Whistles, Clicks and Hisses – How to replicate Sound

Whistles, Clicks and Hisses – How to replicate Sound

Technology News |
By Wisse Hettinga

Any sound can now be perfectly replicated by a combination of whistles, clicks, and hisses, with implications for sound processing across the media landscape

The three-component model of sines, noise and transients has now been refined by researchers at Aalto University Acoustics Lab, using ideas from auditory perception, fuzzy logic, and perfect reconstruction.

Doctoral researcher Leonardo Fierro and professor Vesa Välimäki realized the way that people hear the different components and separate whistles, clicks, and hisses is important. If a click gets spread in time, it starts to ring and sound noisier; by contrast, focusing on very brief sounds might cause some loss of tonality.

This insight from auditory perception was coupled with fuzzy logic: at any moment, part of the sound can belong to each of the three classes of sines, transients or noise, not just one of them. With the goal of perfect reconstruction, Fierro optimized the way sound is decomposed. 

In the enhanced method, sines and transients are two opposite characteristics of sound, and the sound is not allowed to belong to both classes at the same time. However, any of two opposite component types can still occur simultaneously with noise. Thus, the idea of fuzzy logic is present in a restricted way. The noise works as a fuzzy link between the sines and transients, describing all the nuances of the sound that are not captured by simple clicks and whistles. ‘It’s like finding the missing piece of a puzzle to connect those two parts that did not fit together before,’ says Fierro.

‘The new sound decomposition method opens many exciting possibilities in sound processing,’ says professor Välimäki. ‘The slowing down of sound is currently our main interest. It is striking that for example in sports news, the slow-motion videos are always silent. The reason is probably that the sound quality in current slow-down audio tools is not good enough. We have already started developing better time-scale modification methods, which use a deep neural network to help stretch some components.’

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