Task 1

Name: Correlation between subjective quality parameters of the singing voice and objective acoustic features

Coordination: ESMAE-IPP/FEUP

Duration: 6 months

Task description

The objective of this task is to identify and characterize the most important stylistic/expressive perceptual parameters in singing, to investigate what objective acoustic features correlate well with those parameters, and to develop efficient algorithms that are able to estimate them reliably and in real-time. This information is of paramount importance for TASK2 since the right features must be known and the right estimation algorithms must be implemented before a meaningful and useful visual representation is given to the associated perceptual parameters. Examples of perceptual parameters are pitch, brightness, warmth, clarity, vibrato, singer's formant, legato, portamento. Examples of possible acoustic features are fundamental frequency, power spectral density, spectral envelope, spectral balance, harmonic irregularity and extension, closing/open coefficient of the glottal pulse.

Most likely, new features will be found that serve better the objectives of this task. A strong possibility for this research is to include models of perception (i.e., psychoacoustic models) so as to selectively capture a representation of the acoustic information that is relevant to the auditory system, as it is acknowledged in the Memorandum of Understanding of an on-going Cost Action (2103) concerning "Advanced Voice Function Assessment" [cost2103]. Other inspiring contributions may arise from the area of auditory scene analysis [Bre90].

This research will be very interactive and experimental in nature and will involve singing students and teachers (ESMAE), digital signal processing engineers and PhD students (FEUP) who have a strong familiarity or research experience in the area. In particular, databases of singing voices will be structured in the context of this task, possibly using the voices of students and teachers at ESMAE.

Expected results

The main expected results of this task are: one report, one journal paper, and software models of estimation techniques of acoustic features.

Human resources: 15.5 person-month.