Subband Information Research Articles

Wavelet Deconvolution to Improving Log Vertical Resolution

Improving log vertical resolution is the principal pathway to enhance identification capability for the thin-layers. This paper introduces the method which is based on the wavelet transform and the deconvolution to improve log resolution. First, gain the sub-band information on the different scales after logging curve is decomposed by wavelet. Then the detailed coefficients on the third scale are processed with deconvolution filtering, and the detailed coefficients on the first scale are dealt with threshold denoising. At last, reconstruct the logging curve whose resolution is improved. The processed results of two wells’ logs indicate that the log vertical resolution has been improved obviously using this method, which can satisfy the demands of thin-layer evaluation.

Wordless Sounds: Robust Speaker Diarization Using Privacy-Preserving Audio Representations

This paper investigates robust privacy-sensitive audio features for speaker diarization in multiparty conversations: i.e., a set of audio features having low linguistic information for speaker diarization in a single and multiple distant microphone scenarios. We systematically investigate Linear Prediction (LP) residual. Issues such as prediction order and choice of representation of LP residual are studied. Additionally, we explore the combination of LP residual with subband information from 2.5 kHz to 3.5 kHz and spectral slope. Next, we propose a supervised framework using deep neural architecture for deriving privacy-sensitive audio features. We benchmark these approaches against the traditional Mel Frequency Cepstral Coefficients (MFCC) features for speaker diarization in both the microphone scenarios. Experiments on the RT07 evaluation dataset show that the proposed approaches yield diarization performance close to the MFCC features on the single distant microphone dataset. To objectively evaluate the notion of privacy in terms of linguistic information, we perform human and automatic speech recognition tests, showing that the proposed approaches to privacy-sensitive audio features yield much lower recognition accuracies compared to MFCC features.

MULTIORIENTATION-BASED MULTISTRUCTURE MORPHOLOGICAL INPAINTING

A multiorientation-based multistructure morphological inpainting for the recovery of damaged digitized photographs is proposed. As the inpainting order plays a vital role for human visualization, the method is guided by the orientation of edges at the surrounding known regions of the missing (spoiled) domain. The damaged picture is decomposed into its constituent orientation subbands by steerable filters. The subband information is used for reconstructing the regions within the missing part at a particular orientation, as well as for guiding the integration of the reconstructed regions. Subbands having response at the boundary of the missing domain are named as constructive subbands. The damaged regions are morphologically eroded using the structuring elements of corresponding orientations that of the constructive subbands. The resultant image is obtained using winner-takes-all integration. The novelty of our approach is to explicitly specify the direction of filling thereby ensuring ease in convergence in different orientations and then streamlining the integration process to guarantee complete and natural look. Implementation of region-filling through morphological erosion, a noniterative and nonsampling process, makes the method faster than many other traditional texture synthesis inpainting algorithms and it successfully recovers images with better peak signal to noise ratios and structural similarity index even for massive damages.

Enhancing P300 wave of BCI systems via negentropy in adaptive wavelet denoising

Brian Computer Interface (BCI) is a direct communication pathway between the brain and an external device. BCIs are often aimed at assisting, augmenting or repairing human cognitive or sensory-motor functions. EEG separation into target and non-target ones based on presence of P300 signal is of difficult task mainly due to their natural low signal to noise ratio. In this paper a new algorithm is introduced to enhance EEG signals and improve their SNR. Our denoising method is based on multi-resolution analysis via Independent Component Analysis (ICA) Fundamentals. We have suggested combination of negentropy as a feature of signal and subband information from wavelet transform. The proposed method is finally tested with dataset from BCI Competition 2003 and gives results that compare favorably.

Texture Classification Using Refined Histogram

In this correspondence, we propose a novel, efficient, and effective Refined Histogram (RH) for modeling the wavelet subband detail coefficients and present a new image signature based on the RH model for supervised texture classification. Our RH makes use of a step function with exponentially increasing intervals to model the histogram of detail coefficients, and the concatenation of the RH model parameters for all wavelet subbands forms the so-called RH signature. To justify the usefulness of the RH signature, we discuss and investigate some of its statistical properties. These properties would clarify the sufficiency of the signature to characterize the wavelet subband information. In addition, we shall also present an efficient RH signature extraction algorithm based on the coefficient-counting technique, which helps to speed up the overall classification system performance. We apply the RH signature to texture classification using the well-known databases. Experimental results show that our proposed RH signature in conjunction with the use of symmetrized Kullback-Leibler divergence gives a satisfactory classification performance compared with the current state-of-the-art methods.

Spherical Coding Algorithm for Wavelet Image Compression

In recent literature, there exist many high-performance wavelet coders that use different spatially adaptive coding techniques in order to exploit the spatial energy compaction property of the wavelet transform. Two crucial issues in adaptive methods are the level of flexibility and the coding efficiency achieved while modeling different image regions and allocating bitrate within the wavelet subbands. In this paper, we introduce the "spherical coder," which provides a new adaptive framework for handling these issues in a simple and effective manner. The coder uses local energy as a direct measure to differentiate between parts of the wavelet subband and to decide how to allocate the available bitrate. As local energy becomes available at finer resolutions, i.e., in smaller size windows, the coder automatically updates its decisions about how to spend the bitrate. We use a hierarchical set of variables to specify and code the local energy up to the highest resolution, i.e., the energy of individual wavelet coefficients. The overall scheme is nonredundant, meaning that the subband information is conveyed using this equivalent set of variables without the need for any side parameters. Despite its simplicity, the algorithm produces PSNR results that are competitive with the state-of-art coders in literature.

Two-dimensional filter bank design for optimal reconstruction using limited subband information

In this correspondence, we propose design techniques for analysis and synthesis filters of 2-D perfect reconstruction filter banks (PRFB's) that perform optimal reconstruction when a reduced number of subband signals is used. Based on the minimization of the squared error between the original signal and some low-resolution representation of it, the 2-D filters are optimally adjusted to the statistics of the input images so that most of the signal's energy is concentrated in the first few subband components. This property makes the optimal PRFB's efficient for image compression and pattern representations at lower resolutions for classification purposes. By extending recently introduced ideas from frequency domain principal component analysis to two dimensions, we present results for general 2-D discrete nonstationary and stationary second-order processes, showing that the optimal filters are nonseparable. Particular attention is paid to separable random fields, proving that only the first and last filters of the optimal PRFB are separable in this case. Simulation results that illustrate the theoretical achievements are presented.

Estimation of unreliable packets in subband coding of speech

A robust speech communications system over a digital mobile radio channel is considered. We examine a system using 12 Kbit/s embedded subband coding of speech and rate compatible punctured convolutional (RCPC) codes with generalised Viterbi decoding for combined error detection and correction. System performance is improved by exploiting the frame-toframe redundancy in the subband energy profile used for adaptive bit allocation in the subband coder. Unreliable subband side information about the energy profile is identified through a two-stage process comprising the channel error detector and a source-based subband energy profile error detector. The subband side information deemed unreliable is replaced with the estimates based on frame-to-frame redundancy. This improved error detection capability in the subband side information allows a judicious reassignment of channel error protection bits to the subband main information, leading to an overall improvement in system performance. Using a realistic simulation model of a digital mobile radio communication system, an improvement of up to 4.4 dB in recovered speech segmental SNR is obtained at a channel SNR of 6 dB, compared with a system with only channel error protection.