The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Recent developments in openSMILE, the munich open-source multimedia feature extractor
Open accessAbstract
We present recent developments in the openSMILE feature extraction toolkit. Version 2.0 now unites feature extraction paradigms from speech, music, and general sound events with basic video features for multi-modal processing. Descriptors from audio and video can be processed jointly in a single framework allowing for time synchronization of parameters, on-line incremental processing as well as off-line and batch processing, and the extraction of statistical functionals (feature summaries), such as moments, peaks, regression parameters, etc. Postprocessing of the features includes statistical classifiers such as support vector machine models or file export for popular toolkits such as Weka or HTK. Available low-level descriptors include popular speech, music and video features including Mel-frequency and similar cepstral and spectral coefficients, Chroma, CENS, auditory model based loudness, voice quality, local binary pattern, color, and optical flow histograms. Besides, voice activity detection, pitch tracking and face detection are supported. openSMILE is implemented in C++, using standard open source libraries for on-line audio and video input. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. openSMILE 2.0 is distributed under a research license and can be downloaded from http://opensmile.sourceforge.net/.
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Opensmile: the munich versatile and fast open-source audio feature extractor
Eyben, F., Wöllmer, M., & Schuller, B. (2010) · Proceedings of the 18th ACM international conference on Multimedia · doi:10.1145/1873951.1874246 · Cited by 2,642
Self-citationOn the Acoustics of Emotion in Audio: What Speech, Music, and Sound have in Common
Weninger, F., Eyben, F., Schuller, B. W., Mortillaro, M., & Scherer, K. R. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00292 · Cited by 278
Self-citationDuan, Z., Mysore, G. J., & Smaragdis, P. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-181 · Cited by 61
The Computational Paralinguistics Challenge [Social Sciences]
Schuller, B. W. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2192211 · Cited by 56
Self-citationThe Computational Paralinguistics Challenge
Schuller, B. W. (2012) · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · Cited by 34
Self-citationSpeaker trait characterization in web videos: Uniting speech, language, and facial features
Weninger, F. J., Wagner, C., Wöllmer, M., Schuller, B. W., & Morency, L. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638338 · Cited by 4
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The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing
Eyben, F., Scherer, K. R., Schuller, B. W., Sundberg, J., Andre, E., Busso, C., Devillers, L. Y., Epps, J., Laukka, P., Narayanan, S. S., et al. (2016) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2015.2457417 · Cited by 1,818
Self-citationAST: Audio Spectrogram Transformer
Gong, Y., Chung, Y.-A., & Glass, J. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-698 · Cited by 1,159
AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge
Valstar, M., Gratch, J., Schuller, B., Ringeval, F., Lalanne, D., Torres Torres, M., Scherer, S., Stratou, G., Cowie, R., & Pantic, M. (2016) · Proceedings of the 6th International Workshop on Audio/Visual Emotion Challenge · doi:10.1145/2988257.2988258 · Cited by 503
Self-citationRingeval, F., Schuller, B., Valstar, M., Cummins, N., Cowie, R., Tavabi, L., Schmitt, M., Alisamir, S., Amiriparian, S., Messner, E.-M., et al. (2019) · Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop · doi:10.1145/3347320.3357688 · Cited by 409
Self-citationMultimodal Speech Emotion Recognition Using Audio and Text
Yoon, S., Byun, S., & Jung, K. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639583 · Cited by 351
Metadata
- Authors
- Eyben, F., Weninger, F., Gross, F., & Schuller, B.
- Venue
- Proceedings of the 21st ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2013-10-21
- Pages
- 835-838
- OpenAlex
- W2090777335
- Cited by
- 1,241
- References
- 10
- FWCI
- 57.85
- Type
- conference paper
- Language
- en
- Open access
- green
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