Missing audio segment reconstruction based on sparse representation with power spectrogram

This paper presents a method for reconstructing missing audio segments based on sparse representation with power spectrogram. In the proposed method, an error of power spectrograms is utilized as a quality measure representing reconstruction performance. Then the proposed method estimates missing segments based on sparse representation optimized with respect to the error of power spectrograms. This error minimization problem can be solved with a greedy algorithm by limiting the solution to only sparse one. By using our method, perceptually optimized reconstruction becomes feasible since missing segments are estimated by using the quality measure which represents auditory properties. Experimental results obtained by applying the proposed method to actual music signals from RWC Music Database show its effectiveness.