Automatic speaker change detection with the Bayesian information criterion using MPEG-7 features and a fusion scheme

This paper addresses unsupervised speaker change detection, a necessary step for several indexing tasks. We assume that there is no prior knowledge either on the number of speakers or their identities. Features included in the MPEG-7 Audio Prototype are investigated such as the AudioWaveformEnvelope and the AudioSpecrtumCentroid. The model selection criterion is the Bayesian Information Criterion (BIC). A multiple pass algorithm is proposed. It uses a dynamic thresholding for scalar features and a fusion scheme so as to refine the segmentation results. It also models every speaker by a multivariate Gaussian probability density function and whenever new information is available, the respective model is updated. The experiments are carried out on a dataset created by concatenating speakers from the TIMIT database, that is referred to as the TIMIT data set. It is and demonstrated that the performance of the proposed multiple pass algorithm is better than that of other approaches

Estimation of Autoregressive Parameters from Noisy Observations Using Iterated Covariance Updates

Estimating the parameters of the autoregressive (AR) random process is a problem that has been well-studied. In many applications, only noisy measurements of AR process are available. The effect of the additive noise is that the system can be modeled as an AR model with colored noise, even when the measurement noise is white, where the correlation matrix depends on the AR parameters. Because of the correlation, it is expedient to compute using multiple stacked observations. Performing a weighted least-squares estimation of the AR parameters using an inverse covariance weighting can provide significantly better parameter estimates, with improvement increasing with the stack depth. The estimation algorithm is essentially a vector RLS adaptive filter, with time-varying covariance matrix. Different ways of estimating the unknown covariance are presented, as well as a method to estimate the variances of the AR and observation noise. The notation is extended to vector autoregressive (VAR) processes. Simulation results demonstrate performance improvements in coefficient error and in spectrum estimation

On the design of two-channel 2-D nonseparable multi-plet perfect reconstruction filter banks

This paper proposes a new design method for a class of two-channel 2D non-separable perfect reconstruction (PR) filter banks (FBs) using the multiplet FBs. 1D multiplet FBs are PR FBs that can be obtained by frequency transformation of a prototype PR FB in the conventional lifting structure so that a better frequency characteristics can be obtained and varied online to process different signals. By employing the 1D to 2D transformation of Phoong et al., new 2D PR multiplet FBs with quincunx, hourglass, and parallelogram spectral support are obtained. These nonseparable multiplet FBs can be cascaded to realize new PR directional FB for image processing and motion analysis. The design procedure is very general and it can be applied to both linear-phase and low-delay 2D FBs. Design examples are given to demonstrate the usefulness of the proposed method.published_or_final_versio

Wideband Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is an essential enabling functionality for cognitive radio networks to detect spectrum holes and opportunistically use the under-utilized frequency bands without causing harmful interference to legacy networks. This paper introduces a novel wideband spectrum sensing technique, called multiband joint detection, which jointly detects the signal energy levels over multiple frequency bands rather than consider one band at a time. The proposed strategy is efficient in improving the dynamic spectrum utilization and reducing interference to the primary users. The spectrum sensing problem is formulated as a class of optimization problems in interference limited cognitive radio networks. By exploiting the hidden convexity in the seemingly non-convex problem formulations, optimal solutions for multiband joint detection are obtained under practical conditions. Simulation results show that the proposed spectrum sensing schemes can considerably improve the system performance. This paper establishes important principles for the design of wideband spectrum sensing algorithms in cognitive radio networks

Radix-2r Arithmetic for Multiplication by a Constant.

