Harmonic to Noise Ratio Measurement - Selection of Window and Length

Fernandes, Joana; Teixeira, Felipe; Guedes, Vitor; Cândido, Arnaldo; Teixeira, João Paulo

doi:10.1016/j.procs.2018.10.040

Cited by 34 publications

(13 citation statements)

References 6 publications

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“…Apart from the ratio of periodic-to-noise components, another factor that might account for the pure/clear dimension is the degree to which the periodic components of a tone conform to a harmonic series. This conformity is reflected in the concept of harmonicity, which is commonly measured by the harmonics-to-noise ratio (HNR; Fernandes, Teixeira, Guedes, Junior, & Teixeira, 2018; Wayland, Gargash, & Longman, 1995). The HNR can be calculated by comparing the aggregate energy of all partials whose frequencies conform to the harmonic series with the aggregate energy for all other spectral components.…”

Section: Discussionmentioning

confidence: 99%

Using auditory imagery tasks to map the cognitive linguistic dimensions of musical instrument timbre qualia.

Reymore¹,

Huron²

2020

Psychomusicology: Music, Mind, and Brain

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Two studies are reported related to musical instrument timbre qualia. In the first study, open-ended interviews were conducted with 23 musicians who were asked to describe their phenomenal experiences of imagined sounds for 20 Western instruments. A content analysis of the transcribed interviews suggests 77 qualitative categories underlying the musicians’ descriptions. In a second study, 460 musician participants rated subsets of the same 20 imagined instrument sounds according to the 77 categories derived from Study 1. Principal component analyses were applied to the results of Study 2, yielding several models. Researcher interpretations of the components in these models were combined with the results of supplementary polls where musicians rated the descriptive utility of each candidate component, producing a final 20-dimensional timbre qualia model. The model dimensions include rumbling/low, soft/singing, watery/fluid, direct/loud, nasal/reedy, shrill/noisy, percussive, pure/clear, brassy/metallic, raspy/grainy, ringing/long decay, sparkling/brilliant, airy/breathy, resonant/vibrant, hollow, woody, muted/veiled, sustained/even, open, and focused/compact.

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Section: Discussionmentioning

confidence: 99%

Using auditory imagery tasks to map the cognitive linguistic dimensions of musical instrument timbre qualia.

Reymore¹,

Huron²

2020

Psychomusicology: Music, Mind, and Brain

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“…Analysis of the spectrum was done using the calculation formulas of jitter percentage, shimmer percentage, and HNR as outlined below (Boersma, 1993 ; Fernandes et al, 2018 ; Sueur, 2018 ). Nine emotional voices were selected and analyzed by Praat, and characteristic parameter data of seven emotional voices were directly extracted.…”

Section: Methodsmentioning

confidence: 99%

Affective Voice Interaction and Artificial Intelligence: A Research Study on the Acoustic Features of Gender and the Emotional States of the PAD Model

2021

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New types of artificial intelligence products are gradually transferring to voice interaction modes with the demand for intelligent products expanding from communication to recognizing users' emotions and instantaneous feedback. At present, affective acoustic models are constructed through deep learning and abstracted into a mathematical model, making computers learn from data and equipping them with prediction abilities. Although this method can result in accurate predictions, it has a limitation in that it lacks explanatory capability; there is an urgent need for an empirical study of the connection between acoustic features and psychology as the theoretical basis for the adjustment of model parameters. Accordingly, this study focuses on exploring the differences between seven major “acoustic features” and their physical characteristics during voice interaction with the recognition and expression of “gender” and “emotional states of the pleasure-arousal-dominance (PAD) model.” In this study, 31 females and 31 males aged between 21 and 60 were invited using the stratified random sampling method for the audio recording of different emotions. Subsequently, parameter values of acoustic features were extracted using Praat voice software. Finally, parameter values were analyzed using a Two-way ANOVA, mixed-design analysis in SPSS software. Results show that gender and emotional states of the PAD model vary among seven major acoustic features. Moreover, their difference values and rankings also vary. The research conclusions lay a theoretical foundation for AI emotional voice interaction and solve deep learning's current dilemma in emotional recognition and parameter optimization of the emotional synthesis model due to the lack of explanatory power.

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“…The public available cured database described by Fernandes et al (2019) was used in this work. This database contains, among others, the seven parameters used here, extracted by the algorithms developed by Teixeira and Gonçalves (2016) and by Fernandes et al (2018). This cured database was made with the available features extracted from the Saarbrücken Voice Database (SVD) (Barry and Pützer).…”

Section: Methodsmentioning

confidence: 99%

Clustering of Voice Pathologies based on Sustained Voice Parameters

Oliveira

Dájer

Fernandes

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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Signal processing techniques can be used to extract information that contribute to the detection of laryngeal disorders. The goal of this paper is to perform a statistical analysis through the boxplot tool from 832 voice signals of individuals with different laryngeal pathologies from the Saarbrücken Voice Database in order to create relevant groups, making feasible an automatic identification of these dysfunctions. Jitter, Shimmer, HNR, NHR and Autocorrelation features were compared between several groups of voice pathologies/conditions, resulting in three identified clusters.

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Harmonic to Noise Ratio Measurement - Selection of Window and Length

Cited by 34 publications

References 6 publications

Using auditory imagery tasks to map the cognitive linguistic dimensions of musical instrument timbre qualia.

Using auditory imagery tasks to map the cognitive linguistic dimensions of musical instrument timbre qualia.

Affective Voice Interaction and Artificial Intelligence: A Research Study on the Acoustic Features of Gender and the Emotional States of the PAD Model

Clustering of Voice Pathologies based on Sustained Voice Parameters

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