Bayesian Identification of Closely-Spaced Chords from Single-Frame STFT Peaks

Harvey Thornburg and Randal Leistikow $<${harv23,randal} at ccrma$>$ (EE)

Identifying chords and related musical attributes from digital audio has proven a long-standing problem spanning many decades of research. A robust identification may facilitate automatic transcription, semantic indexing, polyphonic source separation and other emerging applications. To this end, we develop a Bayesian inference engine operating on single-frame STFT peaks. Peak likelihoods conditional on pitch component information are evaluated by an MCMC approach accounting for overlapping harmonics as well as undetected/spurious peaks, thus facilitating operation in noisy environments at very low computational cost. Our inference engine evaluates posterior probabilities of musical attributes such as root, chroma (including inversion), octave and tuning, given STFT peak frequency and amplitude observations. The resultant posteriors become highly concentrated around the correct attributes, as demonstrated using 227 ms piano recordings with -14 dB additive white Gaussian noise.