RED

RED - Robust and Efficient DSP

Currently Available Chapters

Recursive Computation of Signal Vector Norms

written by Katharina Rebbe, Gerhard Schmidt, and Tim Owe Wisch

This chapter is about numerically robust ways for recursive norm computation. In contrast to iterative norm computations, which are numerically very accurate and robust, recursive approaches offer a large reduction in computational complexity. However, after several thousand iterations error accumulation appears. To avoid this a mixed iterative and recursive approach is proposed that is cheap in complexity and robust with respect to error accumulation.

Removal of Signal Trends

written by Christin Bald, Julia Kreisel, and Gerhard Schmidt

This chapter is about the removal of the offset - also referred to as trend - of a signal. Therefore three different methods are introduced and compared against each other: subtracting a priori knowledge, highpass filtering, and a nonlinear, time-variant method. The presented methods are numerically robust and computationally efficient. The performances of the methods are demonstrated by removing the trend of a magnetically measured heart signal.

Chapters in Preparation

Complex magnitude approximations (in progress)	5%
Design of crossover filters (in progress)	25%
Fast convolution without additional delay (in progress)	15%
Levinson-Durbin recursion (in progress)	5%
Prediction-based filter design (in progress)	30%

Authors

List of the RED authors

Pdf Versions of the Entire RED Collection

Link to pdf file	Date	Version	Description
	02.01.2018	0.1	Added links, improvement of the main document structure (one of chapters is "nearly" ready in this version)
	02.08.2017	0.0	Initial version (none of chapters is ready in this version)

We are delighted to announce that Marten Finck has been awarded the GaS prize for his Master's thesis on "Evaluation of Speech Quality Using Machine Learning".

In his work, he investigated possible side effects of magnetic resonance-guided focussed ultrasound treatment (MRgFUS) on speech quality in patients with neurological diseases such as Parkinson's disease and essential tremor. This innovative and non-invasive treatment method is used to reduce tremor, i.e. the shaking of limbs.

To evaluate the effects on speech, pre- and post-operative speech recordings of 56 patients were analysed: Speech quality was assessed in five categories (monotony, clarity, fluency, auditory effort and vocal tremor) through a survey with 21 participants and used to create a convolutional neural network (CNN). This generates an objective assessment of speech quality using mel frequency cepstral coefficients (MFCCs), fundamental frequency and loudness. A mean absolute error (MAE) of 0.81 on a scale of 0 to 10 was achieved, compared to a standard deviation of 0.67 for the survey participants compared to a reference assessment. The evaluation of the survey revealed minor positive and negative changes in speech quality, which were balanced overall and tended to indicate natural speech variations in connection with the patients' underlying illnesses.

Congratulations on this well-deserved honour!

RED - Robust and Efficient DSP

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