Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement

A significant and often unavoidable problem in bioacoustic signal processing is the presence of background noise due to an adverse recording environment. This paper proposes a new bioacoustic signal enhancement technique which can be used on a wide range of species. The technique is based on a perceptually scaled wavelet packet decomposition using a species-specific Greenwood scale function. Spectral estimation techniques, similar to those used for human speech enhancement, are used for estimation of clean signal wavelet coefficients under an additive noise model. The new approach is compared to several other techniques, including basic bandpass filtering as well as classical speech enhancement methods such as spectral subtraction, Wiener filtering, and Ephraim–Malah filtering. Vocalizations recorded from several species are used for evaluation, including the ortolan bunting (Emberiza hortulana), rhesus monkey (Macaca mulatta), and humpback whale (Megaptera novaeanglia), with both additive white Gaussian noise and environment recording noise added across a range of signal-to-noise ratios (SNRs). Results, measured by both SNR and segmental SNR of the enhanced wave forms, indicate that the proposed method outperforms other approaches for a wide range of noise conditions

EMD-based filtering (EMDF) of low-frequency noise for speech enhancement

An Empirical Mode Decomposition based filtering (EMDF) approach is presented as a post-processing stage for speech enhancement. This method is particularly effective in low frequency noise environments. Unlike previous EMD based denoising methods, this approach does not make the assumption that the contaminating noise signal is fractional Gaussian Noise. An adaptive method is developed to select the IMF index for separating the noise components from the speech based on the second-order IMF statistics. The low frequency noise components are then separated by a partial reconstruction from the IMFs. It is shown that the proposed EMDF technique is able to suppress residual noise from speech signals that were enhanced by the conventional optimallymodified log-spectral amplitude approach which uses a minimum statistics based noise estimate. A comparative performance study is included that demonstrates the effectiveness of the EMDF system in various noise environments, such as car interior noise, military vehicle noise and babble noise. In particular, improvements up to 10 dB are obtained in car noise environments. Listening tests were performed that confirm the results

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Speech Signal Enhancement through Adaptive Wavelet Thresholding

This paper demonstrates the application of the Bionic Wavelet Transform (BWT), an adaptive wavelet transform derived from a non-linear auditory model of the cochlea, to the task of speech signal enhancement. Results, measured objectively by Signal-to-Noise ratio (SNR) and Segmental SNR (SSNR) and subjectively by Mean Opinion Score (MOS), are given for additive white Gaussian noise as well as four different types of realistic noise environments. Enhancement is accomplished through the use of thresholding on the adapted BWT coefficients, and the results are compared to a variety of speech enhancement techniques, including Ephraim Malah filtering, iterative Wiener filtering, and spectral subtraction, as well as to wavelet denoising based on a perceptually scaled wavelet packet transform decomposition. Overall results indicate that SNR and SSNR improvements for the proposed approach are comparable to those of the Ephraim Malah filter, with BWT enhancement giving the best results of all methods for the noisiest (−10 db and −5 db input SNR) conditions. Subjective measurements using MOS surveys across a variety of 0 db SNR noise conditions indicate enhancement quality competitive with but still lower than results for Ephraim Malah filtering and iterative Wiener filtering, but higher than the perceptually scaled wavelet method

A New Wavelet Denoising Method for Noise Threshold

A new method is used wavelet 1-D experimental signal for denoising. It is provided the optimal adaptive threshold of sub-band based on input signals. The new method: 1) use a new method with low complexity that calculates thresholds; 2) use threshold for each sub-bands; 3) divide three sub-band with range of human hearing and range of the hearing tests are often displayed in the form of an audiogram; 4) use a new denoising algorithm depends on attribute of signal for wavelet coefficients; 5) applies denoising to the detail coefficients. The new method called Adaptive Thresholding with Mean for hybrid Denoising method of hard and soft function (ATMDe) and applied to hearing loss and it is found that it increases the signal-to-noise ratio by more than 114 % and decreases the mean-square-error (MSE). The result of new method with SNR and MSE is higher than standard denoising methods. Hence, the new method was found that has good performance and adaptive threshold value is better than other methods.This study is proposed a new adaptive threshold based on noisy speech for each sub-bands with low complex and it is suitability for range of human hearing and range of hearing test. A new method is used wavelet 1-D experimental signal for denoising. It provided the optimal adaptive threshold of three sub-band with applies to the detail coefficients. The speech enhancement is used of threshoding on the adpated wavelet coefficients, and the results are compared a variety of noisy speech and four well-known benchmark signals. The results, measured objectively by Signal-to-Noise ratio (SNR) and Mean Square Error (MSE), are given for additive white Gaussian noise as well as two different types of noisy environment. The new method called Adaptive Thresholding with Mean for hybrid Denoising method of hard and soft function (ATMDe) and applied to hearing loss and it is found that it increases the signal-to-noise ratio by more than 114% and decreases the mean-square-error (MSE). The result of new method with SNR and MSE is higher than standard denoising methods. Hence, the new method was found that has good performance and adaptive threshold value is better than other methods