Perceptually motivated quasi-periodic signal selection for polyphonic music transcription

A multiple fundamental frequency estimator is a key building block in music transcription and indexing operations. However, systems trying to perform this task tend to be very complex [1]. Indeed, music transcription requires an analysis accounting for both physical and psycho-acoustical matters. In this work, we propose a physically-motivated audio signal analysis followed by an auditory-based selection. The audio signal model allows for a better time/frequency resolution tradeoff, while the auditory distance discards the redundant/non-relevant information. No prior information on the musical instrument, musical genre, and/or maximum polyphony are needed. Simulations show that the proposed technique achieves good transcription results for a variety of string and wind instruments. The proposed scheme is also shown to be robust in the presence of noise, percussive sounds and in unbalanced Signal-to-Interference Ratio (SIR) situations.