We present a framework for selecting best audio features for audiovisual analysis and synthesis of dance figures. Dance figures are performed synchronously with the musical rhythm. They can be analyzed through the audio spectra using spectral and rhythmic musical features. In the proposed audio feature evaluation system, dance figures are manually labeled over the video stream. The music segments, which correspond to labeled dance figures, are used to train hidden Markov model (HMM) structures to learn temporal spectrum patterns for the dance figures. The dance figure recognition performances of the HMM models for various spectral feature sets are evaluated. Audio features, which are maximizing dance figure recognition performances, are selected as the best audio features for the analyzed audiovisual dance recordings. In our evaluations, mel-scale cepstral coefficients (MFCC) with their first and second derivatives, spectral centroid, spectral flux and spectral roll-off are used as candidate audio features. Selection of the best audio features can be used towards analysis and synthesis of audio-driven body animation.