Opensmile: the munich versatile and fast open-source audio feature extractor
Open accessAbstract
We introduce the openSMILE feature extraction toolkit, which unites feature extraction algorithms from the speech processing and the Music Information Retrieval communities. Audio low-level descriptors such as CHROMA and CENS features, loudness, Mel-frequency cepstral coefficients, perceptual linear predictive cepstral coefficients, linear predictive coefficients, line spectral frequencies, fundamental frequency, and formant frequencies are supported. Delta regression and various statistical functionals can be applied to the low-level descriptors. openSMILE is implemented in C++ with no third-party dependencies for the core functionality. It is fast, runs on Unix and Windows platforms, and has a modular, component based architecture which makes extensions via plug-ins easy. It supports on-line incremental processing for all implemented features as well as off-line and batch processing. Numeric compatibility with future versions is ensured by means of unit tests. openSMILE can be downloaded from http://opensmile.sourceforge.net/.
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In current MER, it is common practice to use OpenSMILE, COVAREP, OpenEAR, and librosa to extract acoustic features that represent the emotional characteristics of the speech modality.
“In current MER, it is common practice to use OpenSMILE [45], COVAREP [46], OpenEAR [47], and librosa [48] to extract the aforementioned acoustic features that represent the emotional characteristics of the speech modality.”
From A Survey of Deep Learning-Based Multimodal Emotion Recognition: Speech, Text, and Face (2023) · cites this paper as [45]
OpenSMILE features extracted with the IS13 ComParE configuration have similar performance to the IS10 utterance-level acoustic features used in prior work.
“OpenSMILE toolkit (Eyben et al., 2010) with the configuration of 'IS13 ComParE' is used to extract frame-level features, which have similar performance with the IS10 utterance-level acoustic features used in (Liang et al., 2020).”
From Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities (2021) · cites this paper as Eyben et al., 2010
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“Acoustic features: OpenSMILE toolkit (Eyben et al., 2010) with the configuration of “IS13 ComParE” is used to extract frame-level features, which have similar performance with the IS10 utterance-level acoustic features used in (Liang et al.”
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- Authors
- Eyben, F., Wöllmer, M., & Schuller, B.
- Affiliations
- Technical University of Munich (DE)
- Venue
- Proceedings of the 18th ACM international conference on Multimedia
- Publisher
- ACM
- Published
- 2010-10-25
- Pages
- 1459-1462
- OpenAlex
- W2085662862
- Cited by
- 2,642
- References
- 14
- Citations by year
- 2026: 202 · 2025: 280 · 2024: 234 · 2023: 236 · 2022: 227 · 2021: 231 · 2020: 211 · 2019: 164 · 2018: 157 · 2017: 122 · 2016: 128 · 2015: 110 · 2014: 129 · 2013: 105 · 2012: 66
- FWCI
- 77.12
- Type
- conference paper
- Language
- en
- Open access
- green
- nbn-resolving.org
- Locations
- ACM International Conference on Multimedia (ACM MM) (publishedVersion)
- OPUS (Augsburg University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- openSMILE, audio feature extraction, chroma features, loudness, Mel-frequency cepstral coefficients, linear prediction coefficients, line spectral frequencies, fundamental frequency, formant frequencies, music information retrieval, speech processing, open source software
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