In this paper, we study the peaky nature of wavelet coefficient distributions. The study shows that the wavelet coefficients cannot be effectively modeled by a single distribution. We then propose a new modeling scheme based on a Laplacian mixture model and apply it to the indexing and retrieval of image and video databases. In this work, the parameters of the model are first used to represent texture information in image retrieval. Then we explore its application to video retrieval. Traditionally, visual information is used for video indexing and retrieval. However, in some cases audio information is more helpful for finding clues to the video events. The proposed feature extraction scheme is based on the fundamental property of the wavelet transform. Therefore, it can also be adopted to analyze the audio contents of the video data. The experimental evaluation indicates the high discriminatory power of the proposed feature set. The dimension of the extracted feature vector is low, which is important for the retrieval efficiency of the system in terms of response time. User feedback is used to enhance the retrieval performance by modifying the system parameters according to the users' behavior. A nonlinear approach for defining the similarity between the two images is also explored in this work.

In this paper, we introduce a novel watermark representation for audio watermarking, where we embed linear chirps as watermark signals. Different chirp rates, i.e., slopes on the time–frequency (TF) plane, represent watermark messages such that each slope corresponds to a unique message. These watermark signals, i.e., linear chirps, are embedded and extracted using an existing watermarking algorithm. The extracted chirps are then postprocessed at the receiver using a line detection algorithm based on the Hough–Radon transform (HRT). The HRT is an optimal line-detection algorithm, which detects directional components that satisfy a parametric constraint equation in the image of a TF plane, even at discontinuities corresponding to bit errors. Simulation results show that HRT correctly detects the embedded watermark message after common signal processing operations for bit error rates up to 20%. The new watermark representation and the postprocessing stage based on HRT significantly improve the performance of the watermark detection process and can be combined with existing watermark embedding/extraction algorithms for increased robustness.

Audio is a rich source of information in the digital videos that can provide useful descriptors for indexing the video databases. In this paper, we model the shape of the distribution of wavelet coefficients of embedded audio with a Laplacian mixture. The distributions of wavelet coefficients are very peaky in nature. The shape of these distributions can be modeled with only two components in the Laplacian mixture with low computational complexity. The parameters of this mixture model form a low dimensional feature vector representing global similarity of the audio content of the video clips. An interactive approach involving the feature vector updating scheme is used to adapt the retrieval system to the users' needs. This relevance feedback (RF) increases the retrieval ratio substantially. A comprehensive experimental evaluation using the CNN news database has been performed.

In this study, we propose a new spread spectrum audio watermarking algorithm that embeds linear chirps as watermark messages. Different chirp rates, i.e., slopes on the time-frequency (TF) plane, represent watermark messages such that each slope corresponds to a different message. We extract the watermark message using a line detection algorithm based on the Hough-Radon transform (HRT). The HRT detects the directional elements that satisfy a parametric constraint in the image of a TF plane. The proposed method not only detects the presence of watermark, but also extracts the embedded watermark bits and ensures the message is received correctly. The results show that the HRT detects the embedded watermark message even after common signal processing operations such as MPEG audio coding, resampling, lowpass filtering and amplitude re-scaling.

This paper presents a new audio compressor based on the wavelet packet (WP) decomposition. The major drawback of the present audio compressors is the large computational effort associated with subband decomposition and psychoacoustic modeling. We integrate the psychoacoustic model with the design of the decomposition filterbank which separates the wideband signal into 28 subbands closely approximating the critical bands. The psychoacoustic model exploits noise masking and joint stereo coding to compress the subband signals. We demonstrate that the WP decomposition provides sufficient resolution to extract the time-frequency characteristics of the input signal. The WP based audio compressor provides transparent sound quality at compression rates comparable to the MPEG compressor with less than one third of the computational effort.

We investigate the problem of variable-bit rate coding of wideband stereo audio signals for transmission over asynchronous transfer mode networks. We develop a layered source coding algorithm which first decomposes the stereo signal into subband signals. The subband signals are then analyzed with respect to their relative energy and are assigned to high and low priority data streams. The bit rate at the output of audio coder has to be characterized and the statistics of the bit rate should be provided at the time when the initial connection to the network is established. We derive a probability distribution model for the output data rate expressed in terms of the asymptotic distribution of the DFT coefficients. The derivation of the probability distribution of the output data rate assumes knowledge of the PSD function of the audio signal.

High-resolution direction finding algorithms applied to circular array structures are discussed. Uncalibrated direction finding algorithms yield highly biased direction-of-arrival estimates due to strong mutual coupling effects among array elements. Alternate array response models based on array geometry and electromagnetic characteristics of sensors are derived. The array output generated by these models is used to calibrate direction finding algorithms. This method is applied to MUSIC algorithm and its performance is simulated. This approach exhibits superior estimation performance compared to the standard MUSIC algorithm.>