This paper presents a new method for multiple fundamental frequency (F0) estimation on piano recordings. We propose a framework based on a genetic algorithm in order to analyze the overlapping overtones and search for the most likely F0 combination. The search process is aided by adaptive spectral envelope modeling and dynamic noise level estimation: while the noise is dynamically estimated, the spectral envelope of previously recorded piano samples (internal database) is adapted in order to best match the piano played on the input signals and aid the search process for the most likely combination of F0s. For comparison, several state-of-the-art algorithms were run across various musical pieces played by different pianos and then compared using three different metrics. The proposed algorithm ranked first place on Hybrid Decay/Sustain Score metric, which has better correlation with the human hearing perception and ranked second place on both onset-only and onset–offset metrics. A previous genetic algorithm approach is also included in the comparison to show how the proposed system brings significant improvements on both quality of the results and computing time.

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This paper presents a novel approach to the problem of automatic music transcription using electronic synthesis with genetic algorithms. Although the problem is well known and different techniques have been applied before, evolutionary algorithms have never been considered when addressing this problem. By means of a series of steps, we show that a polyphonic MIDI file-containing instrument’s partiturescan be automatically generated from an audio recording, by extracting and separating simultaneous notes. The results obtained shows the feasibility of the approach. Categories and Subject Descriptors H.5.5 [Sound and Music Computing]: Signal analysis, synthesis, and processing; I.2.m [Artificial Intelligence]: