Abstract

Abstract
This paper introduces new ideas on wideband stationary/non-stationary noise removal for audio signals. Current noise reduction techniques have generally proven to be effective, yet these typically exhibit certain undesirable characteristics. Distortion and/or alteration of the audio characteristics of primary audio sound is a common problem. Also user intervention in identifying the noise profile is sometimes necessary. The proposed technique is centered on the classical Kalman filtering technique for noise removal but uses a novel architecture whereby advanced signal processing techniques are used to identify and preserve the richness of the audio spectrum. The paper also includes conceptual and derivative results on parameter estimation, a description of multi parameter Signal Activity Detector (SAD) and our new found improved results.

Abstract
Audio bandwidth extension has emerged as a key low bit rate coding tool. In continuation with our on going research on audio bandwidth extension, this paper presents new enhancements to Audio Bandwidth Extension Toolkit (ABET). ABET consists of three primary tools Accurate Spectral Replacement (ASR), Fractal Self Similarity Model (FSSM) and Multi-band Temporal Envelope Amplitude Coding (MBTAC) [1],[2],[3]. Additionally we have also introduced a blind bandwidth extension mode into ABET [4]. We discuss several new ideas / improvements to ABET. Specifically enhancements to the blind bandwidth extension architecture which allow it to work with signals with only 3.5-4.0 kHz audio bandwidth are described. We also elaborate on a new tool for efficient coding of time-frequency envelope which cuts the overhead by 0.75-1.0 kbps/channel. We also address a practical issue i.e., the computational complexity and describe a new low decoder complexity mode of ABET.

Abstract
In this paper we address the accurate estimation of the frequency of sinusoids of natural signals such as singing, voice or music. These signals are intrinsicly harmonic and are normally contaminated by noise. Taking the Cramér-Rao Lower Bound for unbiased frequency estimators as a reference, we compare the performance of several DFT-based frequency estimators that are non-iterative and that use the rectangular window or the Hanning window. Tests conditions simulate harmonic interference and two new ArcTan-based frequency estimators are also included in the tests. Conclusions are presented on the relative performance of the different frequency estimators as a function of the SNR. ©2010 IEEE.

Abstract
The accurate estimation of the frequency of sinusoids is a frequent problem in many signal processing problems including the real-time analysis of the singing voice. In this paper we rely on a single DFT magnitude spectrum in order to perform frequency estimation in a non-iterative way. Two new frequency estimation methods are derived that are matched to the time analysis window and that reduce the maximum absolute estimation error to about 0.1% of the bin width of the DFT. The performance of these methods is evaluated including the parabolic method as a reference, and considering the influence of noise. A combined model is proposed that offers higher noise robustness than that of a single model. ©2010 IEEE.

Abstract
A new DSP laboratory course has been included in the Electrical and Computer Engineering curriculum at the Faculdade de Engenharia da Universidade do Porto, in Portugal, since the school year of 1999/2000. This paper addresses the context and motivation underlying this new course, outlines its structure and methodology, highlights the design and goals of all DSP experiments currently proposed for the 13 weeks of the semester, and reports on the receptivity students have expressed to this elective course. The course is based on the TI C31 Starter Kit and tries to combine full use of its resources with a representative diversity of efficient digital signal processing techniques and associated application scenarios. A perspective is also given on current plans to reinforce DSP expertise at the graduate level.