Automatic Feature Extraction from Large Time Series

Mierswa, Ingo

doi:10.1007/3-540-28084-7_71

Cited by 5 publications

(10 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They proposed a framework for extracting domain-dependent "method trees", representing ad hoc features for a time series. They applied their framework to music genre classification and reported an improvement over approaches that use off-the-shelf audio features [50]. The proposed method trees are essentially BOF features constructed from basic audio operators, with the addition of a complexity constraint (method trees are designed to have polynomial complexity).…”

Section: Audio Feature Generationmentioning

confidence: 99%

“…This framework uses analytical features to generally represent audio features. The framework bears some similarities with other feature generation frameworks such as those by Markovitch and Rosenstein [12] and Mierswa [50]. Notably, EDS uses genetic programming as a core generation algorithm to explore the function space.…”

Section: Audio Feature Generationmentioning

confidence: 99%

“…AFs depart from approaches based on heterogeneous data types and operations, such as that of Mierswa [50], who distinguishes between basis transformations, filters, functions, and windowing. In contrast, AFs are designed with these simplicity constraints: (1) only one composition operation is used (functional composition), and (2) AFs encompass the whole processing chain, from the raw signal to the classifier.…”

Section: Analytical Features: a Subset Of All Possible Audio Digitalmentioning

confidence: 99%

See 2 more Smart Citations

Analytical Features: A Knowledge-Based Approach to Audio Feature Generation

Pachet¹,

Roy²

2009

EURASIP Journal on Audio, Speech, and Music Processing

View full text Add to dashboard Cite

We present a feature generation system designed to create audio features for supervised classification tasks. The main contribution to feature generation studies is the notion of analytical features (AFs), a construct designed to support the representation of knowledge about audio signal processing. We describe the most important aspects of AFs, in particular their dimensional type system, on which are based pattern-based random generators, heuristics, and rewriting rules. We show how AFs generalize or improve previous approaches used in feature generation. We report on several projects using AFs for difficult audio classification tasks, demonstrating their advantage over standard audio features. More generally, we propose analytical features as a paradigm to bring raw signals into the world of symbolic computation.

show abstract

Section: Audio Feature Generationmentioning

confidence: 99%

Section: Audio Feature Generationmentioning

confidence: 99%

Section: Analytical Features: a Subset Of All Possible Audio Digitalmentioning

confidence: 99%

See 1 more Smart Citation

Analytical Features: A Knowledge-Based Approach to Audio Feature Generation

Pachet¹,

Roy²

2009

EURASIP Journal on Audio, Speech, and Music Processing

View full text Add to dashboard Cite

show abstract

“…The signal of interest is likewise split into successive 20 ms frames, windowed By LLD analysis a classification by means of dynamic modeling is already feasible. Yet, basing on our past experience [8] and in accordance with the common practice in the field [1] [5], we decided for a further processing step: In a third stage, higher-level functionals f are derived by means of descriptive statistics in order to project the multivariate time-series F on a static feature vector [4] and thereby become less dependent of the spoken phonetic content:…”

Section: Acoustic Featuresmentioning

confidence: 99%

Recognition of interest in human conversational speech

Schuller¹,

Köhler²,

Müller³

et al. 2006

Interspeech 2006

View full text Add to dashboard Cite

“…In this respect we suggest an evolutionary approach to this problem. Genetic Algorithms (GA) have already been shown successful in the field of Music Information Retrieval [7]. In this work we therefore want to transfer this powerful tool.…”

Section: Introductionmentioning

confidence: 99%

Bioanalog Acoustic Emotion Recognition by Genetic Feature Generation Based on Low-Level-Descriptors

Schuller¹,

Arsic²,

Wallhoff³

et al. 2005

EUROCON 2005 - The International Conference on "Computer as a Tool"

View full text Add to dashboard Cite

Affective Computing has grown an important field in today's man-machine-interaction, and the acoustic speech signal is very popular as basis for an automatic classification at the moment. However, recognition performances reported today are mostly not sufficient for a real usage within working systems. Therefore we want to improve on this challenge by evolutionary programming. As a starting point we use prosodic, voice quality and articulatory feature contours. We next propose systematic derivation of functionals by means of descriptive statistics. In order to analyze cross-feature information and feature permutations we use Genetic Algorithms, as a complete coverage of possible alterations is NP-hard. The final attribute set is at the same time optimized by reduction to the most relevant information in order to reduce complexity for the classifier and ensure real-time capability during extraction process. Classification is fulfilled by diverse machine learning methods for utmost discrimination power. We decided for two public databases, namely the Berlin Emotional Speech Database, and the Danish Emotional Speech Corpus for test-runs. These clearly show the high effectiveness of the suggested approach.

show abstract

Automatic Feature Extraction from Large Time Series

Cited by 5 publications

References 3 publications

Analytical Features: A Knowledge-Based Approach to Audio Feature Generation

Analytical Features: A Knowledge-Based Approach to Audio Feature Generation

Recognition of interest in human conversational speech

Bioanalog Acoustic Emotion Recognition by Genetic Feature Generation Based on Low-Level-Descriptors

Contact Info

Product

Resources

About