Noise suppression based on approximate KLT with wavelet packet expansion

In this paper, we perform the noise suppression based on approximate Karhunen-Loeve transform (KL T). The discrete cosine transform(DCT) has been a good candidate for approximate KLT when the signal is modeled as an autoregressive process. However, for nonstationary signals, wavelet transform is more capable than DCT while approximating KLT. To calculate approximate KLT, we first represent the signal by using wavelet packet based on a basis search algorithm, then eigenvectors are evaluated from the basis. A linear estimator based on these eigenvectors can be constructed and used to perform noise reduction. We evaluate the performance of this method by using the Aurora-2 database. The SNR improvement is calculated. Some waveforms and spectrograms of enhanced speech are also shown. Finally. the enhanced speech is tested for speech recognition. These experimental results show that this method achieves satisfactory enhancement of speech.