Abstract
Sounds in our environment like voices, animal calls or musical instruments are easily recognized by human listeners. Understanding the key features underlying this robust sound recognition is an important question in auditory science. Here, we studied the recognition by human listeners of new classes of sounds: acoustic and auditory sketches, sounds that are severely impoverished but still recognizable. Starting from a time-frequency representation, a sketch is obtained by keeping only sparse elements of the original signal, here, by means of a simple peak-picking algorithm. Two time-frequency representations were compared: a biologically grounded one, the auditory spectrogram, which simulates peripheral auditory filtering, and a simple acoustic spectrogram, based on a Fourier transform. Three degrees of sparsity were also investigated. Listeners were asked to recognize the category to which a sketch sound belongs: singing voices, bird calls, musical instruments, and vehicle engine noises. Results showed that, with the exception of voice sounds, very sparse representations of sounds (10 features, or energy peaks, per second) could be recognized above chance. No clear differences could be observed between the acoustic and the auditory sketches. For the voice sounds, however, a completely different pattern of results emerged, with at-chance or even below-chance recognition performances, suggesting that the important features of the voice, whatever they are, were removed by the sketch process. Overall, these perceptual results were well correlated with a model of auditory distances, based on spectro-temporal excitation patterns (STEPs). This study confirms the potential of these new classes of sounds, acoustic and auditory sketches, to study sound recognition.
Highlights
Human listeners can apparently recognize very and with no effort very diverse sound sources in their surrounding environment, the literature focusing on the recognition of natural sounds and on the features used by the listeners to recognize them is relatively scant (e.g. [1,2])
Moerel et al [14] found that the voices and speech regions responded to low-level features, with a bias toward low-frequencies that are characteristic of the human voices. This result is coherent with the theoretical approach proposed by Smith and Lewicki [19], which shows that the auditory code is optimum for natural sounds and especially suggests that the acoustic structure of speech could be adapted to the physiology of the peripheral auditory system
We have studied acoustic and auditory sketches, new classes of sounds based on sparse representations, which are severely impoverished versions of original sounds
Summary
Human listeners can apparently recognize very and with no effort very diverse sound sources in their surrounding environment, the literature focusing on the recognition of natural sounds and on the features used by the listeners to recognize them is relatively scant (e.g. [1,2]). Taking carefully into account some low-level acoustic features, other models have been proposed, involving distributed neural representations in the entire human auditory cortex for both low-level features and abstract category encoding [13,14,15] They showed that a complex spectro-temporal pattern of features represents more accurately the auditory encoding of natural sounds than a purely spectral or temporal approach (see [16] for animal sounds only; [13,17]; see [18] for a computational and psychophysical approach). This result is coherent with the theoretical approach proposed by Smith and Lewicki [19], which shows that the auditory code is optimum for natural sounds and especially suggests that the acoustic structure of speech could be adapted to the physiology of the peripheral auditory system
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