Proposed Integration Algorithm to Optimize the Separation of Audio Signals Using the ICA and Wavelet Transform

San Juan, Enrique; Dehghan Firoozabadi, Ali; Soto, Ismael; Adasme, Pablo; Cañete, Lucio

In the present work, an integration of two combined methodologies is developed for the blind separation of mixed audio signals. The mathematical methodologies are the independent component analysis (ICA) and the discrete Wavelet transform (DWT). The DWT optimizes processing time by decreasing the amount of data, before that signals are processed by ICA. A traditional methodology for signal processing such as Wavelet is combined with a statistical process as ICA, which assumes that the source signals are mixed and they are statistically independent of each other. The problem refers to very common situations where the human being listens to several sound sources at the same time. The human brain being able to pay attention to the message of a particular signal. The results are very satisfactory, effectively achieving signal separation, where only a small background noise and a attenuation in the amplitude of the recovered signal are noticed, but that nevertheless the signal message is identified in such a way.