The NTSP (Numerical Tours of Signal Processing) collection of Matlab/Scilab experiments guides users through the emerging jungle of advanced signal-, image-, and mesh-processing algorithms.

The Numerical Tours of Signal Processing

A remote measurement laboratory for educational experiments

The Numerical Tours of Signal Processing is an online collection of tutorials to learn advanced computational signal and image processing. These tours allow one to follow a step by step Matlab or Scilab implementation of many important processing algorithms. This implementation is commented and the connexions with the relevant mathematical notions are exposed. These algorithms are applied to various signal, image, movie and 3D mesh datasets. These tours are suitable for practitioners in the field, that can use them to learn about state of the art methods. They are also designed to help undergraduate students to understand recent theoretical or numerical advances in signal and image processing.

https://hal.archives-ouvertes.fr/hal-00519521/document

The Numerical Tours of Signal Processing - Advanced Computational Signal and Image Processing

Paralinguistic properties play a more and more decisive role in recent speech processing systems like automatic speech recognition (ASR) or natural text-to-speech systems (TTS). Besides the linguistic information, the so called paralinguistic properties can help solving ambiguities in man-machine-interaction. Nowadays, such man-machine-interfaces can be found for example in call-centers, in driver assistance systems, or at the personal computer at home. There are many different applications for the recognition of paralinguistic properties, e.g. gender, age, voice quality, emotion, or alcohol consumption. Among these properties, the emotional state of a speaker has a superior position because it strongly affects the acoustic signal produced by the speaker in all kind of conversational speech. Emotion recognition has its applications in various fields e.g. in call centers to detect angry customers, in entertainment electronics, in linguistics, and even in politics to analyse speeches of politicians to train the candidates for election campaigns. Various attempts show quite good results in the case of speaker dependent classification (Lugger & Yang, 2006; McGilloway et al., 2000; Nogueiras et al., 2001). But the hardest task and also the most relevant in practice is the speaker independent emotion recognition. Speaker independent means that the speaker of the classified utterances is not included in the training database of the system. He is unknown for the classifier and the deduced learning rules in the training phase. Up to now, a good speaker independent emotion recognition could only be achieved by using very large feature sets in combination with very complex classifiers (Schuller et al., 2006; Lee & Narayanan, 2005). In (Schuller et al., 2006), an average recognition rate of 86.7% was achieved for seven emotions by using 4000 features and support vector machine as classifier. In this work, our goal is to further improve the classification performance of the speaker independent emotion recognition and make it more applicable for real-time systems. Therefore, we use the same German database consisting of 6 basic emotions: sadness, boredom, neutral, anxiety, happiness, and anger. For lack of relevance disgust is ignored. But in contrast to other approaches, we focus on the extraction of less but more adapted features for emotion recognition. Because for real-time systems the feature extraction is the most time consuming part in the whole process chain, we try to reduce the number of features. At the same time we study multi-stage classifiers to optimally adjust the reduced feature number to the different class discriminations during classification. In comparison to support vector machines or neural networks, the Bayesian classifier we use can be O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg

/pdf/psychological-motivated-multi-stage-emotion-classification-sqzwbd5cqw.pdf

Psychological Motivated Multi-Stage Emotion Classification Exploiting Voice Quality Features

In this paper we improve the speaker independent emotion classification of a well known German database consisting of 6 basic emotions: sadness, boredom, neutral, anxiety, happiness, and anger. We achieve this by adding a large set of voice quality parameters to the standard prosodic features. In addition, our observation that the optimal feature set strongly depends on the emotions to be classified, leads to a 3-stage cascaded classification motivated by the psychological model of emotion dimensions. After activation recognition in the first stage, we classify the potency and evaluation dimension in the second and third stage, respectively. Compared to the 2-stage approach, the average classification rate is improved by 14% to 88.8%.

/pdf/cascaded-emotion-classification-via-psychological-emotion-22nlygw23y.pdf

Cascaded emotion classification via psychological emotion dimensions using a large set of voice quality parameters

Discusses the development of a distributed team-oriented senior level curriculum in digital signal processing (DSP). The authors describe an experiment in collaborative education using distributed teams of students from two universities. They also discuss plans for expanded collaboration with additional schools, and present pedagogical objectives. The two courses considered are 'Introduction to signal processing' and 'Advanced topics in signal processing'. The authors discuss computational and Internet tools and evaluate the project. >

G.C. Orsak

Papers

Collaborative DSP education using the Internet and MATLAB(002) 19951127