaries; Discrete Cosine Transform (DCT), Wavelet Symlet with order 8 Transform and the union of DCT and Discrete Sine Transform. Moreover, in speech reconstruction, the use of a codebook of K-SVD dictionaries instead of a codebook of impulse response matrices improves performance.