Audio Signal Processing Using Time-Frequency Approaches: Coding, Classification, Fingerprinting, and Watermarking

Audio signals are information rich nonstationary signals that play an important role in our day-to-day communication, perception of environment, and entertainment. Due to its non-stationary nature, time- or frequency-only approaches are inadequate in analyzing these signals. A joint time-frequency (TF) approach would be a better choice to efficiently process these signals. In this digital era, compression, intelligent indexing for content-based retrieval, classification, and protection of digital audio content are few of the areas that encapsulate a majority of the audio signal processing applications. In this paper, we present a comprehensive array of TF methodologies that successfully address applications in all of the above mentioned areas. A TF-based audio coding scheme with novel psychoacoustics model, music classification, audio classification of environmental sounds, audio fingerprinting, and audio watermarking will be presented to demonstrate the advantages of using time-frequency approaches in analyzing and extracting information from audio signals.</p

A Reliable Multiple Access Scheme Based on Chirp Spread Spectrum and Turbo Codes

Nowadays, smart devices are the indispensable part of everyone's life and they play an important role in the advancement of industries and businesses.These devices are able to communicate with themselves and build the super network of the Internet of Things(IoT). Therefore, the need for the underlying structure of wireless data communications gains momentum. We require a wireless communication to support massive connectivity with ultra-fast data transmission rate and ultra-low latency. This research explores two possible methods of tackling the issues of the current communication systems for getting closer to the realization of the IoT. First, a grant-free scheme for uplink communication is proposed. The idea is to the combine the control signals with data signals by superimposing them on top of each other with minimal degradation of both signals. Moreover, it is well-established that orthogonal multiple access schemes cannot support the massive connectivity. Ergo, the second part of this research investigates a Non-Orthogonal Multiple Access(NOMA) scheme that exploits the powerful notion of turbo codes for separating the signals in a slow fading channel. It has been shown that in spite of the simplicity of the design, it has the potentials to surpass the performance of Sparse Code Multiple Access(SCMA) scheme

Classification and Compression of Multi-Resolution Vectors: A Tree Structured Vector Quantizer Approach

Tree structured classifiers and quantizers have been used withgood success for problems ranging from successive refinement coding of speechand images to classification of texture, faces and radar returns. Althoughthese methods have worked well in practice there are few results on thetheoretical side. We present several existing algorithms for tree structured clustering using multi-resolution data and develop some results on their convergenceand asymptotic performance. We show that greedy growing algorithms will result in asymptoticdistortion going to zero for the case of quantizers and prove terminationin finite time for constraints on the rate. We derive an online algorithmfor the minimization of distortion. We also show that a multiscale LVQalgorithm for the design of a tree structured classifier converges to anequilibrium point of a related ordinary differential equation.Simulation results and description of several applications are used toillustrate the advantages of this approach

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

Journal of Telecommunications and Information Technology, 2001, nr 3

kwartalni

Codebooks of Complex Lines Based on Binary Subspace Chirps

Motivated by problems in machine-type wireless communications, we consider codebooks of complex Grassmannian lines in N = 2m dimensions. Binary Chirp (BC) codebooks of prior art are expanded to codebooks of Binary Subspace Chirps (BSSCs), where there is a binary chirp in a subset of the dimensions, while in the remaining dimensions there is a zero. BSSC codebooks have the same minimum distance as BC codebooks, while the cardinality is asymptotically 2.38 times larger. We discuss how BC codebooks can be understood in terms of a subset of the binary symplectic group Sp(2m, 2) in 2m dimensions; Sp(2m, 2) is isomorphic to a quotient group of the Clifford group acting on the codewords in N dimensions. The Bruhat decomposition of Sp(2m, 2) can be described in terms of binary subspaces in m dimensions, with ranks ranging from r=0 to r=m. We provide a unique parameterization of the decomposition. The BCs arise directly from the full-rank part of the decomposition, while BSSCs are a group code arising from the action of the full group with generic r. The rank of the binary subspace is directly related to the number of zeros (sparsity) in the BSSC. We develop a reconstruction algorithm that finds the correct codeword with O(N log2N) complexity, and present performance results in an additive white Gaussian noise scenario.Peer reviewe