Extending Temporal Feature Integration for Semantic Audio Analysis

Vrysis L, Tsipas N, Dimoulas C, Papanikolaou G. Extending Temporal Feature Integration for Semantic Audio Analysis. In: AES Convention 142. Audio Engineering Society; 2017. Paper 9808. Available from: https://aes.org/publications/elibrary-page/?id=18682

@inproceedings{Vrysis2017_18682,
  author = {Vrysis, Lazaros and Tsipas, Nikolaos and Dimoulas, Charalampos and Papanikolaou, George},
  title = {{Extending Temporal Feature Integration for Semantic Audio Analysis}},
  booktitle = {AES Convention 142},
  note = {Paper 9808},
  year = {2017},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=18682}
}

TY  - CPAPER
TI  - Extending Temporal Feature Integration for Semantic Audio Analysis
AU  - Vrysis, Lazaros
AU  - Tsipas, Nikolaos
AU  - Dimoulas, Charalampos
AU  - Papanikolaou, George
T2  - AES Convention 142
M1  - Paper 9808
PY  - 2017
DA  - 2017/05/06
UR  - https://aes.org/publications/elibrary-page/?id=18682
PB  - Audio Engineering Society
LA  - en
AB  - Semantic audio analysis has become a fundamental task in contemporary audio applications; consequently, further improvement and optimization of classification algorithms has also become a necessity. During the recent years, standard frame-based audio classification methods have been optimized and modern approaches introduced additional feature engineering steps, attempting to capture the temporal dependency between successive feature observations. This type of processing is known as Temporal Feature Integration. In this paper, the enhancement of statistical feature integration is proposed by extending and extensively evaluating the measures that can be deployed. Under this scope, new functions for capturing the shape of a texture window are introduced and evaluated. The ultimate goal of this work is to highlight the best performing measures for early temporal integration, focusing on simple feature engineering, avoiding complexity, and forming a compact and robust set of meta-features that can improve performance in audio classification tasks.
ER  -