International audienceIn this paper, radix-2r arithmetic is explored to minimize the number of additions in the multiplication by a constant. We provide the formal proof that for an N-bit constant, the maximum number of additions using radix-2r is lower than Dimitrov's estimated upper-bound (2.N/log(N)) using double base number system (DBNS). In comparison to canonical signed digit (CSD) and DBNS, the new radix-2r recoding requires an average of 23.12% and 3.07% less additions for 64-bit constant, respectively

New Blind Block Synchronization for Transceivers Using Redundant Precoders

This paper studies the blind block synchronization problem in block transmission systems using linear redundant precoders (LRP). Two commonly used LRP systems, namely, zero padding (ZP) and cyclic prefix (CP) systems, are considered in this paper. In particular, the block synchronization problem in CP systems is a broader version of timing synchronization problem in the popular orthogonal frequency division multiplexing (OFDM) systems. The proposed algorithms exploit the rank deficiency property of the matrix composed of received blocks when the block synchronization is perfect and use a parameter called repetition index which can be chosen as any positive integer. Theoretical results suggest advantages in blind block synchronization performances when using a large repetition index. Furthermore, unlike previously reported algorithms, which require a large amount of received data, the proposed methods, with properly chosen repetition indices, guarantee correct block synchronization in absence of noise using only two received blocks in ZP systems and three in CP systems. Computer simulations are conducted to evaluate the performances of the proposed algorithms and compare them with previously reported algorithms. Simulation results not only verify the capability of the proposed algorithms to work with limited received data but also show significant improvements in the block synchronization error rate performance of the proposed algorithms over previously reported algorithms

Towards a triple mode common operator FFT for Software Radio systems

International audienceA scenario to design a Triple Mode FFT is addressed. Based on a Dual Mode FFT structure, we present a methodology to reach a triple mode FFT operator (TMFFT) able to operate over three different fields: complex number domain C, Galois Fields GF(Ft) and GF(2m). We propose a reconfigurable Triple mode Multiplier that constitutes the core of the Butterflybased FFT. A scalable and flexible unit for the polynomial reduction needed in the GF(2m) multiplication is also proposed. An FPGA implementation of the proposed multiplier is given and the measures show a gain of 18%in terms of performance-to-cost ratio compared to a "Velcro" approach where two self-contained operators are implemented separately

On the theory and design of a class of recombination nonuniform filter banks with low-delay FIR and IIR filters

This paper studies the theory and design of a class of recombination nonuniform FBs (RNFB) with low-delay (LD) FIR and IIR filters. The conditions for suppressing the spurious response and achieving a good frequency characteristic for these LD FIR/IIR RNFBs are developed. The proposed LD FIR RNFBs have a lower system delay than their linear-phase counterparts, at the expense of slight increase in phase distortion of the analysis filters and arithmetic complexity. By model reducing the LD FIR uniform FBs by the modified model reduction method, an IIR RNFB with a similar characteristic can be readily obtained. A design example is given illustrate the effectiveness of the proposed method. © 2006 IEEE.published_or_final_versio

Development of an interface for digital neuromorphic hardware based on an FPGA

Exploring and understanding the functioning of the human brain is one of the greatest challenges for current research. Neuromorphic engineering tries to address this challenge by abstracting biological mechanisms and translating them into technology. Via the abstraction process and experiments with the resulting technical system, an attempt is made to obtain information about the biological counterpart. One subsection of Neuromorphic Engineering (NE) are Spiking Neural Networks (SNN), which describe the structures of the human brain more and more closely than Artificial Neural Networks (ANN). Together with their dedicated hardware, SNNs provide a good platform for developing new algorithms for information processing. In the context of these neuromorphic hardware platforms, this paper aims to develop an interface for a digital hardware platform (SPINN-3 Development Board) to enable the use of industrial or conventional sensors and thus create new approaches for experimental research. The basis for this endeavor is a Field Programmable Gate Array (FPGA), which is placed as a gateway between the sensors and the neuromorphic hardware. Overall, the developed system provides a robust solution for a wide variety of investigations related to neuromorphic hardware and SNNs. Furthermore, the solution also offers suitable possibilities to monitor all processes within the system in order to obtain suitable measurements, which can be examined in search of meaningful results.Comment: Accepted for publication with Proceedings of the Unified Conference of DAMAS, InCoME and TEPEN Conferences (UNIfied 2023), Springer Natur