[[Feeling|Sentiment]] and [[emotion]] characteristics are prominent in different [[phonetic]] and [[prosodic]] properties contained in audio features.<ref>{{cite journal |last1=Chung-Hsien Wu |last2=Wei-Bin Liang |title=Emotion Recognition of Affective Speech Based on Multiple Classifiers Using Acoustic-Prosodic Information and Semantic Labels |journal=IEEE Transactions on Affective Computing |date=January 2011 |volume=2 |issue=1 |pages=10–21 |doi=10.1109/T-AFFC.2010.16}}</ref> Some of the most important audio features employed in multimodal sentiment analysis are [[mel-frequency cepstrum| mel-frequency cepstrum (MFCC)]], [[spectral centroid]], [[spectral flux]], [[beat]]{{disambiguation needed|date=June 2018}} histogram, beat sum, strongest beat, pause duration, and [[pitch accent|pitch]].<ref name="s1" /> [[OpenSMILE]]<ref>{{cite book |last1=Eyben |first1=Florian |last2=Wöllmer |first2=Martin |last3=Schuller |first3=Björn |title=OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit - IEEE Conference Publication |journal= |pages=1 |date=2009 |doi=10.1109/ACII.2009.5349350 |url=http://ieeexplore.ieee.org/document/5349350|isbn=978-1-4244-4800-5 |chapter=OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit }}</ref> and [[Praat]] are popular open-source toolkits for extracting such audio features.<ref>{{cite book|last1=Morency |first1=Louis-Philippe |last2=Mihalcea |first2=Rada |last3=Doshi |first3=Payal |title=Towards multimodal sentiment analysis: harvesting opinions from the web |date=14 November 2011 |pages=169–176 |doi=10.1145/2070481.2070509 |url=https://dl.acm.org/citation.cfm?id=2070509 |publisher=ACM|chapter=Towards multimodal sentiment analysis |isbn=9781450306416 }}</ref>
